* •

Understanding injury risks [9]

  * •

Sport performance diagnosis

  * •

Clinical human movement analytics

  * •

Movement abnormalities or identifying changes due to orthopedic or
physiotherapeutic interventions

  * •

Gait analysis

  * •

Patient healing progress

  * •

Ambulatory monitoring methods for applications to neurological disorders

  * •

Measuring worker productivity: (e.g., fruit picker efficiency) [65]

The accelerometer sensors are also used to understand movement patterns in
animals. Following are some common applications of such sensors in the
livestock industry:

  * •

Behavior analysis (e.g. walking, lying, standing etc. in animals) [203]

  * •

Health monitoring (e.g. birthing events, estrus etc. in animals) [222][227]

  * •

Understanding group behavior (e.g. animal group foraging)

Notice that positioning-based and non-positioning based analytics are not
disjoint dimensions. For instance, one can consider combining position data
with first derivative (velocity) and second derivative (acceleration) to
characterize or enhance position based trajectories.

#### Summary: Analytics on position based movement data

The above review suggests that several approaches have been investigated in
the literature for efficiency monitoring, production control, safety and
collaboration. Most of the reviewed studies consider using standard ML or data
mining methods with only a few exceptions: for example, [12, 107] compute KPIs
for evaluating workflow efficiency from operational data without ML
involvement, and [156] presents an analytical method for processing raw
trajectory data. However, the literature supporting the utilization of RTLS
data based on ML is too shallow – there are few studies available and the
applied ML techniques are simplistic. The applied traditional ML models have
limitations to fit the specific characteristics of position-based data such as
the presence of dependencies among measurements induced by the spatial and
temporal dimensions. For example, standard classification models (e.g. random
forests, neural networks, decision trees) cannot characterize the non-uniform
and sequential patterns commonly associated with trajectory data, and
classical k-means algorithm does not consider the spatio-temporal relation
while grouping the trajectories into different clusters. Thus, advanced and
specialized models can be appropriate choice to develop applications utilizing
RTLS data. These include sequence-to-sequence models that require to predict
an output sequence (e.g. complete future trajectories of objects moving in a
dynamic environment), and TCNN for classification of trajectories by
considering similarities among them both in temporal and spatial senses.

Different application possibilities exist in manufacturing using RTLS data.
Taking the knowledge from extensive studies in other domains, the literature
can be extended by utilizing RTLS data in manufacturing by following ways:

  1. i

Anomaly detection: Anomaly (also known as outlier) identification approaches
are an active area of research across domains and involve the usage of data in
various formats including sequence or trajectory data. Anomaly identification
also can be studied either in the context of individual outliers or collective
outliers. Most of the existing approaches solely focus on identification of
simple basic outliers [32]. However, outliers in manufacturing data are likely
to exist in a group when there is a group of objects (e.g., workers, robots,
or other smart objects) that deviates from the anticipated and usual
trajectory in a given time due bottlenecks in the production systems.
Moreover, defining the abnormality of movement behavior and detecting
anomalies from complex and large trajectories is an inefficient approach since
the models can be overloaded with the dramatic increase of trajectory streams
generated by multiple interacting objects. Hence, features reflecting the
spatial, sequential, and behavioral characteristics of the objects can be
identified from the long trajectory streams and used with the ML models as an
efficient alternative (e.g. [138, 267]).

  2. ii

Trajectory prediction: In the context of manufacturing, trajectory prediction
has considered predicting only next position of workers or objects [163].
However, prediction of a complete route or trajectory from a set of past
position sequences could be more useful to plan and monitor the WIP, collision
avoidance, and enhance collaborations among workers and/or smart objects.

  3. iii

Trajectory clustering: Clustering is a widely used method for discovering
interesting or unexpected patterns in trajectory data. Previous studies on
trajectory clustering apply different algorithms that compare and group
trajectories as a whole [133]. However, in reality each trajectory may have a
long and complicated path and moving objects move rarely together for entire
path. Besides, common sub-trajectories is also useful in many manufacturing
applications, especially when there are regions of special interest for
analysis [133, 252]. Hence, approaches that partition each trajectory into
characteristic segments and then group the segments of all trajectories can
help to better discover common patterns from a trajectory data set.

  4. iv

Trajectory representation: Trajectory data is expected to be large since RTLS
record the positions in very short intervals of time and real-time processing
of such data sets is quite challenging. Hence, existing studies consider
dimensionality reduction of trajectories by mapping raw trajectory data onto a
layout (which is know beforehand) based representation (e.g., [12, 107]), or
using grid based indexing of trajectory data (e.g. [75]. Although this mapping
process is easy to implement, it is not feasible to apply if the exact layout
is not available. This problem can be addressed in two ways. Firstly, by
representing and extracting features from trajectories by the use of
generative models where the behavior of the each trajectory has been
approximated by a parametric model [18]. The learned parameters or features
can then be used as succinct representations of the trajectories. Secondly,
mapping trajectories based on the distance to a set of landmarks points chosen
arbitrarily or placed randomly to cover a domain of focus [196]. New distance
measures that easily and interpretably map objects can be computed based on
how they interact with the set of landmarks. These distance measures
subsequently can be used effortlessly with well established ML models for
trajectory classification, anomaly detection, clustering, etc. Alternative
methods can also be explored for trajectory representation that include
semantic mapping based on classical logic and knowledge representation
techniques.

##  4 Industrial Applications, Commercial Systems and Digital Twins

In the previous section we took a broad view on movement analytics
applications for collision avoidance, finding trajectory patterns, event
detection, workflow evaluation, and health, among others. We viewed these from
a technology angle, in particular the ML approaches underlying most of them.

In this section, we take a different viewpoint from the end-user perspective:
what are the industrial real-world applications that are currently supported
by movement data analytics, and to what benefit (Section 4.1), and what are
the available commercial systems (Section 4.2)? Finally, we investigate on the
opportunity to employ digital twin technology (Section 4.3).

###  4.1 Industrial Applications

Table 4.1 lists industrial applications in maritime industries [262, 82],
transportation [2, 13, 10, 171, 199, 16], autonomous vehicles [263, 228],
health [2, 178, 10], behavior analysis [199, 16], manufacturing [216, 10, 77]
and indoor positioning [216, 2].

Paper |  Category |  Technique |  Applications
---|---|---|---
[216] |  Indoor movement analysis |  GIS |  Manufacturing
[2] |  Survey on indoor positioning |  |  Transportation, manufacturing, logistics, safety, health industry
[262] |  Review of analytics and big data applications |  AIS |  Maritime
|  _Comment:_ The survey covers techniques that are relevant to manufacturing
as well. For example: Behavior analysis, environmental impact, performance,
trading.
[178] |  Health and Safety |  Bayesian neural network |  Wheel chair movement
|  _Comment:_ The technique mentioned can be used for vehicles moving in
manufacturing space
[13] |  Route estimation, collision avoidance |  Parametric optimization |  Transportation
|  _Comment:_ The technique mentioned can be used for automated vehicles,
robots and worker movements in manufacturing space
[10] |  Visual movement analytics |  Visual analytic tool (VAT) |
|  _Comment:_ Transportation, behavior analysis, logistic optimization,
manufacturing
[171] |  Transportation, capacity management |  Transit network flow simulation |  Transportation
|  _Comment:_ The paper covers problems that also exists in manufacturing at
different level
[263] |  Collision avoidance |  Optimization and RRT |  Autonomous flight
|  _Comment:_ Methods can be used in Manufacturing for collision avoidance.
There is a scope to improve the solution of the other methods such as logic
inferences and Bayesian networks or ML for more accurate position estimation.
[82] |  Maritime, collision avoidance |  DDPG and DP |  Transportation
|  _Comment:_ Collision avoidance and path planning with minimum turns is also
a challenge in manufacturing space.
[199] |  Review of route choice methods and impact of behavior |  |  Transportation
|  _Comment:_ Routing is a problem in manufacturing as well
[16] |  Behavioral analysis |  Threshold based labeling algorithm |  Transportation management and demand planning
|  _Comment:_ Tracking worker movements is important in manufacturing as well
[228] |  Collision avoidance of mobile robot |  ML+ random tree + heuristics |  Manufacturing, transportation
[77] |  Manufacturing |  Optimization (static) + situational awareness and rescheduling |  Manufacturing
Big data and movement analytics across various industries. |  |  |

The papers [262, 2, 16] give a good review of how movement and data analytics
are used to assist real-time decision making in the areas of maritime
industries, indoor positioning, behavior analysis, and transportation,
respectively.

Al Nuaimi and Kamel [2] presents a survey of systems and methods used for
tracking people and objects in indoor environments such as factories,
hospitals, nursing homes, and train terminals. The authors have compared
different indoor positioning systems such as “Fixed indoor positioning
systems” (that have a fixed number of Base Stations (BS) installed at fixed
locations within the building); and “Indoor pedestrian positioning”(people
carrying localization sensors). The comparison was done with respect to the
challenges of providing the best indoor position systems. The key factor in
deciding tracking system efficiency depends on how accurately it can track the
people movement and which methods are available to quantify the movement. With
this perspective, the authors have extended the survey on different methods
used for estimating the position of people. The two main methods outlined in
the survey are: “Bayesian filtering” to estimate the steps of the pedestrian
at a certain time when knowing the previous steps of the same pedestrian at
number of times before it and “Kalman filter-based algorithms”: a mathematical
model which is used to accurately estimate the position with the existence of
noise. Their findings suggest that the fixed indoor position system provides a
good accuracy, however there is good scope to enhance the performance of the
“indoor pedestrian positioning” system.

Later Schabus and Scholz [216], also focused on tracking objects and people in
indoor environments. However, rather than deciding on which system is better
as in [2], the authors focused on the objective of finding the best paths from
one point to another and identifying the bottlenecks. For this purpose they
analyzed the movement behavior in an indoor environment using Geographical
Information Services (GISs). The movements behavior is visualized as a network
with paths created from one point to another using routing algorithms to get
shortest paths. These paths are compared with historical paths (actually
visited path by an asset) to gain insight about detailed movement behavior and
deviations from the optimal path. The bottlenecks are identified by summing up
the number of times an edge is visited by an asset.

While the focus of Schabus and Scholz [216] was to identify the deviation from
shortest paths and bottleneck paths with using movement behavior analysis,
Song et al. [228] worked with the objective of finding safe paths in an indoor
environment. They proposed mobile robot path planning to produce the optimal
safe path. A real-time obstacle avoidance decision model based on ML
algorithms is designed to improve the accuracy and speed of real-time obstacle
avoidance prediction for mobile robots in local path planning. The Rapid-
exploration Random Tree algorithm (RRT) algorithm is extended by greedy
algorithm approach to smooth the global path and shorten its total length by
removing the redundant nodes. The method is called Smooth Rapidly exploring
Random Tree (S-RRT) method. The authors have also looked at optimizing the
path planning time together with finding the shortest path. For optimizing the
path planning time and generate a more stable collision-free optimization
path, an improved hybrid genetic algorithm-ant colony optimization algorithm
is proposed. It is based on the idea of hybrid algorithm that combines the
advantages of genetic algorithm and ant colony optimization, and can generate
better paths in global path planning and local path planning.

All three papers mentioned so far used movement analytics to find best and/or
safe paths in indoor environment. However, there are studies demonstrating the
benefits of using the movement analytics with respect to other aspects such as
capturing real time disruptions in manufacturing space. For example, Dobler et
al. [77] studied how big data from interwoven, autonomous and intelligent
supply chains can be integrated and used in optimizing the manufacturing
systems against various real-time disruptions such as machine failure,
resource become unavailable etc. They propose and compare two different
approaches for the optimization of manufacturing lines. The first approach is
based on static optimization of production demand. In the second approach,
real-time situational awareness—implemented as digital avatar—is used to
assign local intelligence to jobs and raw materials. The real-time situational
awareness will inform about events such as disruptions and machine failures
which will be fed into the optimization framework to make better decisions.
The results are generated using event-discrete simulation and are compared to
common (heuristic) job scheduling algorithms.

The application of movement analysis is studied in literature from various
point of view in manufacturing as well as other industries. Another industry
making use of movement analytics is the transport industry where there are
many applications such as identifying consumer behavior that in turn
determines the demand of a specific route; collision free paths; route
congestion; demand of different paths at different times; and estimating
alternate routes [16, 199, 13, 171]. To identify consumer behavior in using
specific routes, correct labeling of events plays an important role. Asakura
and Hato [16] worked on this problem and propose a labeling algorithm for
tracking travelers’ behavior. They used mobile communication systems such as
GPS (global positioning systems), cellular phone and RFID for the same
purpose. Two of the important events to label that helps in identifying the
demand of a specific path/position of route and hence help in identifying the
possible congestion are: move or stay. The authors have proposed a simple
labeling algorithm based on the approach of varying thresholds or discarding
some points from the analysis, for example points that have very short
duration at a place are discarded while labeling stay. The threshold variation
approach helped them in predicting the areas of congestion or demand at a time
with more confidence.

Andrienko et al. [10] worked on the similar problem; however rather than just
labeling the data efficiently, they suggest a complete framework for movement
analysis combining interactive visual displays with database operations and
computational methods. Their movement analysis framework has three main
components: 1). Data cleaning and filtering by including additional fields
such as speed or time interval between two movements that can help with
logically filtering the data; 2). Extraction of significant places where an
object stops frequently or for more duration using a SQL query or user defined
criteria; 3). Extraction and examination of trips where a trip may be
application-and goal-dependent using a SQL based query or thresholds. To avoid
the false flagging/categorizing of a place as significant place, for example,
removing occasional places from regular ones, the clustering algorithm
(OPTICS) is used where parameters of a cluster are user configurable.

While Asakura and Hato [16] and Andrienko et al. [10] worked on data handling
and labeling part, Mo et al. [171] studied the use of movement data to predict
the demand of flow through the rail network. They propose a simulation-based
optimization (SBO) framework to simultaneously calibrate origin-destination
(OD) flow, passenger path choices and train capacity for urban rail systems
using automated fare collection and automated vehicle location data to analyze
performance and conduct performance retrospectives of urban rail systems. The
SBO model has the objective of minimizing the square error between model-
derived OD exit flows and the corresponding observations and the difference
between model-derived and observed journey time distribution (JTD) where
observation data is obtained from autoamted fare collection data. The model
calculates the OD exit flow, and JTD using a black-box function that
corresponds to the transit network loading (TNL) model (a forecasting model),
which assigns passengers over a transit network given the (dynamic) OD entry
demand and path choices. TNL can output the model-derived OD exit flows and
JTD for a given set of path choices and train capacity. TNL has no analytical
form therefore SBO is used. The proposed optimization method is tested on
different scenarios representing different degrees of path choice randomness
and crowding sensitivity. Data from the Hong Kong Mass Transit Railway system
is used as a case study for generating synthetic observations used as “ground
truth". The results show that the response surface methods (particularly
constrained optimization using response surfaces) have consistently good
performance under all scenarios.

Later on Arp et al. [13] worked on the problem of determining best alternative
routes in case of congestion or diversions using movement data. They use
parametric optimization and network based real-time forecasting for traffic
flow en route on a network.

Prato [199] present a good review of the state of the art in the analysis of
route choice behavior within a discrete choice modeling framework. This review
focuses on drivers’ route choice behavior in transportation networks, but the
same modeling framework is applicable to the multi-modal context. The review
examines both major challenges in route choice modeling, namely the generation
of a choice set of alternative routes, and the estimation of discrete choice
models. This is difficult as the semantics identifying the different routes
can be a challenge based on the length and topology of area and noise present
in the data. The choice set generation methods are classified as deterministic
shortest path-based methods, stochastic shortest path-based techniques,
constrained enumeration algorithms that rely on the behavioral assumption that
travelers choose routes according to behavioral rules other than the minimum
cost path, and probabilistic approaches that attach a generation probability
to each route.

The maritime industry is another industry also benefited from the use of
movement analytics [262, 82]. Yang et al. [262] focus on the comprehensive
review of the literature regarding Automatic Information Systems (AIS)
applications in maritime industries. They categorized the AIS applications
into seven application fields of data analysis for maritime industries: AIS
data mining, navigation safety, ship behavior analysis, environmental
evaluation, trade analysis, ship and port performance, and Arctic shipping.
The methodologies in the literature are categorized into four categories: data
processing and mining, index measurement, causality analysis, and operational
research. One of the most recent and relevant works in the maritime industry
using movement analytics is by Du et al. [82]. They worked on the problem of
identifying the obstacles and optimize against those obstacles to determine
the collision-free path for coastal ships with minimum turning points. They
present an optimized path planning method based on improved Deep Deterministic
Policy Gradient (DDPG) and Douglas Peucker (DP) algorithms. The DDPG method
works for continuous space and partly depends on the grid environment and grid
partition strategy. The aim of the study is to avoid known obstacles and
shore-based information obstacles (such as ship-wreck area, restricted
navigation area, and military exercise area) by making better predictions and
finding an obstacle-free path with minimum turning points. To make better
predictions, authors have used Long Short Term Memory (LSTM) as the first
layer of DDPG to enable the uses of historical state information to
approximate the current environmental state information. The DDPG is further
improved by using a two stage reward function, mainline reward function and
auxiliary reward function, to overcome the low learning efficiency and
convergence speed of the traditional DDPG method. The mainline reward function
is used to guide the ship to reach the target point and complete the path
planning task. Meanwhile, the auxiliary function gives reasonable punishment
in the process of path planning, so as to avoid obstacles. The problem that
too many turning points may exist in the above-planned path, which may
increase the navigation risk, an improved DP algorithm is proposed to further
optimize the planned path to make the final path more safe and economical. The
proposed DP algorithm helps in removing the excess turning points.

The other important industry which benefited from movement analytics is
health. Nguyen et al. [178], Andrienko et al. [10] study the use of Bayesian
neural networks in developing a hands-free wheelchair control system. The
experimental results show that with the optimized architecture, classification
Bayesian neural networks can detect head commands of wheelchair users
accurately irrespective to their level of injury

#### Summary: Industrial applications

Big data and movement analytics played a crucial role in different industries
to assist real-time data-informed decision making to mitigate the effects of
possible disruptions. The application areas in the papers reviewed in this
section cover path optimization to avoid collisions with minimum turns and
distance travel; layout optimization to increase the efficiency of resources
and throughput of the system; collision avoidance for systems working with
automated vehicles and robots; demand and inventory management through
consumer behavior; and flow management to avoid congestion (especially in the
transportation industry). For tracking data, various techniques are used based
on the application area and suitability. For example, GIS; infrared,
ultrasonic, radio frequency systems; and indoor pedestrian positioning are
used to track data in an indoor environment. In contrast, GPS, cellular phone,
and RFID systems are used for tracking objects in an outdoor environment. Once
the data is obtained, mainly ML, random tree, neural network, Bayesian
probability, and situational awareness (simulation) based techniques are used
to process the data and make accurate predictions about the obstacles and
issues. To act against the informed obstacles and issues, heuristics and
simulation-based optimization techniques are used for real-time decision-
making.

In summary, different industries are benefiting from the use of movement and
data analysis in predicting relevant events and making efficient decisions
based on those predicted events. There are good review papers for the
application of movement and data analysis in other industries [262, 2, 16],
but there is no existing review paper for movement analytic and manufacturing.
Methods ranging from ML, neural networks, Bayesian probability, and logical
inference have been used in the literature for data and movement analytics,
but none of the mentioned papers have leveraged the advantage by hybridizing
different techniques. There is a clear scope in combining the different
techniques used across various industries and leveraging their benefit to
inform decision making in manufacturing.

###  4.2 Commercial Systems

There are a number of products on the market that provide movement analytics
in manufacturing. In this section, we describe some of the commercial off-the-
shelf products available. Details of individual products can be found in Table
2.

Company |  Hardware |  Software |  What can it do?
---|---|---|---
Data Dog [69] |  None |  Software for monitoring IoT devices. |  Monitor performance of a system of IoT devices.
iMonitor [117] |  None (QR codes which can be scanned by tablets) |  Organizes lists of tasks and parts/inventory |  Reduces paperwork by making data collection easy through scanning of parts using QR codes and tablets.
Machine Metrics [165] |  Box that connects to manufacturing machine via Ethernet cable. |  Real-time visualization of equipment, system or worker on factory floor. |  Visualization, bottleneck analysis and optimization of workflow by sending instructions to factory workers via app.
Motion Analysis [173] |  Sensors for human motion tracking. |  Motion capture software. |  Ford used this to conduct an ergonomic analysis of their assembly line.
SmartX HUB [226] |  RFID tags to track parts, inventory and equipment. |  IoT asset tracking software |  Keeps track of parts and inventory, and monitors where and for how long items are being used.
Worximity Technology [257] |  TileConnect, a wifi-enabled smart sensor. |  Collects and sends data to the company for calculating the Overall Equipment Effectiveness (OEE) and displays data in real time. |  Calculates OEE, displays key performance indicators in real-time on dashboards in factory, and tracks downtime to see when and for how long stoppages occur.
Table 2: Summary of commercial off-the-shelf products for movement analysis in
manufacturing.

Existing systems focus on collecting data to improve organization, real-time
monitoring and improving factory performance and output. Tagging and scanning
parts reduces paperwork makes record keeping easier [117]. It also allows
staff to know where inventory is located to prevent losing it [226]. Real-time
data visualization [257, 165, 69] allows companies to keep track of key
performance indicators from dashboards in the factory. Finally, some products
offer services for improving factory performance and output [165], though the
details of what optimization techniques they use are not clear from their web
sites. The service provided by Worximity Technology [257] includes a
consultation with a Six Sigma expert who calculates the Overall Equipment
Effectiveness (OEE) and will provide suggestions on how to improve it.

An interesting project which took a different approach was that of Motion
Analysis [173], who used cameras and sensors on workers to assess the
ergonomics of their work stations. Rather than tracking the movement of parts
through a factory, this approach was about understanding the body positions of
humans to avoid repetitive strain injuries.

The off-the-shelf products put an emphasis on ease of use and incorporation
into the factory. This could indicate that when creating a product for
industry, a focus needs to be put on ease of implementation, if the technology
is to be taken up by industry.

#### Summary: Commercial Systems

In summary, there exist some products on the market to do movement analytics
in manufacturing, and these products focus on improving organization,
monitoring and factory performance. As these products are sold by private
companies, the underlying analysis methods used in these systems are not
clear. This is understandable, as companies need to protect their intellectual
property, but it leaves a gap in the market for more transparent and
explainable approaches.

###  4.3 Digital Twins

A Digital Twin (DT) is a virtual representation of a physical system and its
associated environment and processes that is updated through the exchange of
information between the physical and virtual system. The concept of DTs was
initially introduced in 2003 by Michael Grieves, in an industry presentation
concerning product lifecycle management. In his white paper from 2014, Grieves
[101] describes a DT as a three-dimension digital model consisting of: 1)
Physical model: used for defining, describing, and extracting information
about the physical entities present in the underline physical space; 2)
Virtual model: which should be the exact mirror image of the physical model in
virtual space; and 3) Connection model: ties physical and virtual space
through transferring the historical and real-time information between the two.

In DTs, online IoT sensors are used to collect data from the physical space,
such as the physical entities’ status data and trajectories. The collected
data is then processed and filtered (to handle scalability issues) using AI/ML
and other big data techniques. The processed data is then sent to the virtual
layer of the DT for an update. Then, AI/ML, optimization, and simulation
techniques are employed to identify potential problems and to propose an on-
time solution. Clearly, the identification of the problem and its on-time
solutions depends on the frequency and correctness of the interaction between
the physical and virtual space through IoT sensors, and hence plays a crucial
role in the success of DTs.

DTs have captured the attention of many researchers. The focus of research
includes reviewing the definition and key parameters of DTs, and the evolution
of their structure over time [236, 247, 96, 102, 66]; how to improve DT
implementations for better usability [67, 78, 210]; and application of DTs to
specific industries and objectives [67, 78, 210, 164, 141, 157, 122, 104,
113].

####  4.3.1 Digital Twins Definitions, Structure, and Key Parameters

With the development of industry 4.0 many authors have redefined DTs and
extended their scope [247, 96, 102, 236, 66]. They also reviewed the state-of-
the-art DT research concerning the key components of DTs, the development of
DTs, and the major DT applications in the industry. For example, Glaessgen and
Stargel [96] redefined DT as “the DT consists of a virtual representation of a
production system that can run on different simulation disciplines that are
characterized by the synchronization between the virtual and real system,
thanks to sensed data and connected smart devices, mathematical models and
real-time data elaboration. The topical role within Industry 4.0 manufacturing
systems is to exploit these features to forecast and optimize the behavior of
the production system at each life cycle phase in real-time.” Kritzinger et
al. [130], gave the definition of DTs for manufacturing space, writing that “a
manufacturing DT offers an opportunity to simulate and optimize the production
system, including its logistical aspects, and enables detailed visualization
of the manufacturing process from single components up to the whole assembly.”

In 2019, Tao et al. [236] further contributed in definition and structure of
the DT concept by extending the three-dimensional model for the DT and
proposing that a complete DT should include five models: 1) Physical modeling:
for extracting, defining, and describing the key features of a physical
entity; 2) Virtual modeling: representing a mirror image of the physical
world; 3) Data modeling: data definition, transmission, conversion, and
storage; and 4) Service modeling: for identification, analysis, and upgrade
services 5) Connection model: for maintaining a constant connection between
the physical model, virtual model, data model, and service model. Based on the
review, the author has identified the production, prognostics, and health
management industries as the primary application industries for DTs.

While Tao et al. [236] extended the model for DT, VanDerHorn and Mahadevan
[247] has proposed a generalized definition of DT. They highlighted the key
parameters required, such as: data update frequency; and level of abstraction
of data, for the implementation of DT and how they can impact the performance
of DT. According to them, these key parameters should be chosen based on the
use case. For example, updating the data every minute may not be required.
Similarly, it may not be required to consider all dimensions and details of
the physical world while creating its virtual replica. However, the high level
of data abstraction gives rise to the high accuracy of the physical model but
may be pretty expensive and not practical. The impact of highlighted key
components of the implementation of DT is demonstrated through a case study
where a DT is developed to support the ongoing asset integrity management of a
naval vessel.

Recently many authors have reviewed the concept of DT [130, 57, 243, 153, 200,
17, 139] from different perspectives. For example, Cimino et al. [57] analyzed
the status of DT research and the key technologies needed to apply DTs.
Uhlenkamp et al. [243], reviewed the concept and different areas of
application of DTs to categorize the literature by identifying major
distinguishing characteristics of the different approaches in the different
application areas. Liu et al. [153] conducts a comprehensive and in-depth
review of the literature to analyze DT from the perspective of concepts,
technologies, and industrial applications. Pronost et al. [200] did a
literature review aiming at categorizing the objects defined under the term
"Digital Twins" in the literature. Assad Neto et al. [17] summarizes a variety
of features (such as digital, analytical, and timeliness) proposed by recent
models in the literature to implement DTs.

###  4.4 DT Implementation

From the reviews mentioned above, it is clear that the implementation of DTs
is a complex task and is, therefore, getting the attention of many researchers
[67, 78, 210]. Damjanovic-Behrendt and Behrendt [67] discuss an open-source
approach for implementing a DT demonstrator for Smart Manufacturing.The DT
demonstrator supports the supervision activity of the operator in monitoring
how the manufacturing system responds to production and environmental changes.
As described by the authors, one of the main potentials of using open source
technology in Smart Manufacturing is to enhance inter-operation and reduce the
capital costs of designing and implementing new manufacturing solutions. The
authors described the major implementation requirements of DTs and Smart
Cyber-Physical Systems (CPSs) for intelligent manufacturing systems where CPS
is the integration of a virtual world that interacts with a physical world.
CPS helps manufacturers to accelerate the design and improve inter-operation
across actual life-cycle processes.

Given the importance of CPS, Dolgui et al. [78] reviewed and identified the
gaps in the architecture of CPS. The authors’ main focus was to find a method
for implementing a robust process control DT in a small or medium enterprise
(SME) that ensures the needed functionality while being easy to understand,
maintain and adjust. They proposed a specific implementation to fulfill a set
of defined requirements. The proposed setup is tested on an industrial use
case focusing on controlling two magnetic induction ovens that preheat
aluminum extrusion billets. Their results demonstrate that a process’s DT must
be as specialized and customized as the system controlling it. CBS integrates
the simulation of DT models with real-time sensory and manufacturing data.
Therefore real-time development and maintenance of simulation models play a
crucial role. Ruppert and Abonyi [210] focused on the problem of real-time
development and maintenance of simulation models. The authors proposed a
method that continuously updates the simulation models based on information
provided by RTLS.

####  4.4.1 Digital Twins Application in Manufacturing

The application of DTs in the manufacturing space and its benefits have been
studied extensively. Kritzinger et al. [130] has reviewed the application and
contribution of DTs in the manufacturing space. The authors have highlighted
the following three main disciplines of production systems that can benefit
from DTs to increase competitiveness, productivity, and efficiency.

  * •

Production planning and control [208]: for example, orders planning based on
statistical assumptions; improved decision support through detailed diagnosis;
and automatic planning and execution of orders by the production units.

  * •

Maintenance [134, 231, 164, 157, 122]: for example: identify the impact of
state changes on a production system; identification and evaluation of
anticipatory maintenance measures; evaluation of machine conditions to achieve
better predictions of the machine’s health condition.

  * •

Layout planning [242, 104, 113]: for example: continuous production system
evaluation and planning; identify hidden design flaws and proposing solutions
within time.

Leng et al. [139] also reviewed the literature in the manufacturing space.
They cover the DT review in manufacturing with the perspective of covering the
available definitions, frameworks, major design steps, new blueprint models,
key enabling technologies, design cases, and research directions of DT-based
smart manufacturing system (SMS) design in this survey.

While Kritzinger et al. [130] and Leng et al. [139] present the literature
review of DTs in the manufacturing space, there are studies covering other
aspects of DTs such as model structure, implementation steps, requirements,
and the use of available digital software to reduce cost and time of
implementation of DTs in the manufacturing space. For example, Banica and
Stefan [22] present a DT model for manufacturing based on the 5 dimension
model [236]. Based on the importance and application of DTs, the authors have
described three types of DTs:

  * •

Product Twin – A virtual prototype of a product used before starting its
production line to analyze its behavior and make adjustments if needed. Thus,
product twin decreases the costs of control and validation phases and can be
used to improve the physical product’s functional performance and quality.

  * •

Process Twin – the next level is represented by the model for a virtual
manufacturing process, which allows the company’s management to make the best
decision in terms of manufactured products, and operations to accomplish and
test them. Process Twin could use Product Twin for each component of the
manufacturing line, establishing its opportunity and efficiency.

  * •

System Twin – this is the higher level, representing the virtual model of an
entire system, based on Product Twin for each device and Process Twin to
optimize the manufacturing processes of these components.

The authors used the supply chain model as a case study to show how the
available digital software can be used to implement DT. The three main
elements of the DT model for the supply chain specified by the author are the
real-time transmission of manufacturing updates, tracking and updating
warehouse inventory, and controlling the distribution networks. Vachalek et
al. [244], Zhuang et al. [278] gave a detailed description of requirement,
implementation and role of each step in DT for a production line in
manufacturing space.

####  4.4.2 Objective-Specific Implementation of DT

While in the last section we outlined reviews that focused on area specific
(supply chain, production line) DT requirements and steps [22, 244, 278], here
we summarise those that focused on objective specific (objective such as:
maintenance activities, life cycle management, factory layout) implementation
of DTs [164, 141, 157, 122, 104, 113].

For example, Macchi et al. [164] highlighted the role of DTs in supporting
decision-making in asset lifecycle management. The authors have highlighted
the use cases where a DT can help in asset management. Li et al. [141] also
studied the use of DTs in life-cycle management; however, their work is
focused on the maintenance of aircraft wing health. They proposed a DT model
for the maintenance of aircraft wing health. To capture the various aleatory
(random) and epistemic (lack of knowledge) uncertainty sources in crack growth
prediction in an aircraft wing, the author proposed a probabilistic model
based on the concept of a dynamic Bayesian network (DBN) (see Section 2.2.3)
for diagnosis and prognosis (a forecast of the likely outcome of a situation)
to realize the DT vision. The DBN integrates physics models and uncertainty
sources in crack growth predictions. In diagnosis, the DBN is utilized to
track the evolution of the time-dependent variables and calibrate the time-
independent variables; in prognosis, the DBN is used for probabilistic
prediction of crack growth in the future. The author further enhances the DBN
structure to make it economical in terms of time by avoiding Bayesian updating
with load data. The proposed approach uses filters as the Bayesian inference
algorithm for the DBN that enables the handling of discrete and continuous
variables of various distribution types and non-linear relationships between
nodes. The author has also addressed the challenge of implementing the
particle filter in the DBN where 1) both dynamic and static nodes exist, and
2) a state variable may have parent nodes across two adjacent networks.

Karve et al. [122] also focused on the application of DTs in maintenance using
Bayesian methods for quantifying uncertainty in diagnosis and damage
prognosis. The authors considered the problem to predict, diagnose and
optimize the repair/ maintenance planning, ensuring system safety for fatigue
cracking under uncertainty using DT. The uncertainty can arise from system
properties, operational parameters, loading and environment, noise in sensor
data, and prediction models. The system safety against fatigue cracking is
ensured by designing mission load profiles for the mechanical component such
that the damage growth in the component is minimized while the component
performs the desired work.

The use of DTs with the objective of optimizing and analyzing layout design is
studied by Guo et al. [104]. They propose a layout design scheme and modular-
based DT model for a factory design. The modular approach brings the
flexibility to accommodate changes that may happen in each design stage of DT,
hence saving workload and time for developing a new DT. The authors have
explained the three main design stages as 1) Conceptual design: the first
stage focused on designing the new factory’s concept, including plant layout,
capital investment, and throughput prediction; 2) Elaborate design: extension
of conceptual design by including machine configuration, process design,
production line or production unit configuration, material handling system
configuration, and work shift configuration; 3) Finalized design: the final
stage where the machine and logistics unit control strategy will be designed
and the whole manufacturing system needs to be integrated. Because the virtual
factory corresponds to the finalized design and is the most similar to the
future physical factory, the DT has the most fidelity to the physical world in
this stage.

To demonstrate the proposed DT design, Guo et al. [104] used a paper cup
factory in China as a case study. The company needs to expand its production
and factory area due to the increasing demand, with its main products as
single-layer, double-layer, and corrugated cups. The authors have demonstrated
that by using the proposed DT model, not only are the flaws in factory design
identified timely, the solutions to overcome the flaws can also be identified.
Moreover, the author shows that the modular approach duration for building a
DT model is significantly reduced, improving the feasibility of applying DTs
to changeable factory design.

Hauge et al. [113] studied the application of DT to support decision-making
processes in two different areas: workstation design and logistics operation
analysis. As explained by the authors, the main task of logistics is to handle
and provide material at the right time, quantity, quality, and place.
Logistics operations often comprise of manual effort and may not be fully
automated. The authors investigate how DTs can contribute to supporting the
decision-making process of selecting the right components for a specific
company. The authors emphasize that the granularity of the DT model depends on
the intended use. For example, the DT model for the interaction of the AGV and
the picking robot in a picking process is more detailed compared to the DT
model that looks at the material flow from a warehouse to an assembly area.

#### Summary: Digital Twins

In summary, a DT is a simulation-based planning and optimization technology
that makes use of the real-time data transformation between the physical and
its virtual counterpart, enabling the virtual system’s dynamic update, leading
to a reliable simulation, and hence better prediction and decisions can be
made for the physical system. The online data is collected using IoT
platforms, which are then stored and processed using the big data
applications. The DT layer then receives the selected data to identify the
current and future (forecasted) possible ambiguities, if there are any,
between the physical and virtual worlds using AI technologies. This helps
provide recommendations for on-the-fly adjustments and optimize its
functionality towards the desired goal. DTs are widely applied in
manufacturing space for effective maintenance planning, life cycle management,
supply chain, layout and factory design. However, as highlighted in the
literature, a DT is a complex system consisting of many layers, and its
success relies on the use-specific selection of the critical components that
include granularity of data and system entities to avoid an over-complicated
virtual model; the integration of data at the right frequency/interval (not
necessarily continuous); and the correct format and accuracy of physical data
passed to the digital model to ensure that the virtual model is aware of the
correct status of its physical counterpart at any time and the right decisions
can be recommended. The movement analytics can help make the virtual model of
a DT more reliable because of its ability to identify and predict the more
accurate status of different system entities involved in a manufacturing
system. Furthermore, it is mentioned in the literature that various AI/ML and
Bayesian network-based techniques are used to draw the correct inference from
data/information regarding the status of the different entities; however,
there is a lack of research about increasing the efficiency of DTs by
hybridizing these techniques.

##  5 Conclusions

In the introduction, we described _movement analytics_ as the process of
gleaning knowledge from tracking data so that it aids meaningful decision
making. The main part of this review was then devoted to better understand the
state of the art on movement analytics in the context of (indoor)
manufacturing and other relevant areas.

We kept the scope of this review wide. Methods, applications and problems that
qualified as potentially relevant were considered in scope. We included
fundamental methods for spatio-temporal analysis from diverse areas such as
logic-based knowledge representation, ML, constraint processing, and
combinations thereof. We looked into applications of movement analytics across
various industries, and we reviewed what commercial systems are on the market
that provide movement analytics in manufacturing. To our knowledge, this
review is unique with respect to such diversity.

We conclude this review with a summary of each of the sections and propose
ideas for future research.

_Classical Logic and Knowledge Representation (Section 2.1)._ Classical
logics, such as first-order logic, and variations, such as, e.g., description
logics support fundamental needs for movement analysis in terms of
representation of internal structure of objects, time and space. While pure
classical logic-based applications for real-world movement analytics are rare,
spatial and temporal logics can be of value in supporting roles. For example,
temporal logic is an obvious choice for specifying constraints in object
movement planning and monitoring. It would be interesting to see how advanced
generic reasoning schemes like SMT solving and description logics can be
instantiated with (spatial) theories and utilized in a temporalized
environment for movement analytics.

Knowledge representation logics can be built on a closed-world semantics of
first-order logic. This makes reasoning non-monotonic but enables drawing
strong conclusions by way of default reasoning – a most useful concept for
domain modeling in general. Prominent realizations are (answer set) logic
programming and, implicitly, relational databases. With respect to movement
analytics, the logic programming based event calculus, some stream processing
and other symbolic trajectories techniques fall into this category.
Probabilistic variants can be of particular interest as they add capabilities
for modeling probabilistic transition systems. We have the impression that
more research in this direction could be done.

_Probabilistic Transition Systems (Section 2.2)._ Probabilistic state space
models enable dynamic systems, such as those with applications to movement
data to be modeled. They can be formulated for pre-processing, prediction,
classification and anomaly detection tasks. Whilst state space models offer
some advantages relative to neural based sequence models, such as the ability
to represent domain knowledge and uncertainty, unlike neural models, they
cannot represent long term sequential dependencies. An interesting area of
current research is developing hybrid models that parameterize state space
models with neural based sequence models to provide the best of both worlds:
interpretability and uncertainty in conjunction with long term sequential
memory.

_Trajectory pre-processing techniques (Section 2.3)._ Pre-processing is a
fundamental component of trajectory modeling, independent of whether ML,
probabilistic or logic-based methods are employed. Pre-processing methods
often reduce the complexity required to model raw trajectory data downstream.
Common methods include noise reduction of erroneous position estimates,
harmonizing trajectories of non-uniform length and/or sampling interval,
segmentation of long trajectories into shorter, more homogeneous subsections,
and mapping raw trajectories into semantically labeled sequences.

_Neural Networks Based Sequence Models (Section 2.4)._ Neural network based
sequence models have potential value to movement based manufacturing
applications with respect to pre-processing, classification, prediction and
anomaly detection tasks. As mentioned previously, unlike i.i.d. (independent
and identically distributed ) ML or state space models, neural network
sequence models can represent long term spatio-temporal dependencies within
motion data. Current research focuses on how to effectively model long
sequences and how to do so in a computationally efficient manner.

The manner in which domain knowledge and context can be included into ML
models is limited. For instance, there remain many open questions about how
symbolic knowledge can be effectively represented, aligned and fused within ML
models.

_Integration of Logic-Based Methods with Probabilistic/ML Methods (Section
2.5)._ A weakness of today’s neural network architectures is their lack of
capability to incorporate domain knowledge, specifically symbolic knowledge
[224, 168]. Integrating logic (for symbolic knowledge) with ML is a still an
emerging topic [33]. We expect that progress in this area will help advancing
movement analytics by offering ways for a more holistic comprehension of
trajectory data and the participating objects.

Some established paths are available already today. For instance, one could
build heterogeneous architectures comprised of different methods for (sensor)
data acquisition, filtering, aggregation and evaluating in a more global
context. One way of approaching this is by transfer of results in the _data
integration_ area.333 The data integration problem, in general, is to provide
uniform access to multiple heterogeneous information sources. A key element in
data integration is a language capable of describing and querying data sources
over a uniform, mediated schema. Logical languages have been shown to be very
useful for that [140], including logic programming [92, 42] and description
logics [46, 48].

Statistical Relational AI (SRAI) is another relevant research area. It uses
first-order logic for relational structure within and between objects of
discourse and probabilities for uncertainty. SRAI methods support
probabilistic inference and learning. A prominent representative is
probabilistic logic programming (but there are many more) which has
applications to trajectory analysis. A rather recent development is
(probabilistic) logic programming coupled with deep neural networks. There are
already applications to movement analysis, but, generally speaking, the area
seems to offer unexplored potential. This could be a promising direction for
movement analytic applications via integrating generative modeling, state
transitions systems, probabilistic inference, learning and deep models.

Other proposals set out from deep learning architectures and add “logic”
components to it. According to Dash et al. [68], this can be done in a variety
of ways, (a) by transforming data; (b) by transforming the loss function
informed by a domain model; and (c) by transforming the model, e.g., by
modeling logic operators within the network itself. All these methods appear
generic enough to be applicable to movement analytics. However, questions
remain if the expressive power of the supported logics are strong enough for
all purposes (need support for space and time).

As an overall observation, it would be important to understand when
combinations of ML and logic are not working. This is being addressed in areas
where knowing confidence in conclusions is paramount [62], e.g., autonomous
driving, but becomes even more relevant when more (logic) components with
their own set of shortcomings come into play.

_Constraint Optimization (Section 2.6)._ Constraint optimization is a consumer
of curated data from big data and movement analytics to make better decisions
in production scheduling and factory layout problems. The curated data can
provide more realistic value estimations, real-time information, and discovery
of work patterns as an input for optimization methods. Since these methods are
the consumer of such information, standard optimization techniques can be
applied.

A possible direction for future research lies in improving optimization
capabilities for deriving accurate information about the (state of) a
manufacturing process, e.g., by adapting movement analytics techniques from
path planning (see Yang et al. [263], Lutz et al. [161]). Another possible
direction is to explore the question how to combine constraint optimization
techniques to with ML and logic techniques in order to bridge a semantic gap
between ML methods.

_Applications of Movement Data Analytics (Section 3)._ Our review indicates
that movement analytics applications for manufacturing revolve around
efficiency monitoring, production control, safety and collaboration. By and
large, they fall under categories of trajectory anomaly detection, prediction,
clustering and representation (approximation, dimensionality reduction). The
use of generic i.i.d. ML and data mining methods is prevalent within these
applications. However, such models have limitations in fitting trajectory data
sets, given the long term spatio-temporal dependencies present within the
object motion. Consequently, the uptake of sequence based neural
architectures, as outlined in Section 2.4, would be beneficial for movement
based manufacturing applications. Furthermore, it could be beneficial to
include motion properties extracted from trajectories, such as velocity and
acceleration, as the inputs to ML models.

_Industrial Applications (Section 4.1)._ Movement analytics plays an important
role in food, transportation, supply chain and health industries, among
others. Specific applications target (real-time) data-informed decision-making
informed by current system state, e.g., path optimization to avoid collisions
with minimum turns and distance travel; layout optimization to increase
efficient use of resources and system throughput; collision avoidance in
systems with automated vehicles and robots; demand and inventory management
through consumer behavior; and flow management to avoid congestion (especially
in the transportation industry). Several ML/AI, optimization, and Bayesian
network-based methods have been proposed for making inferences from movement
data. We expect an opportunity in combining these and other different
techniques, as summarized above, and put them into actual industrial
(manufacturing) use.

_Commercial Systems (Section 4.2)._ The focus of most commercial products is
to accurately produce indoor tracking data. There are some products on
movement analytics in manufacturing with a focus on improving organization,
monitoring, and improving factory performance. These analytics services are
limited in nature, and it is not clear how underlying analytics are done (most
likely to protect IP). This leaves an opportunity for more transparent and
explainable analytics approaches. By including analysis from other types of
movement based analytics approaches (based on kinetic e.g. accelerator), a
wider range of other decision making is possible from movement data. This is
also a gap in this space that can be researched further.

_Digital Twins (Section 4.3)._ We have found wide applications of DTs in
industries specifically in manufacturing, such as for effective maintenance
planning, life cycle management, supply chain, layout, and factory design. The
success of DTs largely depends on how accurately the virtual model reflects
its physical counterpart and the size of the virtual model. This by itself is
a non-trivial problem that can be addressed by paying attention to basic
implementation, architecture aspects, and sensor methods such as RTLS for
updating simulations.

We speculate that movement analytics can also contribute to addressing this
problem through capabilities to identify an entities state when it can
potentially impact the system’s performance. However, we have not found any
paper explicitly mentioning the use of movement analytics for such purposes.

Furthermore, it is mentioned in the literature that various AI/ML and Bayesian
network-based techniques are used in making the correct inferences from
data/information regarding the status of the different entities; however,
there is a lack of research about increasing the efficiency of DTs by
hybridizing these techniques.

## References

  * A. de Freitas et al. [2021] Nicksson A. de Freitas, Ticiana Coelho da Silva, José Fernandes de Macêdo, Leopoldo Melo Junior, and Matheus Cordeiro.  Using Deep Learning for Trajectory Classification:.  In _Proceedings of the 13th International Conference on Agents and Artificial Intelligence_ , pages 664–671, Online Streaming, — Select a Country —, 2021. SCITEPRESS - Science and Technology Publications.  ISBN 978-989-758-484-8.  10.5220/0010227906640671.  URL https://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0010227906640671.
  * Al Nuaimi and Kamel [2011] Klaithem Al Nuaimi and Hesham Kamel.  A survey of indoor positioning systems and algorithms.  In _2011 International Conference on Innovations in Information Technology_ , pages 185–190, Abu Dhabi, United Arab Emirates, April 2011. IEEE.  ISBN 978-1-4577-0311-9.  10.1109/INNOVATIONS.2011.5893813.  URL http://ieeexplore.ieee.org/document/5893813/.
  * Al-Shedivat et al. [2020] Maruan Al-Shedivat, Avinava Dubey, and Eric Xing.  Contextual Explanation Networks.  _Journal of Machine Learning Research_ , 21:1–44, 2020\.
  * Alahi et al. [2016] Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Silvio Savarese.  Social LSTM: Human Trajectory Prediction in Crowded Spaces.  In _2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_ , pages 961–971, Las Vegas, NV, USA, June 2016\. IEEE.  ISBN 978-1-4673-8851-1.  10.1109/CVPR.2016.110.  URL http://ieeexplore.ieee.org/document/7780479/.
  * Alevizos et al. [2017] Elias Alevizos, Anastasios Skarlatidis, Alexander Artikis, and Georgios Paliouras.  Probabilistic Complex Event Recognition: A Survey.  _ACM Computing Surveys_ , 50(5):71:1–71:31, September 2017.  ISSN 0360-0300.  10.1145/3117809.  URL https://doi.org/10.1145/3117809.
  * Allen [1983] James F. Allen.  Maintaining knowledge about temporal intervals.  _Communications of the ACM_ , 26(11):832–843, November 1983.  ISSN 0001-0782, 1557-7317.  10.1145/182.358434.  URL https://dl.acm.org/doi/10.1145/182.358434.
  * Altiok [2012] Tayfur Altiok.  _Performance Analysis of Manufacturing Systems_.  Springer Science & Business Media, December 2012.  ISBN 978-1-4612-1924-8.  Google-Books-ID: hdfiBwAAQBAJ.
  * Alvares et al. [2007] Luis Otavio Alvares, Vania Bogorny, Bart Kuijpers, Jose Antonio Fernandes de Macedo, Bart Moelans, and Alejandro Vaisman.  A model for enriching trajectories with semantic geographical information.  In _Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems - GIS ’07_ , page 1, Seattle, Washington, 2007. ACM Press.  ISBN 978-1-59593-914-2.  10.1145/1341012.1341041.  URL http://portal.acm.org/citation.cfm?doid=1341012.1341041.
  * Analytics [2020] Soter Analytics.  Soter Analytics - Reduce back & shoulder injuries by up to 55%, April 2020.  URL https://soteranalytics.com/.
  * Andrienko et al. [2007] Gennady Andrienko, Natalia Andrienko, and Stefan Wrobel.  Visual analytics tools for analysis of movement data.  _ACM SIGKDD Explorations Newsletter_ , 9(2):38–46, December 2007.  ISSN 1931-0145, 1931-0153.  10.1145/1345448.1345455.  URL https://dl.acm.org/doi/10.1145/1345448.1345455.
  * Apt and Bol [1994] Krzysztof R. Apt and Roland N. Bol.  Logic programming and negation: A survey.  _The Journal of Logic Programming_ , 19-20:9–71, May 1994\.  ISSN 0743-1066.  10.1016/0743-1066(94)90024-8.  URL https://www.sciencedirect.com/science/article/pii/0743106694900248.
  * Arkan and Van Landeghem [2013] Ihsan Arkan and Hendrik Van Landeghem.  Evaluating the performance of a discrete manufacturing process using RFID: A case study.  _Robotics and Computer-Integrated Manufacturing_ , 29(6):502–512, December 2013.  ISSN 0736-5845.  10.1016/j.rcim.2013.06.003.  URL https://www.sciencedirect.com/science/article/pii/S0736584513000471.
  * Arp et al. [2020] Laurens Arp, Dyon van Vreumingen, Daniela Gawehns, and Mitra Baratchi.  Dynamic macro scale traffic flow optimisation using crowd-sourced urban movement data.  In _2020 21st IEEE International Conference on Mobile Data Management (MDM)_ , pages 168–177, Versailles, France, June 2020. IEEE.  ISBN 978-1-72814-663-8.  10.1109/MDM48529.2020.00039.  URL https://ieeexplore.ieee.org/document/9162242/.
  * Artikis et al. [2012] Alexander Artikis, Anastasios Skarlatidis, François Portet, and Georgios Paliouras.  Logic-based event recognition.  _The Knowledge Engineering Review_ , 27(4):469–506, December 2012.  ISSN 0269-8889, 1469-8005.  10.1017/S0269888912000264.  URL https://www.cambridge.org/core/product/identifier/S0269888912000264/type/journal_article.
  * Arulkumaran et al. [2017] Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath.  Deep Reinforcement Learning: A Brief Survey.  _IEEE Signal Processing Magazine_ , 34(6):26–38, November 2017.  ISSN 1053-5888.  10.1109/MSP.2017.2743240.  URL http://ieeexplore.ieee.org/document/8103164/.
  * Asakura and Hato [2004] Yasuo Asakura and Eiji Hato.  Tracking survey for individual travel behaviour using mobile communication instruments.  _Transportation Research Part C: Emerging Technologies_ , 12(3-4):273–291, June 2004.  ISSN 0968090X.  10.1016/j.trc.2004.07.010.  URL https://linkinghub.elsevier.com/retrieve/pii/S0968090X04000130.
  * Assad Neto et al. [2021] Anis Assad Neto, Elias Ribeiro da Silva, Fernando Deschamps, and Edson Pinheiro de Lima.  Digital twins in manufacturing: An assessment of key features.  _Procedia CIRP_ , 97:178–183, 2021.  ISSN 22128271.  10.1016/j.procir.2020.05.222.  URL https://linkinghub.elsevier.com/retrieve/pii/S2212827120314438.
  * Atluri et al. [2018] Gowtham Atluri, Anuj Karpatne, and Vipin Kumar.  Spatio-Temporal Data Mining: A Survey of Problems and Methods.  _ACM Computing Surveys_ , 51(4):83:1–83:41, August 2018.  ISSN 0360-0300.  10.1145/3161602.  URL https://doi.org/10.1145/3161602.
  * Baader et al. [2008] Franz Baader, Ian Horrocks, and Ulrike Sattler.  Description Logics.  In _Foundations of Artificial Intelligence_ , volume 3, pages 135–179. Elsevier, 2008.  ISBN 978-0-444-52211-5.  10.1016/S1574-6526(07)03003-9.  URL https://linkinghub.elsevier.com/retrieve/pii/S1574652607030039.
  * Backes et al. [2019] John Backes, Sam Bayless, Byron Cook, Catherine Dodge, Andrew Gacek, Alan J. Hu, Temesghen Kahsai, Bill Kocik, Evgenii Kotelnikov, Jure Kukovec, Sean McLaughlin, Jason Reed, Neha Rungta, John Sizemore, Mark Stalzer, Preethi Srinivasan, Pavle Subotić, Carsten Varming, and Blake Whaley.  Reachability Analysis for AWS-Based Networks.  In Isil Dillig and Serdar Tasiran, editors, _Computer Aided Verification_ , Lecture Notes in Computer Science, pages 231–241, Cham, 2019. Springer International Publishing.  ISBN 978-3-030-25543-5.  10.1007/978-3-030-25543-5_14.
  * Ball et al. [2004] Thomas Ball, Byron Cook, Vladimir Levin, and Sriram K. Rajamani.  SLAM and Static Driver Verifier: Technology Transfer of Formal Methods inside Microsoft.  In Eerke A. Boiten, John Derrick, and Graeme Smith, editors, _Integrated Formal Methods_ , Lecture Notes in Computer Science, pages 1–20, Berlin, Heidelberg, 2004. Springer.  ISBN 978-3-540-24756-2.  10.1007/978-3-540-24756-2_1.
  * Banica and Stefan [2019] Logica Banica and Cristian Stefan.  Stepping into the Industry 4.0: The Digital Twin Approach.  _Annals of Dunarea de Jos University of Galati. Fascicle I. Economics and Applied Informatics_ , 25(3):107–113, December 2019.  ISSN 15840409, 2344441X.  10.35219/eai1584040962.  URL http://eia.feaa.ugal.ro/images/eia/2019_3/Banica_Stefan.pdf.
  * Baptiste et al. [2001] Philippe Baptiste, Claude Le Pape, and Wim Nuijten.  _Constraint-Based Scheduling_ , volume 39 of _International Series in Operations Research & Management Science_.  Springer US, Boston, MA, 2001.  ISBN 978-1-4613-5574-8 978-1-4615-1479-4.  10.1007/978-1-4615-1479-4.  URL http://link.springer.com/10.1007/978-1-4615-1479-4.
  * Baral and Gelfond [1994] Chitta Baral and Michael Gelfond.  Logic programming and knowledge representation.  _The Journal of Logic Programming_ , 19-20:73–148, May 1994\.  ISSN 0743-1066.  10.1016/0743-1066(94)90025-6.  URL https://www.sciencedirect.com/science/article/pii/0743106694900256.
  * Barrett and Tinelli [2018] Clark Barrett and Cesare Tinelli.  Satisfiability Modulo Theories.  In Edmund M. Clarke, Thomas A. Henzinger, Helmut Veith, and Roderick Bloem, editors, _Handbook of Model Checking_ , pages 305–343. Springer International Publishing, Cham, 2018.  ISBN 978-3-319-10575-8.  10.1007/978-3-319-10575-8_11.  URL https://doi.org/10.1007/978-3-319-10575-8_11.
  * Bauer et al. [2011] Andreas Bauer, Martin Leucker, and Christian Schallhart.  Runtime Verification for LTL and TLTL.  _ACM Transactions on Software Engineering and Methodology_ , 20(4):14:1–14:64, September 2011.  ISSN 1049-331X.  10.1145/2000799.2000800.  URL https://doi.org/10.1145/2000799.2000800.
  * Baum, L.E. and Petrie, T. [1966] Baum, L.E. and Petrie, T.  Statistical inference for probabilistic functions of finite-state Markov chains.  _Annals of MAthematical Statistics_ , 37(6):1554–1563, 1966.
  * Baumgartner [2021] Peter Baumgartner.  Combining Event Calculus and Description Logic Reasoning via Logic Programming.  In Boris Konev and Giles Reger, editors, _Frontiers of Combining Systems_ , volume 12941, pages 98–117. Springer International Publishing, Cham, 2021.  ISBN 978-3-030-86204-6 978-3-030-86205-3.  10.1007/978-3-030-86205-3_6.  URL https://link.springer.com/10.1007/978-3-030-86205-3_6.  Series Title: Lecture Notes in Computer Science.
  * Baumgartner and Krumpholz [2021] Peter Baumgartner and Alexander Krumpholz.  Anomaly Detection in a Boxed Beef Supply Chain.  In _ICCMS ’21_ , pages 1–7. Association for Computing Machinery, June 2021.  ISBN 978-1-4503-8979-2.  10.1145/3474963.3474964.
  * Bechhofer [2009] Sean Bechhofer.  OWL: Web Ontology Language.  In Ling Liu and M. Tamer Özsu, editors, _Encyclopedia of Database Systems_ , pages 2008–2009. Springer US, Boston, MA, 2009.  ISBN 978-0-387-39940-9.  10.1007/978-0-387-39940-9_1073.  URL https://doi.org/10.1007/978-0-387-39940-9_1073.
  * Beck et al. [2018] Harald Beck, Minh Dao-Tran, and Thomas Eiter.  LARS: A Logic-based framework for Analytic Reasoning over Streams.  _Artificial Intelligence_ , 261:16–70, August 2018.  ISSN 00043702.  10.1016/j.artint.2018.04.003.  URL https://linkinghub.elsevier.com/retrieve/pii/S0004370218301929.
  * Belhadi et al. [2021] Asma Belhadi, Youcef Djenouri, Gautam Srivastava, Alberto Cano, and Jerry Chun-Wei Lin.  Hybrid Group Anomaly Detection for Sequence Data: Application to Trajectory Data Analytics.  _IEEE Transactions on Intelligent Transportation Systems_ , pages 1–12, 2021.  ISSN 1558-0016.  10.1109/TITS.2021.3114064.
  * Belle [2020] Vaishak Belle.  Symbolic Logic Meets Machine Learning: A Brief Survey in Infinite Domains.  In Jesse Davis and Karim Tabia, editors, _Scalable Uncertainty Management_ , volume 12322, pages 3–16. Springer International Publishing, Cham, 2020.  ISBN 978-3-030-58448-1 978-3-030-58449-8.  10.1007/978-3-030-58449-8_1.  URL http://link.springer.com/10.1007/978-3-030-58449-8_1.  Series Title: Lecture Notes in Computer Science.
  * Bellomarini et al. [2020] Luigi Bellomarini, Eleonora Laurenza, Emanuel Sallinger, and Evgeny Sherkhonov.  Reasoning Under Uncertainty in Knowledge Graphs.  In Víctor Gutiérrez-Basulto, Tomáš Kliegr, Ahmet Soylu, Martin Giese, and Dumitru Roman, editors, _Rules and Reasoning_ , volume 12173, pages 131–139. Springer International Publishing, Cham, 2020.  ISBN 978-3-030-57976-0 978-3-030-57977-7.  URL https://link.springer.com/10.1007/978-3-030-57977-7_9.
  * Beltagy et al. [2020] Iz Beltagy, Matthew E. Peters, and Arman Cohan.  Longformer: The Long-Document Transformer.  _arXiv:2004.05150 [cs]_ , December 2020.  URL http://arxiv.org/abs/2004.05150.  arXiv: 2004.05150.
  * Bian et al. [2019] Jiang Bian, Dayong Tian, Yuanyan Tang, and Dacheng Tao.  Trajectory Data Classification: A Review.  _ACM Transactions on Intelligent Systems and Technology_ , 10(4):33:1–33:34, August 2019.  ISSN 2157-6904.  10.1145/3330138.  URL https://doi.org/10.1145/3330138.
  * Bienvenu and Ortiz [2015] Meghyn Bienvenu and Magdalena Ortiz.  Ontology-Mediated Query Answering with Data-Tractable Description Logics.  In Wolfgang Faber and Adrian Paschke, editors, _Reasoning Web. Web Logic Rules_ , volume 9203, pages 218–307. Springer International Publishing, Cham, 2015.  ISBN 978-3-319-21767-3 978-3-319-21768-0.  URL http://link.springer.com/10.1007/978-3-319-21768-0_9.
  * Blazquez and Vonderohe [2005] Carola A. Blazquez and Alan P. Vonderohe.  Simple Map-Matching Algorithm Applied to Intelligent Winter Maintenance Vehicle Data.  _Transportation Research Record_ , 1935(1):68–76, January 2005.  ISSN 0361-1981.  10.1177/0361198105193500108.  URL https://doi.org/10.1177/0361198105193500108.  Publisher: SAGE Publications Inc.
  * Borgwardt and Thost [2015] Stefan Borgwardt and Veronika Thost.  Temporal query answering in the description logic EL.  In _Proceedings of the 24th International Conference on Artificial Intelligence_ , IJCAI’15, pages 2819–2825, Buenos Aires, Argentina, July 2015. AAAI Press.  ISBN 978-1-57735-738-4.
  * Box, George et al. [2008] Box, George, Jenkins, Gwilym, and Reinsel, Gregory.  _Time Series Analysis: Forecasting and Control_.  Wiley, 4 edition, 2008.
  * Bragaglia et al. [2012] Stefano Bragaglia, Federico Chesani, Paola Mello, Marco Montali, and Paolo Torroni.  Reactive Event Calculus for Monitoring Global Computing Applications.  In David Hutchison, Takeo Kanade, Josef Kittler, Jon M. Kleinberg, Friedemann Mattern, John C. Mitchell, Moni Naor, Oscar Nierstrasz, C. Pandu Rangan, Bernhard Steffen, Madhu Sudan, Demetri Terzopoulos, Doug Tygar, Moshe Y. Vardi, Gerhard Weikum, Alexander Artikis, Robert Craven, Nihan Kesim Çiçekli, Babak Sadighi, and Kostas Stathis, editors, _Logic Programs, Norms and Action_ , volume 7360, pages 123–146. Springer Berlin Heidelberg, Berlin, Heidelberg, 2012.  ISBN 978-3-642-29413-6 978-3-642-29414-3.  10.1007/978-3-642-29414-3_8.  URL http://link.springer.com/10.1007/978-3-642-29414-3_8.  Series Title: Lecture Notes in Computer Science.
  * Bravo and Bertossi [2003] Loreto Bravo and Leopoldo Bertossi.  Logic programs for consistently querying data integration systems.  In _Proceedings of the 18th international joint conference on Artificial intelligence_ , IJCAI’03, pages 10–15, San Francisco, CA, USA, August 2003. Morgan Kaufmann Publishers Inc.
  * Bu [2018] Fan Bu.  A Data Mining Framework for Massive RFID Data Based on Apriori Algorithm.  _Journal of Physics: Conference Series_ , 1087:022020, September 2018.  ISSN 1742-6596.  10.1088/1742-6596/1087/2/022020.  URL https://doi.org/10.1088/1742-6596/1087/2/022020.
  * Bu et al. [2012] Yingyi Bu, Vinayak Borkar, Michael J. Carey, Joshua Rosen, Neoklis Polyzotis, Tyson Condie, Markus Weimer, and Raghu Ramakrishnan.  Scaling Datalog for Machine Learning on Big Data.  _arXiv:1203.0160 [cs]_ , March 2012.  URL http://arxiv.org/abs/1203.0160.  arXiv: 1203.0160.
  * Cai et al. [2017] Haoshu Cai, Yu Guo, Wen-An Yang, and Kun Lu.  Mining frequent trajectory patterns of WIP in Internet of Things-based spatial-temporal database.  _International Journal of Computer Integrated Manufacturing_ , 30(12):1253–1271, December 2017.  ISSN 0951-192X.  10.1080/0951192X.2017.1307522.  URL https://doi.org/10.1080/0951192X.2017.1307522.  Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/0951192X.2017.1307522.
  * Calvanese et al. [2002] Diego Calvanese, Giuseppe De Giacomo, and Maurizio Lenzerini.  Description Logics for Information Integration.  In Antonis C. Kakas and Fariba Sadri, editors, _Computational Logic: Logic Programming and Beyond: Essays in Honour of Robert A. Kowalski Part II_ , Lecture Notes in Computer Science, pages 41–60. Springer, Berlin, Heidelberg, 2002.  ISBN 978-3-540-45632-2.  10.1007/3-540-45632-5_2.  URL https://doi.org/10.1007/3-540-45632-5_2.
  * Camacho et al. [2019] Alberto Camacho, Rodrigo Toro Icarte, Toryn Q. Klassen, Richard Valenzano, and Sheila A. McIlraith.  LTL and Beyond: Formal Languages for Reward Function Specification in Reinforcement Learning.  In _Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence_ , pages 6065–6073, Macao, China, August 2019. International Joint Conferences on Artificial Intelligence Organization.  ISBN 978-0-9992411-4-1.  10.24963/ijcai.2019/840.  URL https://www.ijcai.org/proceedings/2019/840.
  * Caruccio et al. [2014] Loredana Caruccio, Vincenzo Deufemia, and Giuseppe Polese.  Visual data integration based on description logic reasoning.  In _Proceedings of the 18th International Database Engineering & Applications Symposium_, IDEAS ’14, pages 19–28, New York, NY, USA, July 2014. Association for Computing Machinery.  ISBN 978-1-4503-2627-8.  10.1145/2628194.2628215.  URL https://doi.org/10.1145/2628194.2628215.
  * Centobelli et al. [2016] P. Centobelli, R. Cerchione, and T. Murino.  Layout and Material Flow Optimization in Digital Factory.  _International Journal of Simulation Modelling_ , 15(2):223–235, June 2016.  ISSN 17264529.  10.2507/IJSIMM15(2)3.327.  URL http://www.ijsimm.com/Full_Papers/Fulltext2016/text15-2_223-235.pdf.
  * Ceri et al. [1990] Stefano Ceri, Georg Gottlob, and Letizia Tanca.  _Logic Programming and Databases_.  Surveys in Computer Science. Springer Berlin Heidelberg, Berlin, Heidelberg, 1990.  ISBN 978-3-642-83954-2 978-3-642-83952-8.  10.1007/978-3-642-83952-8.  URL http://link.springer.com/10.1007/978-3-642-83952-8.
  * Chekol and Stuckenschmidt [2019] Melisachew Wudage Chekol and Heiner Stuckenschmidt.  Time-Aware Probabilistic Knowledge Graphs.  In Johann Gamper, Sophie Pinchinat, and Guido Sciavicco, editors, _26th International Symposium on Temporal Representation and Reasoning, TIME 2019, October 16-19, 2019, Málaga, Spain_ , volume 147 of _LIPIcs_ , pages 8:1–8:17. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2019.  10.4230/LIPIcs.TIME.2019.8.
  * Chen et al. [2020] Xiang Chen, Yuanchang Liu, Kamalasudhan Achuthan, and Xinyu Zhang.  A ship movement classification based on Automatic Identification System (AIS) data using Convolutional Neural Network.  _Ocean Engineering_ , 218:108182, December 2020.  ISSN 00298018.  10.1016/j.oceaneng.2020.108182.  URL https://linkinghub.elsevier.com/retrieve/pii/S0029801820311124.
  * Chen et al. [2021] Xinyu Chen, Jiajie Xu, Rui Zhou, Wei Chen, Junhua Fang, and Chengfei Liu.  TrajVAE: A Variational AutoEncoder model for trajectory generation.  _Neurocomputing_ , 428:332–339, March 2021.  ISSN 0925-2312.  10.1016/j.neucom.2020.03.120.  URL https://www.sciencedirect.com/science/article/pii/S0925231220312017.
  * Cheng et al. [2020] Yujiao Cheng, Liting Sun, Changliu Liu, and Masayoshi Tomizuka.  Towards Efficient Human-Robot Collaboration With Robust Plan Recognition and Trajectory Prediction.  _IEEE Robotics and Automation Letters_ , 5(2):2602–2609, April 2020.  ISSN 2377-3766.  10.1109/LRA.2020.2972874.
  * Choromanski et al. [2021] Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, David Belanger, Lucy Colwell, and Adrian Weller.  Rethinking Attention with Performers.  _arXiv:2009.14794 [cs, stat]_ , March 2021.  URL http://arxiv.org/abs/2009.14794.  arXiv: 2009.14794.
  * Chung et al. [2014] Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.  _arXiv:1412.3555 [cs]_ , December 2014.  URL http://arxiv.org/abs/1412.3555.  arXiv: 1412.3555.
  * Cimino et al. [2019] Chiara Cimino, Elisa Negri, and Luca Fumagalli.  Review of digital twin applications in manufacturing.  _Computers in Industry_ , 113:103130, December 2019.  ISSN 01663615.  10.1016/j.compind.2019.103130.  URL https://linkinghub.elsevier.com/retrieve/pii/S0166361519304385.
  * Cinar et al. [2017] Yagmur Gizem Cinar, Hamid Mirisaee, Parantapa Goswami, Eric Gaussier, Ali Aït-Bachir, and Vadim Strijov.  Position-Based Content Attention for Time Series Forecasting with Sequence-to-Sequence RNNs.  In Derong Liu, Shengli Xie, Yuanqing Li, Dongbin Zhao, and El-Sayed M. El-Alfy, editors, _Neural Information Processing_ , Lecture Notes in Computer Science, pages 533–544, Cham, 2017. Springer International Publishing.  ISBN 978-3-319-70139-4.  10.1007/978-3-319-70139-4_54.
  * Cohn and Renz [2008] Anthony G. Cohn and Jochen Renz.  Qualitative Spatial Representation and Reasoning.  In _Foundations of Artificial Intelligence_ , volume 3, pages 551–596. Elsevier, 2008.  ISBN 978-0-444-52211-5.  10.1016/S1574-6526(07)03013-1.  URL https://linkinghub.elsevier.com/retrieve/pii/S1574652607030131.
  * Collins and Cockburn [2020] Anne G. E. Collins and Jeffrey Cockburn.  Beyond dichotomies in reinforcement learning.  _Nature Reviews Neuroscience_ , 21(10):576–586, October 2020.  ISSN 1471-0048.  10.1038/s41583-020-0355-6.  URL https://www.nature.com/articles/s41583-020-0355-6.  Number: 10 Publisher: Nature Publishing Group.
  * Cook [2018] Byron Cook.  Formal Reasoning About the Security of Amazon Web Services.  In Hana Chockler and Georg Weissenbacher, editors, _Computer Aided Verification_ , Lecture Notes in Computer Science, pages 38–47, Cham, 2018. Springer International Publishing.  ISBN 978-3-319-96145-3.  10.1007/978-3-319-96145-3_3.
  * Core [2022] TensorFlow Core.  Uncertainty-aware Deep Learning with SNGP, 2022.  URL https://www.tensorflow.org/tutorials/understanding/sngp.
  * Cowling and Johansson [2002] Peter Cowling and Marcus Johansson.  Using real time information for effective dynamic scheduling.  _European Journal of Operational Research_ , 139(2):230–244, June 2002.  ISSN 03772217.  10.1016/S0377-2217(01)00355-1.  URL https://linkinghub.elsevier.com/retrieve/pii/S0377221701003551.
  * [64] CSIRO.  Secure Intelligent IoT for Digital Manufacturing.  URL https://www.csiro.au/en/work-with-us/industries/manufacturing/future-digital-manufacturing-fund/secure-intelligent-iot-for-digital-manufacturing.  Publisher: CSIRO.
  * Dabrowski and Rahman [2019] Joel Dabrowski and Ashfaqur Rahman.  Sensor Data Analytics for Fruit Picker Bag Drop Detection: A Feasibility Study.  Technical Report EP191416, Data61, CSIRO, 2019.  URL https://epublish.csiro.au/v7y18/sensor-data-analytics-for-fruit-.
  * Dahmen and Rossmann [2021] Ulrich Dahmen and Juergen Rossmann.  What is a Digital Twin – A Mediation Approach.  In _2021 IEEE International Conference on Electro Information Technology (EIT)_ , pages 165–172, Mt. Pleasant, MI, USA, May 2021. IEEE.  ISBN 978-1-66541-846-1.  10.1109/EIT51626.2021.9491883.  URL https://ieeexplore.ieee.org/document/9491883/.
  * Damjanovic-Behrendt and Behrendt [2019] Violeta Damjanovic-Behrendt and Wernher Behrendt.  An open source approach to the design and implementation of Digital Twins for Smart Manufacturing.  _International Journal of Computer Integrated Manufacturing_ , 32(4-5):366–384, May 2019.  ISSN 0951-192X, 1362-3052.  10.1080/0951192X.2019.1599436.  URL https://www.tandfonline.com/doi/full/10.1080/0951192X.2019.1599436.
  * Dash et al. [2022] Tirtharaj Dash, Sharad Chitlangia, Aditya Ahuja, and Ashwin Srinivasan.  A review of some techniques for inclusion of domain-knowledge into deep neural networks.  _Scientific Reports 2022 12:1_ , 12(1):1–15, January 2022.  ISSN 2045-2322.  10.1038/s41598-021-04590-0.  URL https://www.nature.com/articles/s41598-021-04590-0.  Publisher: Nature Publishing Group ISBN: 0123456789.
  * Data Dog [2022] Data Dog.  Monitor your IoT devices and backend services in a single unified platform, 2022.  URL https://www.datadoghq.com/dg/monitor/iot/.
  * De Raedt and Kersting [2008] Luc De Raedt and Kristian Kersting.  Probabilistic Inductive Logic Programming.  In Luc De Raedt, Paolo Frasconi, Kristian Kersting, and Stephen Muggleton, editors, _Probabilistic Inductive Logic Programming: Theory and Applications_ , Lecture Notes in Computer Science, pages 1–27. Springer, Berlin, Heidelberg, 2008.  ISBN 978-3-540-78652-8.  10.1007/978-3-540-78652-8_1.  URL https://doi.org/10.1007/978-3-540-78652-8_1.
  * De Raedt et al. [2007] Luc De Raedt, Angelika Kimmig, and Hannu Toivonen.  ProbLog: a probabilistic prolog and its application in link discovery.  In _Proceedings of the 20th international joint conference on Artifical intelligence_ , IJCAI’07, pages 2468–2473, San Francisco, CA, USA, January 2007. Morgan Kaufmann Publishers Inc.
  * de Vries and van Someren [2012] Gerben Klaas Dirk de Vries and Maarten van Someren.  Machine learning for vessel trajectories using compression, alignments and domain knowledge.  _Expert Systems with Applications_ , 39(18):13426–13439, December 2012.  ISSN 0957-4174.  10.1016/j.eswa.2012.05.060.  URL https://www.sciencedirect.com/science/article/pii/S0957417412007762.
  * Dean and Kanazawa [1989] Thomas Dean and Keiji Kanazawa.  A model for reasoning about persistence and causation.  _Computational Intelligence_ , 5(2):142–150, 1989\.  ISSN 1467-8640.  10.1111/j.1467-8640.1989.tb00324.x.
  * Del Moral, Pierre [1996] Del Moral, Pierre.  Non Linear Filtering: Interacting Particle Solution.  _Markov Processes and Related Fields_ , 2(4):555–580, 1996.
  * Ding et al. [2018] Feng Ding, Jian Wang, Jiaqi Ge, and Wenfeng Li.  Anomaly Detection in Large-Scale Trajectories Using Hybrid Grid-Based Hierarchical Clustering.  _International Journal of Robotics and Automation_ , 33(5), 2018.  ISSN 1925-7090.  10.2316/Journal.206.2018.5.206-0061.  URL http://www.actapress.com/PaperInfo.aspx?paperId=45822.
  * Ding et al. [2011] Xu Chu Ding, Stephen L. Smith, Calin Belta, and Daniela Rus.  MDP optimal control under temporal logic constraints.  In _IEEE Conference on Decision and Control and European Control Conference_ , pages 532–538, Orlando, FL, USA, December 2011. IEEE.  ISBN 978-1-61284-801-3 978-1-61284-800-6 978-1-4673-0457-3 978-1-61284-799-3.  10.1109/CDC.2011.6161122.  URL http://ieeexplore.ieee.org/document/6161122/.
  * Dobler et al. [2020] Martin Dobler, Jens Schumacher, Philipp Busel, and Christian Hartmann.  Supporting SMEs in the Lake Constance Region in the Implementation of Cyber-Physical-Systems: Framework and Demonstrator.  In _2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)_ , pages 1–8, Cardiff, United Kingdom, June 2020. IEEE.  ISBN 978-1-72817-037-4.  10.1109/ICE/ITMC49519.2020.9198430.  URL https://ieeexplore.ieee.org/document/9198430/.
  * Dolgui et al. [2021] Alexandre Dolgui, Alain Bernard, David Lemoine, Gregor von Cieminski, and David Romero, editors.  _Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems: IFIP WG 5.7 International Conference, APMS 2021, Nantes, France, September 5–9, 2021, Proceedings, Part IV_ , volume 633 of _IFIP Advances in Information and Communication Technology_.  Springer International Publishing, Cham, 2021.  ISBN 978-3-030-85909-1 978-3-030-85910-7.  10.1007/978-3-030-85910-7.  URL https://link.springer.com/10.1007/978-3-030-85910-7.
  * Domingos and Lowd [2019] Pedro Domingos and Daniel Lowd.  Unifying logical and statistical AI with Markov logic.  _Communications of the ACM_ , 62(7):74–83, June 2019.  ISSN 0001-0782.  10.1145/3241978.  URL https://doi.org/10.1145/3241978.
  * Dries et al. [2015] Anton Dries, Angelika Kimmig, Wannes Meert, Joris Renkens, Guy Van den Broeck, Jonas Vlasselaer, and Luc De Raedt.  ProbLog2: Probabilistic Logic Programming.  In Albert Bifet, Michael May, Bianca Zadrozny, Ricard Gavalda, Dino Pedreschi, Francesco Bonchi, Jaime Cardoso, and Myra Spiliopoulou, editors, _Machine Learning and Knowledge Discovery in Databases_ , Lecture Notes in Computer Science, pages 312–315, Cham, 2015. Springer International Publishing.  ISBN 978-3-319-23461-8.  10.1007/978-3-319-23461-8_37.
  * Du et al. [2020] Tiantian Du, Michela Turrin, Sabine Jansen, Andy van den Dobbelsteen, and Jian Fang.  Gaps and requirements for automatic generation of space layouts with optimised energy performance.  _Automation in Construction_ , 116:103132, August 2020.  ISSN 09265805.  10.1016/j.autcon.2020.103132.  URL https://linkinghub.elsevier.com/retrieve/pii/S0926580519307496.
  * Du et al. [2021] Yiquan Du, Xiuguo Zhang, Zhiying Cao, Shaobo Wang, Jiacheng Liang, Fengge Zhang, and Jiawei Tang.  An Optimized Path Planning Method for Coastal Ships Based on Improved DDPG and DP.  _Journal of Advanced Transportation_ , 2021:1–23, October 2021.  ISSN 2042-3195, 0197-6729.  10.1155/2021/7765130.  URL https://www.hindawi.com/journals/jat/2021/7765130/.
  * Duan and Cao [2020] Kang-Kang Duan and Shuang-Yin Cao.  Emerging RFID technology in structural engineering – A review.  _Structures_ , 28:2404–2414, December 2020.  ISSN 23520124.  10.1016/j.istruc.2020.10.036.  URL https://linkinghub.elsevier.com/retrieve/pii/S2352012420305968.
  * Dzeng et al. [2014] Ren-Jye Dzeng, Chong-Wey Lin, and Fan-Yi Hsiao.  Application of RFID tracking to the optimization of function-space assignment in buildings.  _Automation in Construction_ , 40:68–83, April 2014.  ISSN 09265805.  10.1016/j.autcon.2013.12.011.  URL https://linkinghub.elsevier.com/retrieve/pii/S092658051300232X.
  * Eiter and Kiesel [2020] Thomas Eiter and Rafael Kiesel.  Weighted LARS for Quantitative Stream Reasoning.  In _ECAI 2020 - 24th European Conference on Artificial Intelligence_ , pages 729–736, 2020.
  * Feinberg et al. [2002] Eugene A. Feinberg, Adam Shwartz, and Frederick S. Hillier, editors.  _Handbook of Markov Decision Processes_ , volume 40 of _International Series in Operations Research & Management Science_.  Springer US, Boston, MA, 2002.  ISBN 978-1-4613-5248-8 978-1-4615-0805-2.  10.1007/978-1-4615-0805-2.  URL http://link.springer.com/10.1007/978-1-4615-0805-2.
  * Ferreira et al. [2021] Ricardo Ferreira, Carolina Lopes, Ricardo Gonçalves, Matthias Knorr, Ludwig Krippahl, and João Leite.  Deep Neural Networks for Approximating Stream Reasoning with C-SPARQL.  In _Progress in Artificial Intelligence - 20th EPIA Conference on Artificial Intelligence_ , July 2021.  URL http://arxiv.org/abs/2106.08452.
  * Flossdorf et al. [2021] Jonathan Flossdorf, Anne Meyer, Dmitri Artjuch, Jaques Schneider, and Carsten Jentsch.  Unsupervised Movement Detection in Indoor Positioning Systems.  _arXiv:2109.10757 [cs, stat]_ , August 2021.  URL http://arxiv.org/abs/2109.10757.  arXiv: 2109.10757.
  * Galton [2009] Antony Galton.  Spatial and temporal knowledge representation.  _Earth Science Informatics_ , 2(3):169–187, September 2009.  ISSN 18650473.  10.1007/s12145-009-0027-6.
  * Gao et al. [2017] Qiang Gao, Fan Zhou, Kunpeng Zhang, Goce Trajcevski, Xucheng Luo, and Fengli Zhang.  Identifying Human Mobility via Trajectory Embeddings.  In _Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence_ , pages 1689–1695, Melbourne, Australia, August 2017. International Joint Conferences on Artificial Intelligence Organization.  ISBN 978-0-9992411-0-3.  10.24963/ijcai.2017/234.  URL https://www.ijcai.org/proceedings/2017/234.
  * Ge et al. [2018] Xiaoyu Ge, Jochen Renz, and Hua Hua.  Towards Explainable Inference about Object Motion using Qualitative Reasoning.  In Michael Thielscher, Francesca Toni, and Frank Wolter, editors, _Principles of Knowledge Representation and Reasoning: Proceedings of the Sixteenth International Conference, KR 2018, Tempe, Arizona, 30 October - 2 November 2018_ , pages 641–642. AAAI Press, 2018.  URL https://aaai.org/ocs/index.php/KR/KR18/paper/view/18044.
  * Genesereth [2010] Michael Genesereth.  Data Integration: The Relational Logic Approach.  _Synthesis Lectures on Artificial Intelligence and Machine Learning_ , 4(1):1–97, January 2010.  ISSN 1939-4608.  10.2200/S00226ED1V01Y200911AIM008.  URL https://www.morganclaypool.com/doi/abs/10.2200/S00226ED1V01Y200911AIM008.  Publisher: Morgan & Claypool Publishers.
  * Ghahramani [1998] Zoubin Ghahramani.  Learning dynamic Bayesian networks.  In Jaime G. Carbonell, Jörg Siekmann, G. Goos, J. Hartmanis, J. van Leeuwen, C. Lee Giles, and Marco Gori, editors, _Adaptive Processing of Sequences and Data Structures_ , volume 1387, pages 168–197. Springer Berlin Heidelberg, Berlin, Heidelberg, 1998.  ISBN 978-3-540-64341-8 978-3-540-69752-7.  URL http://link.springer.com/10.1007/BFb0053999.
  * Giatrakos et al. [2020] Nikos Giatrakos, Elias Alevizos, Alexander Artikis, Antonios Deligiannakis, and Minos Garofalakis.  Complex event recognition in the Big Data era: a survey.  _The VLDB Journal_ , 29(1):313–352, January 2020\.  ISSN 1066-8888, 0949-877X.  10.1007/s00778-019-00557-w.  URL http://link.springer.com/10.1007/s00778-019-00557-w.
  * Giuliari et al. [2021] Francesco Giuliari, Irtiza Hasan, Marco Cristani, and Fabio Galasso.  Transformer Networks for Trajectory Forecasting.  In _2020 25th International Conference on Pattern Recognition (ICPR)_ , pages 10335–10342, January 2021.  10.1109/ICPR48806.2021.9412190.  ISSN: 1051-4651.
  * Glaessgen and Stargel [2012] Edward Glaessgen and David Stargel.  The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles.  In _53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference_ , Honolulu, Hawaii, April 2012. American Institute of Aeronautics and Astronautics.  ISBN 978-1-60086-937-2.  10.2514/6.2012-1818.  URL http://arc.aiaa.org/doi/abs/10.2514/6.2012-1818.
  * Gogate and Domingos [2016] Vibhav Gogate and Pedro Domingos.  Probabilistic theorem proving.  _Communications of the ACM_ , 59(7):107–115, June 2016.  ISSN 0001-0782, 1557-7317.  10.1145/2936726.  URL https://dl.acm.org/doi/10.1145/2936726.
  * Goodfellow et al. [2014] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio.  Generative Adversarial Nets.  In _Advances in Neural Information Processing Systems_ , volume 27. Curran Associates, Inc., 2014.  URL https://papers.nips.cc/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html.
  * Goranko and Galton [1999] Valentin Goranko and Antony Galton.  Temporal Logic, November 1999.  URL https://plato.stanford.edu/archives/win2015/entries/logic-temporal/.  Last Modified: 2015-05-20.
  * Greco and Molinaro [2015] Sergio Greco and Cristian Molinaro.  Datalog and Logic Databases.  _Synthesis Lectures on Data Management_ , 7(2):1–169, November 2015.  ISSN 2153-5418, 2153-5426.  10.2200/S00648ED1V01Y201505DTM041.  URL http://www.morganclaypool.com/doi/10.2200/S00648ED1V01Y201505DTM041.
  * Grieves [2014] Michael Grieves.  Digital Twin: Manufacturing Excellence through Virtual Factory Replication, 2014.  URL https://www.3ds.com/fileadmin/PRODUCTS-SERVICES/DELMIA/PDF/Whitepaper/DELMIA-APRISO-Digital-Twin-Whitepaper.pdf.
  * Grieves and Vickers [2017] Michael Grieves and John Vickers.  Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems.  In Franz-Josef Kahlen, Shannon Flumerfelt, and Anabela Alves, editors, _Transdisciplinary Perspectives on Complex Systems_ , pages 85–113. Springer International Publishing, Cham, 2017.  ISBN 978-3-319-38754-3 978-3-319-38756-7.  10.1007/978-3-319-38756-7_4.  URL http://link.springer.com/10.1007/978-3-319-38756-7_4.
  * Gu et al. [2020] Yulong Gu, Yu Guan, and Paolo Missier.  Towards Learning Instantiated Logical Rules from Knowledge Graphs.  _arXiv:2003.06071 [cs]_ , May 2020.  URL http://arxiv.org/abs/2003.06071.  arXiv: 2003.06071.
  * Guo et al. [2019] Jiapeng Guo, Ning Zhao, Lin Sun, and Saipeng Zhang.  Modular based flexible digital twin for factory design.  _Journal of Ambient Intelligence and Humanized Computing_ , 10(3):1189–1200, March 2019.  ISSN 1868-5137, 1868-5145.  10.1007/s12652-018-0953-6.  URL http://link.springer.com/10.1007/s12652-018-0953-6.
  * Gupta et al. [2018] Agrim Gupta, Justin Johnson, Li Fei-Fei, Silvio Savarese, and Alexandre Alahi.  Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks.  In _2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , pages 2255–2264, Salt Lake City, UT, June 2018. IEEE.  ISBN 978-1-5386-6420-9.  10.1109/CVPR.2018.00240.  URL https://ieeexplore.ieee.org/document/8578338/.
  * Gustafsson et al. [2002] F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson, and P.-J. Nordlund.  Particle filters for positioning, navigation, and tracking.  _IEEE Transactions on Signal Processing_ , 50(2):425–437, February 2002.  ISSN 1941-0476.  10.1109/78.978396.
  * Gyulai et al. [2020] Dávid Gyulai, András Pfeiffer, and Júlia Bergmann.  Analysis of asset location data to support decisions in production management and control.  _Procedia CIRP_ , 88:197–202, January 2020.  ISSN 2212-8271.  10.1016/j.procir.2020.05.035.  URL https://www.sciencedirect.com/science/article/pii/S2212827120303504.
  * Güting et al. [2006] Ralf Hartmut Güting, Victor Teixeira De Almeida, and Zhiming Ding.  Modeling and querying moving objects in networks.  _VLDB Journal_ , 15(2):165–190, 2006.  ISSN 10668888.  10.1007/s00778-005-0152-x.  Publisher: Springer New York.
  * Güting et al. [2015] Ralf Hartmut Güting, Fabio Valdés, and Maria Luisa Damiani.  Symbolic trajectories.  _ACM Transactions on Spatial Algorithms and Systems_ , 1(2), July 2015.  ISSN 23740361.  10.1145/2786756.
  * Halpern et al. [2001] Joseph Y. Halpern, Robert Harper, Neil Immerman, Phokion G. Kolaitis, Moshe Y. Vardi, and Victor Vianu.  On the Unusual Effectiveness of Logic in Computer Science.  _Bulletin of Symbolic Logic_ , 7(2):213–236, March 2001.  ISSN 1079-8986, 1943-5894.  10.2307/2687775.  URL https://www.cambridge.org/core/journals/bulletin-of-symbolic-logic/article/abs/on-the-unusual-effectiveness-of-logic-in-computer-science/64C8A4DE3D8E95FF54C970310A1F0A8E.
  * Han et al. [2014] Yixiang Han, Conrad S. Tucker, Timothy W. Simpson, and Erik Davidson.  A Data Mining Trajectory Clustering Methodology for Modeling Indoor Design Space Utilization.  In _ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference_. American Society of Mechanical Engineers Digital Collection, February 2014.  10.1115/DETC2013-12690.  URL https://biomechanical.asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2013/55898/V03BT03A017/253862.
  * Harding et al. [2005] J. A. Harding, M. Shahbaz, Srinivas, and A. Kusiak.  Data Mining in Manufacturing: A Review.  _Journal of Manufacturing Science and Engineering_ , 128(4):969–976, December 2005.  ISSN 1087-1357.  10.1115/1.2194554.  URL https://doi.org/10.1115/1.2194554.
  * Hauge et al. [2020] Jannicke Baalsrud Hauge, Masoud Zafarzadeh, Yongkuk Jeong, Yi Li, Wajid Ali Khilji, and Magnus Wiktorsson.  Employing digital twins within production logistics.  In _2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)_ , pages 1–8, Cardiff, UK, June 2020\. IEEE.  ISBN 978-1-72817-037-4.  10.1109/ICE/ITMC49519.2020.9198540.  URL https://ieeexplore.ieee.org/document/9198540/.
  * Herr et al. [2019] Dominik Herr, S. Grund, and T. Ertl.  BlueCollar: Optimizing Worker Paths on Factory Shop Floors with Visual Analytics.  In _HICSS_ , 2019.  10.24251/HICSS.2019.191.
  * Hu et al. [2020] Weiming Hu, Jun Gao, Bing Li, Ou Wu, Junping Du, and Stephen Maybank.  Anomaly Detection Using Local Kernel Density Estimation and Context-Based Regression.  _IEEE Transactions on Knowledge and Data Engineering_ , 32(2):218–233, February 2020.  ISSN 1041-4347, 1558-2191, 2326-3865.  10.1109/TKDE.2018.2882404.  URL https://ieeexplore.ieee.org/document/8540843/.
  * Hu et al. [2016] Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard Hovy, and Eric Xing.  Harnessing Deep Neural Networks with Logic Rules.  In _Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_ , pages 2410–2420, Berlin, Germany, 2016. Association for Computational Linguistics.  10.18653/v1/P16-1228.  URL http://aclweb.org/anthology/P16-1228.
  * iMonitor [2022] iMonitor.  Smart manufacturing platform, 2022.  URL https://www.imonitor.net/?gclid=EAIaIQobChMI46iV8pnd9QIVlQsrCh2kbQDsEAAYBCAAEgLhu_D_BwE.
  * Ji and Wang [2017] Wei Ji and Lihui Wang.  Big data analytics based fault prediction for shop floor scheduling.  _Journal of Manufacturing Systems_ , 43:187–194, April 2017\.  ISSN 0278-6125.  10.1016/j.jmsy.2017.03.008.  URL https://www.sciencedirect.com/science/article/pii/S0278612517300389.
  * Jing Yuan et al. [2013] Jing Yuan, Yu Zheng, Xing Xie, and Guangzhong Sun.  T-Drive: Enhancing Driving Directions with Taxi Drivers’ Intelligence.  _IEEE Transactions on Knowledge and Data Engineering_ , 25(1):220–232, January 2013.  ISSN 1041-4347.  10.1109/TKDE.2011.200.  URL http://ieeexplore.ieee.org/document/6025355/.
  * Kalman R.E [1960] Kalman R.E.  A New Approach to Linear Filtering and Prediction Problems.  _Journal of Basic Engineering_ , 82:35–45, 1960.
  * Kanduč and Rodič [2015] Tadej Kanduč and Blaž Rodič.  Optimization of a furniture factory layout.  _Croatian Operational Research Review_ , 6(1):121–130, March 2015.  ISSN 18480225, 18489931.  10.17535/crorr.2015.0010.  URL http://hrcak.srce.hr/index.php?show=clanak&id_clanak_jezik=204318&lang=en.
  * Karve et al. [2020] Pranav M. Karve, Yulin Guo, Berkcan Kapusuzoglu, Sankaran Mahadevan, and Mulugeta A. Haile.  Digital twin approach for damage-tolerant mission planning under uncertainty.  _Engineering Fracture Mechanics_ , 225:106766, February 2020\.  ISSN 00137944.  10.1016/j.engfracmech.2019.106766.  URL https://linkinghub.elsevier.com/retrieve/pii/S0013794419306496.
  * Katzouris et al. [2021] Nikos Katzouris, Georgios Paliouras, and Alexander Artikis.  Online Learning Probabilistic Event Calculus Theories in Answer Set Programming.  _Theory and Practice of Logic Programming_ , pages 1–25, August 2021\.  ISSN 1471-0684, 1475-3081.  10.1017/S1471068421000107.  URL https://www.cambridge.org/core/journals/theory-and-practice-of-logic-programming/article/abs/online-learning-probabilistic-event-calculus-theories-in-answer-set-programming/57E24EBFE1CFBBD5CF10CD7EBDD5F848.
  * Kingma and Welling [2019] Diederik P. Kingma and Max Welling.  An Introduction to Variational Autoencoders.  _Foundations and Trends® in Machine Learning_ , 12(4):307–392, 2019.  ISSN 1935-8237, 1935-8245.  10.1561/2200000056.  URL http://arxiv.org/abs/1906.02691.  arXiv: 1906.02691.
  * Kitaev et al. [2020] Nikita Kitaev, Łukasz Kaiser, and Anselm Levskaya.  Reformer: The Efficient Transformer.  _arXiv:2001.04451 [cs, stat]_ , February 2020.  URL http://arxiv.org/abs/2001.04451.  arXiv: 2001.04451.
  * Koller and Friedman [2009] Daphne Koller and Nir Friedman.  _Probabilistic graphical models: principles and techniques_.  Adaptive computation and machine learning. MIT Press, Cambridge, MA, 2009\.  ISBN 978-0-262-01319-2.
  * Kosaraju et al. [2019] Vineet Kosaraju, Amir Sadeghian, Roberto Martín-Martín, Ian Reid, Hamid Rezatofighi, and Silvio Savarese.  Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks.  In _Advances in Neural Information Processing Systems_ , volume 32. Curran Associates, Inc., 2019.  URL https://proceedings.neurips.cc/paper/2019/hash/d09bf41544a3365a46c9077ebb5e35c3-Abstract.html.
  * Kowalski [1979] Robert Kowalski.  Algorithm = logic + control.  _Communications of the ACM_ , 22(7):424–436, July 1979.  ISSN 0001-0782.  10.1145/359131.359136.  URL https://doi.org/10.1145/359131.359136.
  * Kowalski and Sergot [1986] Robert Kowalski and Marek Sergot.  A logic-based calculus of events.  _New Generation Computing_ , 4(1):67–95, March 1986.  ISSN 0288-3635, 1882-7055.  10.1007/BF03037383.  URL http://link.springer.com/10.1007/BF03037383.
  * Kritzinger et al. [2018] Werner Kritzinger, Matthias Karner, Georg Traar, Jan Henjes, and Wilfried Sihn.  Digital Twin in manufacturing: A categorical literature review and classification.  _IFAC-PapersOnLine_ , 51(11):1016–1022, 2018\.  ISSN 24058963.  10.1016/j.ifacol.2018.08.474.  URL https://linkinghub.elsevier.com/retrieve/pii/S2405896318316021.
  * Launchbury [2017] John Launchbury.  A DARPA Perspective on Artificial Intelligence, 2017.  URL https://www.youtube.com/watch?v=-O01G3tSYpU.
  * Lecun et al. [1998] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner.  Gradient-based learning applied to document recognition.  _Proceedings of the IEEE_ , 86(11):2278–2324, November 1998.  ISSN 1558-2256.  10.1109/5.726791.  Conference Name: Proceedings of the IEEE.
  * Lee et al. [2007] Jae-Gil Lee, Jiawei Han, and Kyu-Young Whang.  Trajectory clustering: a partition-and-group framework.  In _Proceedings of the 2007 ACM SIGMOD international conference on Management of data_ , SIGMOD ’07, pages 593–604, New York, NY, USA, June 2007. Association for Computing Machinery.  ISBN 978-1-59593-686-8.  10.1145/1247480.1247546.  URL https://doi.org/10.1145/1247480.1247546.
  * Lee et al. [2013] Jay Lee, Edzel Lapira, Behrad Bagheri, and Hung-an Kao.  Recent advances and trends in predictive manufacturing systems in big data environment.  _Manufacturing Letters_ , 1(1):38–41, October 2013.  ISSN 22138463.  10.1016/j.mfglet.2013.09.005.  URL https://linkinghub.elsevier.com/retrieve/pii/S2213846313000114.
  * Lee et al. [2011] Jonghwan Lee, Soonhung Han, and Jeongsam Yang.  Construction of a computer-simulated mixed reality environment for virtual factory layout planning.  _Computers in Industry_ , 62(1):86–98, January 2011.  ISSN 01663615.  10.1016/j.compind.2010.07.001.  URL https://linkinghub.elsevier.com/retrieve/pii/S016636151000093X.
  * Lee and Kim [2022] Jun-Ho Lee and Hyun-Jung Kim.  Reinforcement learning for robotic flow shop scheduling with processing time variations.  _International Journal of Production Research_ , 60(7):2346–2368, April 2022.  ISSN 0020-7543.  10.1080/00207543.2021.1887533.  URL https://doi.org/10.1080/00207543.2021.1887533.  Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/00207543.2021.1887533.
  * Lee and Krumm [2011] Wang-Chien Lee and John Krumm.  Trajectory Preprocessing.  In Yu Zheng and Xiaofang Zhou, editors, _Computing with Spatial Trajectories_ , pages 3–33. Springer, New York, NY, 2011.  ISBN 978-1-4614-1629-6.  10.1007/978-1-4614-1629-6_1.  URL https://doi.org/10.1007/978-1-4614-1629-6_1.
  * Lei [2016] Po-Ruey Lei.  A framework for anomaly detection in maritime trajectory behavior.  _Knowledge and Information Systems_ , 47(1):189–214, April 2016.  ISSN 0219-1377, 0219-3116.  10.1007/s10115-015-0845-4.  URL http://link.springer.com/10.1007/s10115-015-0845-4.
  * Leng et al. [2021] Jiewu Leng, Dewen Wang, Weiming Shen, Xinyu Li, Qiang Liu, and Xin Chen.  Digital twins-based smart manufacturing system design in Industry 4.0: A review.  _Journal of Manufacturing Systems_ , 60:119–137, July 2021\.  ISSN 02786125.  10.1016/j.jmsy.2021.05.011.  URL https://linkinghub.elsevier.com/retrieve/pii/S0278612521001151.
  * Levy [2000] Alon Y. Levy.  Logic-Based Techniques in Data Integration.  In Jack Minker, editor, _Logic-Based Artificial Intelligence_ , The Springer International Series in Engineering and Computer Science, pages 575–595. Springer US, Boston, MA, 2000.  ISBN 978-1-4615-1567-8.  10.1007/978-1-4615-1567-8_24.  URL https://doi.org/10.1007/978-1-4615-1567-8_24.
  * Li et al. [2017a] Chenzhao Li, Sankaran Mahadevan, You Ling, Sergio Choze, and Liping Wang.  Dynamic Bayesian Network for Aircraft Wing Health Monitoring Digital Twin.  _AIAA Journal_ , 55(3):930–941, March 2017a.  ISSN 0001-1452, 1533-385X.  10.2514/1.J055201.  URL https://arc.aiaa.org/doi/10.2514/1.J055201.
  * Li et al. [2016] Heng Li, Greg Chan, Johnny Kwok Wai Wong, and Martin Skitmore.  Real-time locating systems applications in construction.  _Automation in Construction_ , 63:37–47, March 2016.  ISSN 09265805.  10.1016/j.autcon.2015.12.001.  URL https://linkinghub.elsevier.com/retrieve/pii/S0926580515002411.
  * Li et al. [2020a] Huan Li, Hua Lu, Gang Chen, Ke Chen, Qinkuang Chen, and Lidan Shou.  Toward Translating Raw Indoor Positioning Data into Mobility Semantics.  _ACM/IMS Transactions on Data Science_ , 1(4):1–37, November 2020a.  ISSN 2691-1922.  10.1145/3385190.  Publisher: Association for Computing Machinery (ACM).
  * Li et al. [2019] Jianwen Li, Moshe Y. Vardi, and Kristin Y. Rozier.  Satisfiability Checking for Mission-Time LTL.  In Isil Dillig and Serdar Tasiran, editors, _Computer Aided Verification_ , Lecture Notes in Computer Science, pages 3–22, Cham, 2019\. Springer International Publishing.  ISBN 978-3-030-25543-5.  10.1007/978-3-030-25543-5_1.
  * Li and Srikumar [2019] Tao Li and Vivek Srikumar.  Augmenting Neural Networks with First-order Logic.  _ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference_ , pages 292–302, June 2019.  10.18653/v1/p19-1028.  URL https://arxiv.org/abs/1906.06298v3.  arXiv: 1906.06298 Publisher: Association for Computational Linguistics (ACL) ISBN: 9781950737482.
  * Li et al. [2020b] Tengfei Li, Jing Liu, Haiying Sun, Xiang Chen, Lipeng Zhang, and Junfeng Sun.  A spatio-temporal specification language and its completeness & decidability.  _Journal of Cloud Computing_ , 9(1):65, December 2020b.  ISSN 2192-113X.  10.1186/s13677-020-00209-3.  URL https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-020-00209-3.
  * Li et al. [2017b] Xiao Li, Cristian-Ioan Vasile, and Calin Belta.  Reinforcement learning with temporal logic rewards.  In _2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)_ , pages 3834–3839, September 2017b.  10.1109/IROS.2017.8206234.  ISSN: 2153-0866.
  * Li et al. [2018] Xiucheng Li, Kaiqi Zhao, Gao Cong, Christian S. Jensen, and Wei Wei.  Deep Representation Learning for Trajectory Similarity Computation.  In _2018 IEEE 34th International Conference on Data Engineering (ICDE)_ , pages 617–628, April 2018.  10.1109/ICDE.2018.00062.  ISSN: 2375-026X.
  * Li et al. [2017c] Xupeng Li, Bin Cui, Yiru Chen, Wentao Wu, and Ce Zhang.  MLog: towards declarative in-database machine learning.  _Proceedings of the VLDB Endowment_ , 10(12):1933–1936, August 2017c.  ISSN 2150-8097.  10.14778/3137765.3137812.  URL https://doi.org/10.14778/3137765.3137812.
  * Li [2018] Yuxi Li.  Deep Reinforcement Learning.  Technical Report arXiv:1810.06339, arXiv, October 2018.  URL http://arxiv.org/abs/1810.06339.  arXiv:1810.06339 [cs, stat] type: article.
  * Liao [2020] Hsuan-Cheng Liao.  A Survey of Reinforcement Learning with Temporal Logic Rewards.  TUM, 2020.  URL https://mediatum.ub.tum.de/doc/1579215/1579215.pdf.
  * Limketkai et al. [2005] Benson Limketkai, Lin Liao, and Dieter Fox.  Relational object maps for mobile robots.  In _Proceedings of the 19th international joint conference on Artificial intelligence_ , IJCAI’05, pages 1471–1476, San Francisco, CA, USA, July 2005. Morgan Kaufmann Publishers Inc.
  * Liu et al. [2021] Mengnan Liu, Shuiliang Fang, Huiyue Dong, and Cunzhi Xu.  Review of digital twin about concepts, technologies, and industrial applications.  _Journal of Manufacturing Systems_ , 58:346–361, January 2021.  ISSN 02786125.  10.1016/j.jmsy.2020.06.017.  URL https://linkinghub.elsevier.com/retrieve/pii/S0278612520301072.
  * Liu et al. [2018a] Ran Liu, Xiaolei Xie, Kaiye Yu, and Qiaoyu Hu.  A survey on simulation optimization for the manufacturing system operation.  _International Journal of Modelling and Simulation_ , 38(2):116–127, April 2018a.  ISSN 0228-6203, 1925-7082.  10.1080/02286203.2017.1401418.  URL https://www.tandfonline.com/doi/full/10.1080/02286203.2017.1401418.
  * Liu and Tang [2021] Xueyi Liu and Jie Tang.  Network representation learning: A macro and micro view.  _AI Open_ , 2:43–64, 2021.  ISSN 26666510.  10.1016/j.aiopen.2021.02.001.  URL https://linkinghub.elsevier.com/retrieve/pii/S2666651021000024.
  * Liu et al. [2012] Yunhao Liu, Yiyang Zhao, Lei Chen, Jian Pei, and Jinsong Han.  Mining Frequent Trajectory Patterns for Activity Monitoring Using Radio Frequency Tag Arrays.  _IEEE Transactions on Parallel and Distributed Systems_ , 23(11):2138–2149, November 2012.  ISSN 1558-2183.  10.1109/TPDS.2011.307.  Conference Name: IEEE Transactions on Parallel and Distributed Systems.
  * Liu et al. [2018b] Zheng Liu, Norbert Meyendorf, and Nezih Mrad.  The role of data fusion in predictive maintenance using digital twin.  _AIP Conference Proceedings_ , 1949(1):020023, April 2018b.  ISSN 0094-243X.  10.1063/1.5031520.  URL https://aip.scitation.org/doi/abs/10.1063/1.5031520.  Publisher: American Institute of Physics.
  * Ljunggren [2018] Henrik Ljunggren.  Using Deep Learning for Classifying Ship Trajectories.  In _2018 21st International Conference on Information Fusion (FUSION)_ , pages 2158–2164, July 2018.  10.23919/ICIF.2018.8455776.
  * Luna et al. [2015] Ryan Luna, Morteza Lahijanian, Mark Moll, and Lydia E. Kavraki.  Asymptotically Optimal Stochastic Motion Planning with Temporal Goals.  In H. Levent Akin, Nancy M. Amato, Volkan Isler, and A. Frank van der Stappen, editors, _Algorithmic Foundations of Robotics XI_ , volume 107, pages 335–352. Springer International Publishing, Cham, 2015.  ISBN 978-3-319-16594-3 978-3-319-16595-0.  10.1007/978-3-319-16595-0_20.  URL http://link.springer.com/10.1007/978-3-319-16595-0_20.  Series Title: Springer Tracts in Advanced Robotics.
  * Lutz and Miličić [2007] Carsten Lutz and Maja Miličić.  A Tableau Algorithm for Description Logics with Concrete Domains and General TBoxes.  _Journal of Automated Reasoning_ , 38(1-3):227–259, February 2007.  ISSN 0168-7433, 1573-0670.  10.1007/s10817-006-9049-7.  URL http://link.springer.com/10.1007/s10817-006-9049-7.
  * Lutz et al. [2008] Carsten Lutz, Frank Wolter, and Michael Zakharyaschev.  Temporal Description Logics: A Survey.  In _2008 15th International Symposium on Temporal Representation and Reasoning_ , pages 3–14, Montreal, QC, June 2008. IEEE.  ISBN 978-0-7695-3181-6.  10.1109/TIME.2008.14.  URL https://ieeexplore.ieee.org/document/4553284/.
  * Lv et al. [2018] Jianming Lv, Qing Li, Qinghui Sun, and Xintong Wang.  T-CONV: A Convolutional Neural Network for Multi-scale Taxi Trajectory Prediction.  In _2018 IEEE International Conference on Big Data and Smart Computing (BigComp)_ , pages 82–89, January 2018.  10.1109/BigComp.2018.00021.  ISSN: 2375-9356.
  * Löcklin et al. [2020] Andreas Löcklin, Tamás Ruppert, László Jakab, Robert Libert, Nasser Jazdi, and Michael Weyrich.  Trajectory Prediction of Humans in Factories and Warehouses with Real-Time Locating Systems.  In _2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)_ , volume 1, pages 1317–1320, September 2020.  10.1109/ETFA46521.2020.9211913.  ISSN: 1946-0759.
  * Macchi et al. [2018] Marco Macchi, Irene Roda, Elisa Negri, and Luca Fumagalli.  Exploring the role of Digital Twin for Asset Lifecycle Management.  _IFAC-PapersOnLine_ , 51(11):790–795, 2018.  ISSN 24058963.  10.1016/j.ifacol.2018.08.415.  URL https://linkinghub.elsevier.com/retrieve/pii/S2405896318315416.
  * MachineMetrics [2022] MachineMetrics.  Elminate guesswork with real-time production tracking, 2022.  URL https://www.machinemetrics.com/production-tracking-software.
  * Manhaeve et al. [2021] Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig, Thomas Demeester, and Luc De Raedt.  Neural probabilistic logic programming in DeepProbLog.  _Artificial Intelligence_ , 298:103504, September 2021.  ISSN 00043702.  10.1016/j.artint.2021.103504.  URL https://linkinghub.elsevier.com/retrieve/pii/S0004370221000552.
  * Mantenoglou et al. [2020] Periklis Mantenoglou, Alexander Artikis, and Georgios Paliouras.  Online Probabilistic Interval-Based Event Calculus.  In _ECAI 2020_ , pages 2624–2631. IOS Press, 2020.  10.3233/FAIA200399.  URL https://ebooks.iospress.nl/doi/10.3233/FAIA200399.
  * Marcus [2020] Gary Marcus.  The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence.  Technical Report arXiv:2002.06177, arXiv, February 2020.  URL http://arxiv.org/abs/2002.06177.  arXiv:2002.06177 [cs] type: article.
  * Markosian et al. [2007] Lawrence Z. Markosian, Masoud Mansouri-Samani, Peter C. Mehlitz, and Tom Pressburger.  Program Model Checking Using Design-for-Verification: NASA Flight Software Case Study.  In _2007 IEEE Aerospace Conference_ , pages 1–9, March 2007\.  10.1109/AERO.2007.352767.  ISSN: 1095-323X.
  * Mei et al. [2020] Hongyuan Mei, Guanghui Qin, Minjie Xu, and Jason Eisner.  Neural datalog through time: informed temporal modeling via logical specification.  In _Proceedings of the 37th International Conference on Machine Learning_ , ICML’20, pages 6808–6819. JMLR.org, July 2020.
  * Mo et al. [2021] Baichuan Mo, Zhenliang Ma, Haris N. Koutsopoulos, and Jinhua Zhao.  Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data.  _Journal of Advanced Transportation_ , 2021:1–15, February 2021.  ISSN 2042-3195, 0197-6729.  10.1155/2021/5597130.  URL https://www.hindawi.com/journals/jat/2021/5597130/.
  * Mohamed et al. [2017] Reham Mohamed, Heba Aly, and Moustafa Youssef.  Accurate Real-time Map Matching for Challenging Environments.  _IEEE Transactions on Intelligent Transportation Systems_ , 18(4):847–857, April 2017.  ISSN 1558-0016.  10.1109/TITS.2016.2591958.  Conference Name: IEEE Transactions on Intelligent Transportation Systems.
  * Motion Analysis [2022] Motion Analysis.  Motion analysis powered by Cortex, 2022.  URL https://motionanalysis.com/industrial/.
  * Muggleton and de Raedt [1994] Stephen Muggleton and Luc de Raedt.  Inductive Logic Programming: Theory and methods.  _The Journal of Logic Programming_ , 19-20:629–679, May 1994.  ISSN 0743-1066.  10.1016/0743-1066(94)90035-3.  URL https://www.sciencedirect.com/science/article/pii/0743106694900353.
  * Nagorny et al. [2017] Kevin Nagorny, Pedro Lima-Monteiro, Jose Barata, and Armando Walter Colombo.  Big Data Analysis in Smart Manufacturing: A Review.  _International Journal of Communications, Network and System Sciences_ , 10(3):31–58, March 2017.  10.4236/ijcns.2017.103003.  URL http://www.scirp.org/Journal/Paperabs.aspx?paperid=75656.  Number: 3 Publisher: Scientific Research Publishing.
  * Natarajan et al. [2013] Sriraam Natarajan, Kristian Kersting, Edward Ip, David R. Jacobs, and Jeffrey Carr.  Early prediction of coronary artery calcification levels using machine learning.  In _Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence_ , AAAI’13, pages 1557–1562, Bellevue, Washington, July 2013. AAAI Press.
  * Nelson and Pecheur [2003] Stacy D. Nelson and Charles Pecheur.  Formal Verification for a Next-Generation Space Shuttle.  In Michael G. Hinchey, James L. Rash, Walter F. Truszkowski, Christopher Rouff, and Diana Gordon-Spears, editors, _Formal Approaches to Agent-Based Systems_ , Lecture Notes in Computer Science, pages 53–67, Berlin, Heidelberg, 2003. Springer.  ISBN 978-3-540-45133-4.  10.1007/978-3-540-45133-4_5.
  * Nguyen et al. [2006] Son T. Nguyen, Hung T. Nguyen, Philip B. Taylor, and James Middleton.  Improved Head Direction Command Classification using an Optimised Bayesian Neural Network.  In _2006 International Conference of the IEEE Engineering in Medicine and Biology Society_ , pages 5679–5682, New York, NY, August 2006. IEEE.  ISBN 978-1-4244-0032-4.  10.1109/IEMBS.2006.260430.  URL http://ieeexplore.ieee.org/document/4463095/.
  * Nikhil and Morris [2019] Nishant Nikhil and Brendan Tran Morris.  Convolutional Neural Network for Trajectory Prediction.  In Laura Leal-Taixé and Stefan Roth, editors, _Computer Vision – ECCV 2018 Workshops_ , volume 11131, pages 186–196. Springer International Publishing, Cham, 2019.  ISBN 978-3-030-11014-7 978-3-030-11015-4.  10.1007/978-3-030-11015-4_16.  URL http://link.springer.com/10.1007/978-3-030-11015-4_16.  Series Title: Lecture Notes in Computer Science.
  * Nilsson [1986] Nils J. Nilsson.  Probabilistic logic.  _Artificial Intelligence_ , 28(1):71–87, February 1986.  ISSN 0004-3702.  10.1016/0004-3702(86)90031-7.  URL https://www.sciencedirect.com/science/article/pii/0004370286900317.
  * Nitti et al. [2014] Davide Nitti, Tinne De Laet, and Luc De Raedt.  Relational object tracking and learning.  In _2014 IEEE International Conference on Robotics and Automation (ICRA)_ , pages 935–942, May 2014.  10.1109/ICRA.2014.6906966.  ISSN: 1050-4729.
  * Niu et al. [2014] Xiaoguang Niu, Ying Zhu, and Xining Zhang.  DeepSense: A novel learning mechanism for traffic prediction with taxi GPS traces.  In _2014 IEEE Global Communications Conference_ , pages 2745–2750, December 2014.  10.1109/GLOCOM.2014.7037223.  ISSN: 1930-529X.
  * Oord et al. [2016] Aäron van den Oord, S. Dieleman, H. Zen, K. Simonyan, Oriol Vinyals, A. Graves, Nal Kalchbrenner, A. Senior, and K. Kavukcuoglu.  WaveNet: A Generative Model for Raw Audio.  In _SSW_ , 2016.
  * Ouelhadj and Petrovic [2009] Djamila Ouelhadj and Sanja Petrovic.  A survey of dynamic scheduling in manufacturing systems.  _Journal of Scheduling_ , 12(4):417–431, August 2009.  ISSN 1094-6136, 1099-1425.  10.1007/s10951-008-0090-8.  URL http://link.springer.com/10.1007/s10951-008-0090-8.
  * Ovacik and Uzsoy [1994] Irfan M Ovacik and Reha Uzsoy.  Exploiting Shop Floor Status Information to Schedule Complex Job Shops.  _Journal of Manufacturing Systems_ , 13(2):12, 1994.
  * Ovacik and Uzsoy [1997] Irfan M. Ovacik and Reha Uzsoy.  _Decomposition Methods for Complex Factory Scheduling Problems_.  Springer US, Boston, MA, 1997.  ISBN 978-1-4613-7906-5 978-1-4615-6329-7.  10.1007/978-1-4615-6329-7.  URL http://link.springer.com/10.1007/978-1-4615-6329-7.
  * Palmer et al. [2018] Claire Palmer, Esmond N. Urwin, Ali Niknejad, Dobrila Petrovic, Keith Popplewell, and Robert I. M. Young.  An ontology supported risk assessment approach for the intelligent configuration of supply networks.  _Journal of Intelligent Manufacturing_ , 29(5):1005–1030, June 2018.  ISSN 0956-5515, 1572-8145.  10.1007/s10845-016-1252-8.  URL http://link.springer.com/10.1007/s10845-016-1252-8.
  * Panzer and Bender [2021] Marcel Panzer and Benedict Bender.  Deep reinforcement learning in production systems: a systematic literature review.  _International Journal of Production Research_ , 0(0):1–26, September 2021.  ISSN 0020-7543.  10.1080/00207543.2021.1973138.  URL https://doi.org/10.1080/00207543.2021.1973138.
  * Parent et al. [2013] Christine Parent, Stefano Spaccapietra, Chiara Renso, Gennady Andrienko, Natalia Andrienko, Vania Bogorny, Maria Luisa Damiani, Aris Gkoulalas-Divanis, Jose Macedo, Nikos Pelekis, Yannis Theodoridis, and Zhixian Yan.  Semantic trajectories modeling and analysis.  _ACM Computing Surveys_ , 45(4), August 2013.  ISSN 03600300.  10.1145/2501654.2501656.
  * Parisi and Grant [2014] Francesco Parisi and John Grant.  Integrity Constraints for Probabilistic Spatio-Temporal Knowledgebases.  In Umberto Straccia and Andrea Calì, editors, _Scalable Uncertainty Management_ , Lecture Notes in Computer Science, pages 251–264, Cham, 2014. Springer International Publishing.  ISBN 978-3-319-11508-5.  10.1007/978-3-319-11508-5_21.
  * Parisi and Grant [2016] Francesco Parisi and John Grant.  Knowledge Representation in Probabilistic Spatio-Temporal Knowledge Bases.  _Journal of Artificial Intelligence Research_ , 55:743–798, March 2016.  ISSN 1076-9757.  10.1613/jair.4883.  URL https://jair.org/index.php/jair/article/view/10992.
  * Park et al. [2018] SeongHyeon Park, Byeongdo Kim, C. Kang, C. Chung, and J. Choi.  Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture.  _2018 IEEE Intelligent Vehicles Symposium (IV)_ , 2018.  10.1109/IVS.2018.8500658.
  * Pearl [1988] Judea Pearl.  _Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference_.  Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1988.  ISBN 978-1-55860-479-7.
  * Pellissier Tanon et al. [2017] Thomas Pellissier Tanon, Daria Stepanova, Simon Razniewski, Paramita Mirza, and Gerhard Weikum.  Completeness-Aware Rule Learning from Knowledge Graphs.  In Claudia d’Amato, Miriam Fernandez, Valentina Tamma, Freddy Lecue, Philippe Cudré-Mauroux, Juan Sequeda, Christoph Lange, and Jeff Heflin, editors, _The Semantic Web – ISWC 2017_ , Lecture Notes in Computer Science, pages 507–525, Cham, 2017. Springer International Publishing.  ISBN 978-3-319-68288-4.  10.1007/978-3-319-68288-4_30.
  * Pellissier Tanon et al. [2020] Thomas Pellissier Tanon, Gerhard Weikum, and Fabian Suchanek.  YAGO 4: A Reason-able Knowledge Base.  In Andreas Harth, Sabrina Kirrane, Axel-Cyrille Ngonga Ngomo, Heiko Paulheim, Anisa Rula, Anna Lisa Gentile, Peter Haase, and Michael Cochez, editors, _The Semantic Web_ , volume 12123, pages 583–596. Springer International Publishing, Cham, 2020.  ISBN 978-3-030-49460-5 978-3-030-49461-2.  URL http://link.springer.com/10.1007/978-3-030-49461-2_34.
  * Phillips and Tang [2019] Jeff M. Phillips and Pingfan Tang.  Simple Distances for Trajectories via Landmarks.  In _Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems_ , pages 468–471, Chicago IL USA, November 2019. ACM.  ISBN 978-1-4503-6909-1.  10.1145/3347146.3359098.  URL https://dl.acm.org/doi/10.1145/3347146.3359098.
  * Pitsikalis et al. [2020] Manolis Pitsikalis, Konstantina Bereta, Marios Vodas, Dimitris Zissis, and Alexander Artikis.  Event processing for maritime situational awareness.  In _Big Data Analytics for Time-Critical Mobility Forecasting: From Raw Data to Trajectory-Oriented Mobility Analytics in the Aviation and Maritime Domains_ , pages 255–274. Springer International Publishing, January 2020.  ISBN 978-3-030-45164-6.  10.1007/978-3-030-45164-6_9.
  * Poole [2003] David Poole.  First-order probabilistic inference.  In _Proceedings of the 18th international joint conference on Artificial intelligence_ , IJCAI’03, pages 985–991, San Francisco, CA, USA, August 2003. Morgan Kaufmann Publishers Inc.
  * Prato [2009] Carlo Giacomo Prato.  Route choice modeling: past, present and future research directions.  _Journal of Choice Modelling_ , 2(1):65–100, 2009\.  ISSN 17555345.  10.1016/S1755-5345(13)70005-8.  URL https://linkinghub.elsevier.com/retrieve/pii/S1755534513700058.