--- title: "SOC830 Lab 5: Univariate Statistics" lastmod: 2018-12-28T00:00:00.000Z draft: false type: docs maths: true output: blogdown::html_page: toc: true linktitle: "Lab 5: Univariate Statistics" menu: docs: parent: SOC830(SOCI702) weight: 30 ---
The fifth lab session covers the following:
We will use four packages for this lab. Load them using the following code:
library(sjlabelled)
library(sjmisc)
library(sjPlot)
library(summarytools)
Note that the loading order of packages is essential. If you do not follow the suggested loading order, you will not get the same result as in the all below examples.
Also, you may see some warning messages when you load these packages. Please ignore them. They will not affect your analytic outcomes. The issue is mostly from the fact that you did not update R or necessary packages. Nonetheless, it is always recommended to update R and R packages.
This lab uses the 2009 AuSSa dataset. You can download the file of this dataset in the course website(iLearn). Download the data file and put it in your working directory. Then, run the following code:
aus2009 <-readRDS("aussa2009.rds")
The dataset is loaded as aus2009.
I briefly introduced how to make a frequency table in Lab 4. ‘frq(data name$variable name)
’ generates a frequecy table. For instance, the following code produces a frequency table of sex in aus2009:
frq(aus2009$sex)
##
## # R: Sex (x) <numeric>
## # total N=1525 valid N=1494 mean=1.57 sd=0.50
##
## val label frq raw.prc valid.prc cum.prc
## 1 Male 647 42.43 43.31 43.31
## 2 Female 847 55.54 56.69 100.00
## NA NA 31 2.03 NA NA
In the outcome, NA means missing values (item non-responses). frq is frequencies, raw.prc denotes raw percentages (based on the number of total cases), valid.prc indicates valid percentages (based on the number of valid cases), and cum.prc means cumulative percentages.
Alternatively, you can make a frequency table using ‘freq()
’ from summarytools package. I prefer to use ‘freq()
’ because this function provides more detailed information.
freq(aus2009$sex)
## Frequencies
## aus2009$sex
## Label: R: Sex
## Type: Numeric
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ----------- ------ --------- -------------- --------- --------------
## 1 647 43.31 43.31 42.43 42.43
## 2 847 56.69 100.00 55.54 97.97
## <NA> 31 2.03 100.00
## Total 1525 100.00 100.00 100.00 100.00
The problem of this output is that value labels are not shown in the frequency table. This is because summarytools package does not support the value labels assigned by sjlabelled package. To show value labels, we need to add one more function: ‘to_label()
’.
freq(to_label(aus2009$sex))
## Frequencies
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ------------ ------ --------- -------------- --------- --------------
## Male 647 43.31 43.31 42.43 42.43
## Female 847 56.69 100.00 55.54 97.97
## <NA> 31 2.03 100.00
## Total 1525 100.00 100.00 100.00 100.00
Now, you can see the labels of all values in the frequency table. In the table, Freq is frequencies, % Valid indicates valid percentages (based on the number of valid cases), and % Valid Cum. means cumulative percentages based on valid cases, % Total denotes raw percentages (based on the number of total cases), and % Total Cum. is cumulative percentages based on total cases. Also, this frequency table provides the number of total cases at the bottom row.
The next codes will create frequency tables of marital and richcol.
freq(to_label(aus2009$marital))
## Frequencies
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ------------------------------------------------------------ ------ --------- -------------- --------- --------------
## Married 926 62.11 62.11 60.72 60.72
## Widowed 81 5.43 67.54 5.31 66.03
## Divorced 129 8.65 76.19 8.46 74.49
## Separated (married but sep./not living w legal spouse) 30 2.01 78.20 1.97 76.46
## Never married, single 325 21.80 100.00 21.31 97.77
## <NA> 34 2.23 100.00
## Total 1525 100.00 100.00 100.00 100.00
freq(to_label(aus2009$richcol))
## Frequencies
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## -------------------------------- ------ --------- -------------- --------- --------------
## Strongly agree 133 8.91 8.91 8.72 8.72
## Agree 378 25.34 34.25 24.79 33.51
## Neither agree nor disagree 219 14.68 48.93 14.36 47.87
## Disagree 553 37.06 85.99 36.26 84.13
## Strongly disagree 209 14.01 100.00 13.70 97.84
## <NA> 33 2.16 100.00
## Total 1525 100.00 100.00 100.00 100.00
The frequency table of continuous variables is not easy to read because there are so many values in them. Consequently, researchers often transform continuous variables into ordinal ones and then create the frequency table. Let’s try to make the frequency table of respondents’ age. We will first recode age into a variable of age groups (you did this in Lab 4) and then make the frequency table of age groups.
aus2009 <- rec(aus2009, age, rec = "min:19 = 1; 20:29 = 2; 30:39 = 3; 40:49 = 4;
50:59 = 5; 60:69 = 6; 70:79 = 7; 80:89 = 8; 90:max = 9",
append = TRUE)
aus2009$age_r <- set_label(aus2009$age_r, label = "Age Category")
aus2009$age_r <- set_labels(aus2009$age_r, labels = c ("10s" = 1,
"20s" = 2,
"30s" = 3,
"40s" = 4,
"50s" = 5,
"60s" = 6,
"70s" = 7,
"80s" = 8,
"90s" = 9))
freq(to_label(aus2009$age_r))
## Frequencies
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ----------- ------ --------- -------------- --------- --------------
## 10s 31 2.09 2.09 2.03 2.03
## 20s 134 9.03 11.12 8.79 10.82
## 30s 182 12.26 23.38 11.93 22.75
## 40s 251 16.91 40.30 16.46 39.21
## 50s 338 22.78 63.07 22.16 61.38
## 60s 302 20.35 83.42 19.80 81.18
## 70s 176 11.86 95.28 11.54 92.72
## 80s 67 4.51 99.80 4.39 97.11
## 90s 3 0.20 100.00 0.20 97.31
## <NA> 41 2.69 100.00
## Total 1525 100.00 100.00 100.00 100.00
R does not have a built-in function to compute a mode of a variable. So, I made a function called ‘getmode()
’ which lets you calculate the mode. First, run the following code:
getmode <- function(v) {
uniqv <- unique(v)
uniqv[which.max(tabulate(match(v, uniqv)))]
}
You will see getmode in the tab of Environment. This is the custom function you just added. Thus, every time you want to compute the mode, you need to run the above code first.
Let’s caculate the mode of sex, martial, richcol, age and age_r. Again, we will use ’to_label()
function for nominal and ordinal variables to see value labels instead of values.
getmode(to_label(aus2009$sex))
## [1] Female
## Levels: Male Female
getmode(to_label(aus2009$marital))
## [1] Married
## 5 Levels: Married Widowed ... Never married, single
getmode(to_label(aus2009$richcol))
## [1] Disagree
## 5 Levels: Strongly agree Agree Neither agree nor disagree ... Strongly disagree
getmode(to_label(aus2009$age))
## [1] 62
getmode(to_label(aus2009$age_r))
## [1] 50s
## Levels: 10s 20s 30s 40s 50s 60s 70s 80s 90s
The results show that ‘Female’ is the mode for sex, ‘Married’ for marital, ‘Disagree’ for richcol, 62 for age, and ‘50s’ for age_r.
‘descr()
’ from summarytools package shows various descriptive statistics. The notable statistics include:
The following code shows descriptive statistics of richcol(ordinal variable):
descr(aus2009$richcol)
## Descriptive Statistics
## aus2009$richcol
## Label: Q2c Getting ahead: In [Rs country] only the rich can afford the costs of attending university.
## N: 1525
##
## richcol
## ----------------- ---------
## Mean 3.22
## Std.Dev 1.22
## Min 1.00
## Q1 2.00
## Median 4.00
## Q3 4.00
## Max 5.00
## MAD 1.48
## IQR 2.00
## CV 0.38
## Skewness -0.26
## SE.Skewness 0.06
## Kurtosis -1.09
## N.Valid 1492.00
## Pct.Valid 97.84
Please note that incase of ordinal variables, you need to be careful in interpreting the results. For instance, the median value of richcol is 4, which means the median response is “Disagree”.
The following code will show descriptive statistics of age(continuous variable):
descr(aus2009$age)
## Descriptive Statistics
## aus2009$age
## N: 1525
##
## age
## ----------------- ---------
## Mean 52.53
## Std.Dev 16.78
## Min 17.00
## Q1 41.00
## Median 54.00
## Q3 64.00
## Max 93.00
## MAD 17.79
## IQR 23.00
## CV 0.32
## Skewness -0.18
## SE.Skewness 0.06
## Kurtosis -0.68
## N.Valid 1484.00
## Pct.Valid 97.31
There are many packages to visualise your data in R. In our course I will use sjPlot package because it supports the labels assigned by sjlabelled package and is easy to use. If you want to learn more advanced graphics of R, I recommend using ggplot2 package. One good reliable online resource can be found here.
Bar graphs provide appropriate ways to present nominal or ordinal variables graphically. ‘plot_frq(data name$variable name, type = "bar")
’ makes a bar graph of the specified variable. For example, the following code will make a bar graph of sex in aus2009:
plot_frq(aus2009$sex, type = "bar")
If you want to add the title of a figure, add ‘title = "Any title"
’ to the code. The following code will add “Gender distribution” as the title of the figure:
plot_frq(aus2009$sex, type = "bar", title = "Gender Distribution")
The title of X-axis(R: Sex) is somewhat bizarre. The title of X-axis can be changed by adding ‘axis.title = "Any title"
’. I change it into “Gender”. Thus, the code is:
plot_frq(aus2009$sex, type = "bar", title = "Gender Distribution",
axis.title = "Gender")
If you want to remove percentages of each category at the top of bars, add ‘show.prc = FALSE
’ to the code.
plot_frq(aus2009$sex, type = "bar", title = "Gender Distribution",
show.prc = FALSE)
If you want to remove frequencies of each category at the top of bars, add ‘show.n = FALSE
’ to the code.
plot_frq(aus2009$sex, type = "bar", title = "Gender Distribution",
show.n = FALSE)
Next, let’s make a bar graph of another variable, marital.
plot_frq(aus2009$marital, type = "bar", title = "Marital Status",
axis.title = "Marital Status", show.n = FALSE)
You may notice that one axis label is too long, which makes it difficult to read other labels. So, we will change this long label into a more brief one. To change the axis label, ‘axis.labels
’ option will be used.
plot_frq(aus2009$marital, type = "bar", title = "Marital Status",
axis.title = "Marital Status",
axis.labels = c("Married", "Widowed", "Divorced",
"Separated", "Never Married or Single"),
show.n = FALSE)
We assigned “Married”, “Widowed”, “Divorced”, “Separated”, and “Never Married or Single” to labels of each axis, respectively.
A stacked bar graph is used to break down and compare parts of a whole. Each bar in the graph represents a whole, and segments in the bar represent different categories or responses of that whole. Stacked bar graphs can be created by ‘sjp.stackfrq(data name$variable name)
’. The following code makes a stacked bar graph of marital:
sjp.stackfrq(aus2009$marital, title = "Marital Status",
axis.labels = "Marital Status",
legend.labels = c("Married", "Widowed", "Divorced",
"Separated", "Never Married or Single"))
Note that I changed the label of axis and legend using ‘axis.labels
’ and ‘legend.labels
’ option.
Histograms are used with continuous variables which have many scores. Histograms look like bar graphs, except that the sides of the “bars” touch to form a continuous series.
To make histograms, use ‘plot_frq(data name$variable name, type = "hist")
’. The following code creates a histogram of age.
plot_frq(aus2009$age, type = "hist", title = "Distribution of Age", axis.title = "Age")
In this graph, I would like to set the distance between axis labels equal to 10. So, I add ‘grid.breaks = 10
’ to the code. Thus, the code is:
plot_frq(aus2009$age, type = "hist", title = "Distribution of Age", axis.title = "Age",
grid.breaks = 10)
The R codes you have written so far look like:
################################################################################
# Week6: Univariate Analysis
# 01/04/2019
# SOC830, SOCI702, SOCX830
################################################################################
# load packages
library(sjlabelled)
library(sjmisc)
library(sjPlot)
library(summarytools)
# load Aussa2009
aus2009 <-readRDS("aussa2009.rds")
# Frequency table
frq(aus2009$sex)
freq(aus2009$sex)
freq(to_label(aus2009$sex))
freq(to_label(aus2009$marital))
freq(to_label(aus2009$richcol))
#freq(to_label(aus2009$age))
# Make a frequency table of age gropus
aus2009 <- rec(aus2009, age, rec = "min:19 = 1; 20:29 = 2; 30:39 = 3; 40:49 = 4;
50:59 = 5; 60:69 = 6; 70:79 = 7; 80:89 = 8; 90:max = 9",
append = TRUE)
aus2009$age_r <- set_label(aus2009$age_r, label = "Age Category")
aus2009$age_r <- set_labels(aus2009$age_r, labels = c ("10s" = 1,
"20s" = 2,
"30s" = 3,
"40s" = 4,
"50s" = 5,
"60s" = 6,
"70s" = 7,
"80s" = 8,
"90s" = 9))
freq(to_label(aus2009$age_r))
# Mode
getmode <- function(v) {
uniqv <- unique(v)
uniqv[which.max(tabulate(match(v, uniqv)))]
}
getmode(to_label(aus2009$sex))
getmode(to_label(aus2009$marital))
getmode(to_label(aus2009$richcol))
getmode(to_label(aus2009$age))
getmode(to_label(aus2009$age_r))
# Descriptive statistics
descr(aus2009$richcol)
descr(aus2009$age)
# Visualise the distribution
## Bar graph
plot_frq(aus2009$sex, type = "bar")
plot_frq(aus2009$sex, type = "bar", title = "Gender Distribution") # change the title of figure
plot_frq(aus2009$sex, type = "bar", title = "Gender Distribution",
axis.title = "Gender") # change the title of axis
plot_frq(aus2009$sex, type = "bar", title = "Gender Distribution",
show.prc = FALSE) # do not show percentages
plot_frq(aus2009$sex, type = "bar", title = "Gender Distribution",
show.n = FALSE) # do not show frequencies
plot_frq(aus2009$marital, type = "bar", title = "Marital Status",
axis.title = "Marital Status", show.n = FALSE)
plot_frq(aus2009$marital, type = "bar", title = "Marital Status",
axis.title = "Marital Status",
axis.labels = c("Married", "Widowed", "Divorced",
"Separated", "Never Married or Single"),
show.n = FALSE) # change the labels of axis
## Stacked bar graph for Likert scales
sjp.stackfrq(aus2009$marital, title = "Marital Status",
axis.labels = "Marital Status",
legend.labels = c("Married", "Widowed", "Divorced",
"Separated", "Never Married or Single"))
## Histogram
plot_frq(aus2009$age, type = "hist", title = "Distribution of Age", axis.title = "Age")
plot_frq(aus2009$age, type = "hist", title = "Distribution of Age", axis.title = "Age",
grid.breaks = 10) # grid.breks:sets the distance between breaks for the axis


shape = (1,)
else:
shape = tensordot_output.shape
return (shape1[0],) + shape
def compute_mask(self, inputs, mask=None):
if mask is None or all([m is None for m in mask]):
shape2.pop(0)
output_shape = shape1 + shape2
if len(output_shape) == 1:
output_shape += [1]
return tuple(output_shape)
def compute_mask(self, inputs, mask=None):
if mask is None or all([m is None for m in mask]):
return None


If you check the Network tab in your console you will be able to see that 'sample-feature-module.js' has been downloaded.
By creating an app split in different feature modules you will reduce the intial loading time of your application.
Like everyone if the website you're trying to access takes too much time to load you'll simply go somewhere else and close the page.
Another advantage of enabling lazy loading for your modules is to reduce the data needed for your application. Sometimes the issue will be the network speed but it can also be the data plan of your users.
In conclusion, creating feature modules enables you to take advantage of the lazy-loading but also force you to think about the folder architecture of your application, resulting in more "newcomers-friendly" source code.


Event Location:
Faculty of Computer Science
Vecna pot 113
Ljubljana, Ljubljana, Slovenia
Abstract:
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Abstract:
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Abstract:
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Abstract:
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Abstract:
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Speaker(s): Regis Baccaro,
Abstract:
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Speaker(s): Gianluca Sartori,
Abstract:
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Speaker(s): William Durkin,
Abstract:
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Speaker(s): Gabi Münster,
Abstract:
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Speaker(s): Erland Sommarskog,
Abstract:
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Speaker(s): Karol Papaj,
Abstract:
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Speaker(s): Prathy Kamasani,
Abstract:
When you need to extract data from the database you are writing, more or less complex, T-SQL code. Often simplistic and procedural approach reflects what you have in your mind, however this could have a negatively impact about performance because the database engine might think otherwise. Fortunately T-SQL, as a declarative language, allows us to ask the "what" and delegate to the engine the "how". Everything works best as long as you respect a few simple rules and you may use special constructs. In this session, with few slides and a lot of real-case scenarios, you can see the advantages of writing the query for high performance, even when they are written by that "someone else" called ORM.
Speaker(s): Emanuele Zanchettin,
Abstract:
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Speaker(s): Tobias Koprowski,
Abstract:
There are many community scripts out there that DBAs can use without having to write them yourself. Like Ola Hallengrens scripts, or Brent Ozars or sp_WhoIsActive by Adam Machanic. But how about Powershell? Turns out, this world is still a bit unknown to the DBA but there is a fast growing community with extremely useful scripts for day to day SQL Server management. In this session we will take a look into my favourite scripts and we will also write a bit of Powershell ourselves.
Speaker(s): André Kamman,
Abstract:
SQL Server 2016 brings the topic "in-memory" to a new level. Not that previous versions did not hold as much as data as possible inside of memory through caching, but now we enter a new era: in-memory row stores, in-memory column stores and compiled stored procedures are different from what we have seen so far. In this session, you will see how you can leverage those new features and how you can come by existing limitations. Come back to office with the knowledge how to boost the performance of your ETL, queries and reports into the new era.
Speaker(s): Markus Ehrenmueller-Jensen,
Abstract:
As IOT climbs up the Peak of inflated expectations on the hype cycle, let's take a closer look at Microsoft's approach to the Internet of Things. They have even given us two reference architecture samples to play with. This session will introduce you to Azure's IoT offerings, how to do analysis on your data with Stream Analytics and then present this in a dashboard with PowerBI. We will use demo data, as well as take a look at real world examples.
Speaker(s): Johan Ludvig Brattås,
Abstract:
U-SQL is the new kid on the SQL language block. In this session we'll look at the what, where, why and how of the language. Is it a query language? Is it a data transformation language? Will it replace T-SQL? Should you learn it? Where is it? What is Data Lake? We'll discuss all this and more.
Speaker(s): Alex Whittles,
Abstract:
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Speaker(s): Stijn Wynants,
Abstract:
Session Details not available
Speaker(s): n/a n/a,
Abstract:
The queries that are used for reports are usually the least efficient because you are dealing with huge amounts of data from tables sometimes joined many, many times. In this session we will go through good and bad examples of aggregating the data from your relational database. You will see tips on how to get good use of the indexes, how to lower the number of actual table queries and other tricks that will make your queries run faster.
Speaker(s): Dejan Dular,
Abstract:
From time to time on the forums emerge people in really tough situation: data is deleted, database is in SIMPLE recovery model, and as a cherry on top - there is no backup! It might be boss's private database that nobody has put into backup scheme yet, or some new database that was missing from the backup scheme. It is nearly impossible to recover the lost data from that situation. But I will show you at least three methods. After this presentation, you will be prepared to solve such a tough case. It is really important to know two simple steps you must do really fast, and after that you can relax and extract the data with peace of mind and without time pressure.
Speaker(s): Vedran Kesegic,
Abstract:
SQL Server is full of misunderstood features and misnomers that could affect how it is operating and respectively our applications. If you do not want to be surprised in a bad way by some intended, but not widely known SQL functionalities, join me in this session where we will, in an entertaining way, examine some of the most common behaviors that you probably have not expected.
Speaker(s): Miroslav Dimitrov,
Abstract:
Introducing the new scripting language for tabular models. Before SQL Server 2016 tabular models was wrapped into a multidimensional constructs. TMSL is the new native language for tabular which is build on JSON - this makes it easy to understand, modify and deploy. During this session I will go through and explain some examples on generating a SSAS tabular model by using the new TSML to demonstrate how powerfull this feature is and how you can use it to save time and effort.mduring this session I will spend some time explaining a real world example and how to make an implementation with MDS and business users.
Speaker(s): Bent Pedersen,
Abstract:
As a DBA, the Error log is an essential daily tool. Learning and understanding its content are not the last part of the job, we also have to manage it to obtain a better interpretation. In this session, we will discuss about this famous and essential feedback tool, too often skipped from maintenance plans.
Speaker(s): Stephane Haby,
Abstract:
This session examines in depth the Execution Plans on SQL Server. To go beyond the basics such as how to reading an Execution Plan or detecting Index Scans or Lookups, you must acquire a deeper understanding of how the Query Optimizer builds the optimal Execution Plan and how you can influence the choice of a particular index or a particular join operator, up to force a particular Execution Plan without changing the query text. Finally, we will explore the Plan Cache memory area where SQL Server stores Execution Plans.
Speaker(s): Sergio Govoni,
Abstract:
As SQL Server 2016 is coming soon, it is important to see how new features are working. This session is about Always Encrypted feature and covers the followings: - What encryption types are in SQL Server? - A business scenario: handling sensitive data w/o giving access to DBA team. - Always Encrypted basics - Creating table with encryption by using AE - Migrating data from regular table to AE table - Decrypting data from AE table - AE under the hood - Performance impact
Speaker(s): Janos Berke,
This is a list of speakers from the XML Guidebook records. The details and URLs were valid at the time of the event.
LinkedIn: http://www.linkedin.com/in/sgovoni
Contact: https://mvp.microsoft.com/it-it/PublicProfile/4029181?fullName=Sergio%20Govoni
Since 1999 Sergio Govoni has been a software developer; in 2000 he got a degree in Computer Science at "Università degli Studi" in Ferrara (Italy). He has worked for over 18 years in Centro Software, a software house that produces the best ERP for manufacturing companies that are export oriented. Now, he manages the development product team and he is constantly involved on several team projects, where he focues his attention on the architecture and the mission-critical technical details. Since 2016 he is the vice president of the UGISS (www.ugiss.org). For the provided help to technical communities and for sharing his own experience, since 2010 he has received the Microsoft Data Platform MVP award.
LinkedIn: https://www.linkedin.com/in/stijn-wynants-ba528660?trk=hp-identity-name
Contact: http://swyssql.wordpress.com
Stijn is a Microsoft Premier Field Engineer on Data AI and a DataMinds core member. He is also one of the organizers of the local user group sessions the yearly event called DataMinds Connect (SQL Server Days). He is a speaker at various international events (SQL Saturday,PASS Summit,Difinity,...). He spends his days tuning SQL Servers solving problems with the full data platform. His focus is primarily on sql server core, but has also done various BI ETL projects. He gets his kicks out of Columnstore indexes tuning queries but shivers at the sight of an SSAS cube. When Stijn is not working with SQL Server, you can probably find him enjoying the local cuisine local beers or enjoying time with his wife and his dog.
LinkedIn: http://it.linkedin.com/pub/emanuele-zanchettin/18/921/34/
Emanuele Zanchettin, after studying in Computer Engineering, began his career with DB2 in the banking sector in 1998. After a few years and a change of scope of application, he encountered Access and Oracle with which he subsequently worked for several years in data access tuning and database design for new application features. Since 2007 he has also launched into the SQLServer world 2K, 2005, 2008* and 201* and more importantly, since 2011, into Azure SQL Database (SQL Azure). In order to have a greater capacity for assessment and familiarity with the various technologies that the market offers, he has deepened his knowledge to include MySQL and DocumentDB. Today Emanuele is mainly involved in IT projects and database management.
LinkedIn: http://dk.linkedin.com/in/regisbaccaro
Contact: http://theblobfarm.wordpress.com
Regis has more than 15 years of experience with SQL Server, SharePoint and .Net as an architect and developer. R#233;gis is a SQL Server MVP since 2014 and a frequent speaker at SQL conferences, a PASS Regional Mentor and the founder of SQL Saturday Denmark community event. He is also a certified industry trainer on the APS/PDW platform. In his freetime he is an avid runner, cook and farmer.
LinkedIn: https://www.linkedin.com/in/gabimuenster/
Gabi Münster is working as a Business Intelligence consultant and team lead and brings in her experience with SQL Server, data warehousing, relational and multidimensional database design and implementation, report and dashboard design, and Master Data Services, as well as project management into the community as a regional PASS Local Group lead. She is also passionate about motivating other women to start a technical career and therefore initiates and supports "Women in Technology" events. Apart from SQLSaturdays, she also spoke at SQLBits, SQL Nexus, Tuga IT, and 2015's and 2017's PASS Summit. In October 2017 Gabi received her first Data Platform MVP award.
Karol Papaj is a mentor with SolidQ. He works with SQL Server since version 6.5 as a consultant and trainer. Karol is a regular speaker at conferences in Slovakia and other European countries. For his work for community in Slovakia he was awarded by Microsoft as a Most Valued Professional. Karol is also teaching courses with focus on High Availability, Performance Tuning and T-SQL Programming.
LinkedIn: https://uk.linkedin.com/in/koprowskit
Contact: http://koprowskit.eu/geek/
Founder of Shadowland Consulting. In January 2015 decided to permanently leave Poland and discover new worlds. Actually settled in Hucknall (The Last home of The Byron Family). Community leader focused on SQL Server, SharePoint, security, collaboration and ITIL, DR, BCM, and SLA. Love licensing stuff. Microsoft Certified Trainer, freelancer, and consultant. Subject Matter Expert at Microsoft. Member of Microsoft Terminology Community, Friends of RedGate PLUS, PASS, ISSA, ACM and more. Since 1st July 2010 Data Platform MVP. Creator of SQLDAY Conference and Conference CEO 2009-2011. Co-Leader of PASS East Midlands Group. Speaker at group meetings and conferences. STEM Ambassador. Traveler. Coffee Addicted.
LinkedIn: https://hr.linkedin.com/in/vedrankesegic
Contact: http://blog.sqlxdetails.com
Vedran Kesegic is a Microsoft Certified Master for SQL Server, with Oracle background. With 14+ years enjoyed in exploration of SQL Server depths, developing smart algorithms, optimizing SQL Servers and SANs. He is the author of XDetails, Addin for SQL Management Studio that shows detailed info about indexes and other objects. He likes to hang out, share knowledge, and talk about more or less important things in life.
LinkedIn: https://www.linkedin.com/in/markus-ehrenmueller/
Contact: http://www.savorydata.com
Markus Ehrenmueller-Jensen, as the founder of Savory Data, has a long history of providing customer solutions in the areas of data engineering, data science, and Business Intelligence. He is a certified software engineer, a graduated business educator, and professor of Databases Project Management at HTL Leonding, and is certified as an MCSE MCT. He is a published author and writes articles for well-known journals. He co-founded PASS Austria and organizes SQLSaturdays in Austria. Markus is a founding member of Power BI Usergroup Austria. Since 2017 Markus was awarded as an Microsoft Data Platform MVP.
LinkedIn: https://uk.linkedin.com/in/alexwhittles
Contact: http://www.PurpleFrogSystems.com/blog
Alex is a Data Platform MVP, and is the owner and lead consultant at Purple Frog, a SQL Server Business Intelligence consultancy in the UK. He specializes in Data Warehouse modelling, ETL, Cubes, MDX amp; DAX. He#39;s a chartered engineer amp; has an MSc in Business Intelligence. He runs the Birmingham (UK) SQL Pass chapter amp; Azure user groups, and is on the SQLBits and SQL Relay committees in the UK. Alex is a regular speaker at global events including SQLBits, PASS Summit, SQL Relay, SQL Saturdays as well as numerous SQL Pass chapters around Europe
LinkedIn: http://it.linkedin.com/pub/gianluca-sartori/23/50b/a12
Contact: http://spaghettidba.com
Gianluca Sartori is a Data Platform MVP, independent consultant and performance tuning specialist. He has been working in the software industry since 1999 and has been working with SQL Server ever since. He also works as a SQL Server trainer and in his spare time he writes technical articles and participates the SQL Server forums. Gianluca enjoys presenting SQL Server topics at conferences in Europe and in Italy in particular. He is currently working as lead DBA at a famous Formula 1 team.
LinkedIn: https://uk.linkedin.com/in/robsewellsqldba
Contact: http://sqldbawithabeard.com
Rob was once a production SQL Server DBA, but is now generally found automating the Data Platform and providing training for clients. He has a passion for PowerShell, Data, and DevOps. He is an MVP, an officer for the DevOps PowerShell VG, and has spoken and volunteered at many PowerShell and Data events. He is a member of the committee that organises Data In Devon and the European PowerShell Conference. He is a proud supporter of the Data and PowerShell communities. He relishes sharing and learning and can be found doing both via Twitter @sqldbawithbeard and his blog sqldbawithabeard.com. He spends most of his time looking at a screen and loves to solve problems.
Contact: http://www.sommarskog.se
Erland Sommarskog is an independent consultant based in Stockholm, working with SQL Server since 1991. He was first awarded SQL Server MVP in 2001, and has been re-awarded every year since. His focus is on systems development with the SQL Server Database Engine and his passion is to help people to write better SQL Server applications.
LinkedIn: http://www.linkedin.com/profile/view?id=270775013amp;trk=tab_pro
Contact: http://www.sqlmaster.de
Uwe Ricken is working with IT systems since the 90’s. The primary passion for developments with Microsoft SQL Server expanded in 2007 with his engagement as a DBA for Deutsche Bank AG in Frankfurt am Main. After 6 years of operational experiences as a DBA and over 14 years as a developer of complex database models he achieved the “Microsoft Certified Master – SQL Server 2008” certification which “was” the highest technical certification. In the same year he earned his first award as a Data Platform MVP for his support to the Microsoft SQL Server community in Germany and Europe. Uwe Ricken is a speaker on many international conferences and events and preferred topics are “Database Internals”, “Indexing” and “Development”.
Contact: http://www.dbi-services.com/index.php/blog/dbi-bloggers/blogger/listings/sth
St#233;phane Haby has more than ten years of experience in Microsoft solutions. He is specialized in SQL Server technologies such as installation, migration, best practices, and performance analysis etc. He is MVP as well as MCSA and MCSE for SQL Server 2012 as well as ITIL Foundation V3 certified. He holds a Engineer diploma in industrial computing and automation from France. His branch-related experience covers Chemicals amp; Pharmaceuticals, Banking / Financial Services and many other industries.
LinkedIn: http://nl.linkedin.com/pub/enrico-van-de-laar/33/882/756/
Contact: https://www.enricovandelaar.com
Enrico has been working with data in all kinds of formats and sizes for over 15 years. He is a Data Advanced Analytics Consultant for Dataheroes where he helps organisations take their first steps in the world of Advanced Analytics. Enrico is a Data Platform MVP since 2014 and a frequent speaker on various data related events all over the world. He authored the book “Pro SQL Server Wait Statistics” and blogs about technologies like Microsoft SQL Server and Azure Machine Learning on his blog at www.enricovandelaar.com
LinkedIn: http://www.linkedin.com/in/dejandular
Dejan is an independent .NET and SQL developer. During his career he developed applications mainly in the area of Customer Management Systems and Supply Chain Management. In the last years he specialized as a back-end developer and data specialist. He is also a Microsoft Dynamics CRM expert. In his free time he is working with IoT and calls himself a DIY freak.
Contact: http://andrekamman.com
André Kamman is a DBA and SQL Server Solutions Architect for CloudDBA. He has done a lot of DBA work on 1000’s of servers where he discovered his love for Powershell, architecting SQL Server solutions, building and tuning ETL processes (with BIML). He also likes to work with MPP platforms APS and AzureDW. André is a Data Platform MVP, Dutch PASS Chapter Leader and organiser of SQL Saturday Holland.
LinkedIn: http://www.linkedin.com/in/wdurkin
Contact: http://williamdurkin.com
William Durkin is a DBA, Data Platform MVP, and Data Platform Architect for Data Masterminds (http://datamasterminds.io). He uses his decade of experience with SQL Server to help multinational corporations achieve their data management goals. Born in the UK and now based in Germany, William has worked as a Database Developer and DBA on projects ranging from single server installations, up to environments spanning 5 continents, using a range of high availability solutions. William is a regular speaker at conferences around the globe, organizes the popular event SQLGrillen (http://sqlgrillen.com).
LinkedIn: http://www.linkedin.com/tomaz.kastrun
Contact: http://www.tomaztsql.wordpress.com
Tomaž Kaštrun is BI developer and data analyst. His main focus are data mining, T-SQL development, programming and query optimization. He has been working with SQL server since version 2000. He is Microsoft Certified Professional, Microsoft MVP for data platform and Microsoft trainer.
Andrea Martorana Tusa is a Business Intelligence Team Manager at Würth Phoenix, the IT and consulting company of the Würth-Group. He is awarded as MVP in the Data Platform category Former BI Specialist at Widex, a Danish manufacturing company, and BI Developer in the IT department of an Italian banking group. 20+ years of experience working with data. He is focused on the entire BI stack: database development, data warehousing, data analysis, reporting, etc. Andrea is a usual speaker at many events: SQLSaturdays, conferences in Europe and PASS Summit, and for PASS Virtual Groups. Andrea is an author for sqlshack.com, sqlservercentral.com, and UGISS (User Group Italiano SQL Server).
LinkedIn: http://www.linkedin.com/pub/denis-reznik/3/502/234
Contact: http://reznik.uneta.com.ua
Denis Reznik is a Data Architect at Intapp, Inc. He has more than ten years experience in software development, database design, and performance tuning for cloud and on-premises solutions. In 2010 Denis became Microsoft Data Platform MVP and maintains MVP status today. Apart from his professional career, Denis actively participates in the development of the Ukrainian Data Community.
LinkedIn: https://dk.linkedin.com/in/bentnissenpedersen
Contact: http://www.biblog.eu
Bent is a Data Platform MVP and working as Senior Business Analytics Architect at Kapacity A/S, where he have been working with enterprise customers on solutions with SSAS Tabular, SQL Server and in general Azure Data Platform services for the last years. During the past years, he concentrated on working with architecturing, devOps, Automation, testing and simplifying development processes for the Azure Data Platform.
LinkedIn: https://bg.linkedin.com/in/dimitrovmiroslav
Contact: http://www.sqlshack.com/author/miroslav-dimitrov/
Miroslav Dimitrov is a Senior Big Data and Analytics Engineer at DXC Technology, regular speaker at various events and Microsoft Certified Trainer. He is MCSE: Data Platform for SQL Server and MCITP on SQL 2008. In the last years he had worked on great variety of customers' environments and involved in complex transitions and transformation projects as well as developing private and public cloud solutions. Miroslav is also leading database courses at Sofia University.
LinkedIn: https://www.linkedin.com/in/johanludvig/
Contact: http://brattas.org/sqlmusings
Johan Ludvig Brattås is a managing consultant at Capgemini, and a dedicated community guy. He has worked with MS SQL server since late 1999, mostly with BI in one form or another. The last 6 years, most of his work has been in Azure working on data platforms, Power BI and the last three years expanding to Azure IoT and Stream Analytics. Combining his passion for Microsoft SQL Server with his passion for sharing knowledge, he has spoken at various events in the SQL Community. This is also a way to give back to the community for all the things he has learned over the years. When not working, Johan Ludvig either spends his time with his kids, playing with new technology, or teaching coeliacs how to bake glutenfree food.
LinkedIn: https://hu.linkedin.com/in/janosberke
Contact: http://www.iamberke.com
I am an IT professional with over a decade of technical and management experience. I am using SQL Server since version 6.5. Currently I am a consultant and helping companies in SQL Server design, migrations, operation and developments as well as providing customized training .
LinkedIn: https://uk.linkedin.com/in/robsewellsqldba
Contact: http://sqldbawithabeard.com
Rob was once a production SQL Server DBA, but is now generally found automating the Data Platform and providing training for clients. He has a passion for PowerShell, Data, and DevOps. He is an MVP, an officer for the DevOps PowerShell VG, and has spoken and volunteered at many PowerShell and Data events. He is a member of the committee that organises Data In Devon and the European PowerShell Conference. He is a proud supporter of the Data and PowerShell communities. He relishes sharing and learning and can be found doing both via Twitter @sqldbawithbeard and his blog sqldbawithabeard.com. He spends most of his time looking at a screen and loves to solve problems.
LinkedIn: https://uk.linkedin.com/in/prathy
Contact: http://prathy.com/
Prathy Kamasani is a Microsoft Valuable Professional working as an independent MSBI contractor based in London. She specialises in Power BI, SSIS, SSAS and SSRS. Prathy organisers London Power BI user group and co-organizes the London PASS Local Group. She is a regular speaker at events like SQLSaturday and local user groups.
The following is a list of sponsors that helped fund the event.


Event Location:
Universidade Autónoma de Lisboa
Rua de Santa Marta, nº 56
Lisbon, , Portugal
Abstract:
Power View is Microsoft's Data Visualisation tool. Where does it get it right, and where does it get it wrong? Using demos, we learn about Power View whilst assessing it based on current cognitive research around Data Visualisation principles by luminaries such as Stephen Few, Tufte and others. We will: - investigate Power View based on current cognitive research around Data Visualisation principles - look at the features of Power View - look at where Power View is supplemented by other parts of the Microsoft Business Intelligence stack Come to this session if you really want to think about the best ways of presenting data to your Business Intelligence data consumers, and see how to apply these principles in Power View.
Speaker(s): Jen Stirrup,
Abstract:
The complete title is: Open your mind to new Transact-SQL features in SQL Server 2012: OVER clause and Running and Sliding Aggregates The session will have two speakers (Eladio and Enrique Catalá<EMAIL_ADDRESS>SQL Server 2012 is closer than ever to fully implement the ANSI Windowing functions. In this session we will get deeply into the OVER clause; you will understand how to use the OVER clause in SQL Server 2008, and what are the improvements in SQL Server 2012. The session will cover practical examples with running and sliding aggregates, running totals, and the new analytical functions; The attendee will learn by practical examples how to dramatically improve the performance in his/her Transact-SQL queries.
Speaker(s): Eladio Rincon,
Abstract:
In 1982 the Dragon 32 entered the home computer market but unfortunately there was one small problem ...lower-case letters were almost impossible to access. Two years later, Dragon was no more. In 2008 MS released Server Core providing a fast streamlined edition of Windows with minimal GUI support. As of SQL 2012, installation to W2008R2 Core is now a supported option and means the biggest administrative change to a DBA since the release of SQL 2005 ...or will it? We will demonstrate OS configuration through Cmd line utils and PowerShell, SQL installation, Remote Admin and Clustering on Core. Will the lack of GUI support send Server Core in the same direction as the Dragon 32 or spell a bright new future for SQL Windows Server computin
Speaker(s): Mark Broadbent,
Abstract:
Do you want to improve your DWH query performance by 100 times by just adding one additional index? Not possible? Wrong! SQL Server 2012 provides you the new Columnstore Index which changes the paradigm for DWH queries completely. In this session we will look at the details of the new Columnstore Index, when it makes sense, when not, and what are the current restrictions of it. We will look into Row Store vs. Column Store, how to change data in tables that have a Columnstore Index defined, we talk about Segment Elimination, Batch Mode Execution, about Query Memory Grants, and finally about how you can make sure that SQL Server will use your Columnstore Index as effective as possible.
Speaker(s): Klaus Aschenbrenner,
Abstract:
In this presentation it will be clarified when and why parameter sniffing is a problem in SQL Server stored procedures. Of course we will offer several solutions for this problem and discuss which solution is applicable in which scenario.
Speaker(s): Miloš Radivojević,
Abstract:
There are several enhancements regarding security and auditing in SQL Server 2012. This session will cover some of them in the following (new) topics: - Default Schema for groups; - User-defined Server Roles; - Contained Databases: - Authentication without Logins; - SQL Users with passwords. anf if time permits: - Sql server audit enhancements: T-SQL Stack Information; User-defined audit event
Speaker(s): Etienne Lopes,
Abstract:
The three main topics of this session are: 1. Perform a brief introduction what Extended Events are and the basic concepts/objects behind this feature. 2. Talk about the what's new in SQL Server 2012 and show the new UI and how to use it 3. Present some tipical scenarios where Xevents can help on the troubleshooting of different kind of issues
Speaker(s): Luís Canastreiro,
Abstract:
This session will focus on more agile patterns techniques applied to the usual Microsoft BI stack. Main topics will be: rapid feedback cycles, test automation, flexible integration processes, analysis services flexible models and some others.
Speaker(s): Rui Quintino,
Abstract:
This session will explain the basic principles of SQL Server's data compression feature, how it functions, what options you can choose, and how it performs under differing workloads. We will look at how to integrate data compression into your current schema designs and the challenges you will likely encounter, examining the all-important performance aspects and the configuration changes to consider along with SKU editions requirements. I present this session @ Red Gate SQL-in-the-City Conference (London - July 2011) will also be presenting this @ SQLSaturday #105 Dublin event
Speaker(s): Neil Hambly,
Abstract:
There are lots of DQ issues we are going to talk about some of the common issues and later see how we can handle them. Issues such as 1. Misspelled names 2. Misspelled addresses – addresses which do not exist, e-mail addresses 3. Duplication Pretty much everyone suffers from these problems so come to this session to learn a new way to deal with them in SQL Server 2012 Data Quality Services
Speaker(s): Allan Mitchell,
Abstract:
What does Azure offer to Business Intelligence developers, and to those who use Business Intelligence every day? Come to this session to see how you can transfer your skills in SSIS and SSRS to add Azure to your existing Business Intelligence skill set. We will look at: - the differences in developing SSIS packages that use Azure as a source and as a target. - developing SSRS reports deployed to Azure. - mobile devices and Azure such as tablets Come and see the similarities and differences in using Azure SQL Business Intelligence, and learn to avoid some of the 'gotcha!' pitfalls.
Speaker(s): Jen Stirrup,
Abstract:
This demo driven session will show some of the most outstanding from SQL 2008R2 to 2012 in terms of differences in information output by SHOWPLAN, which may prove helpfull when you are troubleshooting or performing query tuning.
Speaker(s): Pedro Lopes,
Abstract:
So you know all about heaps, clustered indexes, and nonclustered indexes, but are still not satisfied? Then this is just the session for you!I will explore advanced features such as included columns, indexed views, and filtered indexes. You will find out how they are stored on disk, and how SQL Server uses them to satisfy queries, But I will also show the dangers of using too many indexes, and the tools SQL Server itself hands you to help identify just the right subset of indexes.
Speaker(s): Hugo Kornelis,
Abstract:
News of increasingly Bigger and Bigger data are coming from different countries and cities around the world. Lisbon is not an exception. Our biggest fears have come true. Big Data is here to stay. We are calling for a secret meeting to everyone: DBAs, Network admins, developers, EVERYONE! . We need you in this war!. Join us in a session that will describe the strategy , the tactics and the plans to deal with this threat to the very existence of your servers: Operation HADOOP!
Speaker(s): Alejandro Leguizamo,
Abstract:
In this session will be shown a first look to the new SSIS 2012 version. This new version has lot of enhancements and new features. New environment, new tasks, new approaches to deal with packages, and several other stuff that I evaluated during last months of writing a SSIS book for the Packtpub publisher.
Speaker(s): Pedro Perfeito,
Abstract:
This session describes how SQL Server internally stores table data. Although you can use SQL without understanding the internals of data storage, a deep knowledge of how data is stored will improve the development of efficient applications. This session explores the basic of metadata that keeps track of data and index storage information.
Speaker(s): Bruno Basto,
Abstract:
SQL Server is an amazing tool, which has all imaginable components for Creating, Editing, Moving, Analizing and Reporting data. The most important and the most commonly used of them all is the T-SQL, the Transact-SQL implementation by Microsoft. This session will focus on some of the new programability features that will be available in SQL Server 2012, that will make lifes of the developers much easier, like SEQUENCE object, OFFSET functionality, new conversion functions (TRY_CONVERT), new logical and conversion functions like IIF and CONCAT.
Speaker(s): Niko Neugebauer,
Abstract:
The MERGE statement was introduced in SQL Server 2008, and it offers a tremendous value. Most people do know the basic function of MERGE: synchronising imported data with base data by inserting new rows and updating existing rows, all in one go. But MERGE can do much, much more. In this demo-rich session, Hugo Kornelis will present the full syntax of the MERGE statement, explain all the options, and show the possibilities. He will also demonstrate that the introduction of MERGE might have made your existing triggers incorrect, and how to fix the problems.
Speaker(s): Hugo Kornelis,
Abstract:
SQL Server is the guy being around for quite some time and SQL Azure is the new kid on the block. What would a fight between the two look like? What would each of them bring to the arena? In this session will get a look at the resources that SQL Server, as well as SQL Azure, would get to the fight arena and also see how a team-up between them would leverage a set of scenarios.
Speaker(s): Virgílio Esteves,
Abstract:
The session provides an overview of AlwaysOn and introduces the key new features and capabilities that will help businesses achieve the high availability SLA for mission critical applications.
Speaker(s): João Loureiro,
Abstract:
Want to know what's new in SQL Server 2010 BI stack?Come and see what cool features you can take advantage of SQL Server with SharePoint Server 2010
Speaker(s): Alexandre Mendeiros,
Abstract:
You have seen Power View, and you have seen the new SSAS BISM Tabular models in SQL Server 2012. But are you really a master of the unknown? Come to this session, and join the ranks of those who leave the darkness, and uncover the integration between Power View and BISM Models. Weapons, demos, and techniques will enlighten your way.
Speaker(s): Alejandro Leguizamo,
Abstract:
In this session we will travel through the two SQL Server 2012 Analysis Services modes: Tabular and Multidimensional. We will focus on the advantages and business scenarios of the new Tabular mode and we will discuss real-life situations in which we should apply tabular or dimensional approaches, focusing on the main differences and features in each mode.This session will be co-presented with Andre Santana.
Speaker(s): Andro Moreira,
This is a list of speakers from the XML Guidebook records. The details and URLs were valid at the time of the event.
LinkedIn: http://uk.linkedin.com/in/neilhambly
Contact: http://dataidol.com/NeilHambly
Neil Hambly is a SQL Server consultant, founder and consultant at Datamovements, a Gold Microsoft Data Analytics Consulting company. Neil has 20+ years in a variety of SQL Server roles and is an MCT. He is a regular presenter (200+ events) at user groups, and PASS events, including PASS Summit and SQLSaturday, and many UK events (SQLBits, SQLRelay). Neil is the leader of PASS London (UK), a Professional Development VC, a Melissa Data MVP, and a SQL Cruise Technical Lead, who loves Guinness, whisky, and dancing.
LinkedIn: https://pt.linkedin.com/in/rquintino
Contact: http://rquintino.wordpress.com
Senior Architect amp; Data Fellow at DevScope. Rui works with Microsoft data related technologies since 1998 and currently leads Data Research amp; Advanced Analytics team at DevScope. Beyond data techonologies, Rui also has strong background in web, mobile amp; security and he likes to mix all of these to get the most out of Microsoft#39;s Data Platform and make the world a better place through the use of data. Despite his assumed introversion he loves to learn amp; share his findings with developers, data lovers and microsoft platform communities on local amp; online events.
Contact: http://www.culto-de-bi.pt
Pedro was born in 1977 in Portugal and currently works as BI Senior Consultant and Invited Lecture in several BI master degrees. He’s also founder and owner of a BI company named “Culto de BI”. He received in 2010, 2011 and 2012 the Microsoft MVP award for all the dedication and contribution helping theoretical and practical issues in the various BI communities. He#39;s also co-author and editor of several books from Packtpub and Syncfusion
Contact: http://blogs.msdn.com/blogdoezequiel
I’m actually a Senior SQL PFE working in the EMEA region, based in Lisbon (Portugal). I’m working with TIER 1 customers doing SQLRaaS and Performance/Design reviews. Nowadays, I’m the Local SQL PFE Tech lead in terms of the Portuguese SQL PFE team. So, I have the responsibility to manage the list of activities and trainings that must be developed and delivered by the local PFEs here in Portugal and I’m also starting to develop some IP related with SQL for the PFE community.
LinkedIn: https://uk.linkedin.com/in/retracement
Contact: http://tenbulls.co.uk
Mark Broadbent is a Microsoft Certified Master in SQL Server, Chapter Leader, Virtual Chapter Leader, SQLSaturday Cambridge and SharePoint Saturday Cambridge founder (the UKs first SQLSaturday) and awarded Microsoft#39;s Community Contributor award in 2011 and the PASS Outstanding Volunteer award in 2012. Mark is a regular speaker at both International and National events and is an expert in HADR solutions, SQL Server migration, and SQL Server Concurrency control.
Contact: http://www.SQLpassion.at/blog
Klaus Aschenbrenner provides independent SQL Server consulting services across the United States and Europe, focusing on the .NET Framework and SQL Server 2005 and 2008. In 2004 and 2005, he received the Microsoft MVP award for his support of the .NET community. Klaus has also written a book, “Pro SQL Server 2008 Service Broker,” published by Apress (2008).
Contact: https://onlysearch.wordpress.com/
Allan Mitchell is a SQL Server MVP and runs elastio, a small consultancy helping customers to make informed decisions about their data storage and integration. His focus is on enterprise search as well as real-time data integration.
Contact: http://www.jenstirrup.com
Jen is a database strategist, small business owner, SQLFamily and community advocate, public speaker and blogger. Jen was elected Director-At-Large (elect) for the Professional Association of SQL Server, responsible for the EMEA regions plus the Virtual Chapter portfolio. Jen is passionate about all things data and business intelligence, helping leaders derive value from their Microsoft SQL Server, SharePoint and Office365 investments.
Joined Microsoft in January of 2011 as a PFE for Customer support
Contact: http://sqlblog.com/blogs/hugo_kornelis/default.aspx
Hugo is co-founder and RD lead of perFact BV, a Dutch company that improves analysis methods and develops computer-aided tools to generate completely functional applications from the analysis deliverable. The platform for this development is SQL Server. In his spare time, Hugo likes to share and enhance his knowledge of SQL Server by frequenting newsgroups and forums, reading and writing books and blogs, and attending and speaking at conferences. Hugo is also a SQL Server MVP since 2006.
Contact: https://sqland.wordpress.com
Etienne Lopes currently works as a SQL Server DBA at Celfinet. Working with SQL Server since 1997, member of sqlport community (www.sqlport.com), regular speaker since 2012 at SQL Server events such as: SQLSaturday and 24 Hours of Pass. Blog: http://sqland.wordpress.com/
Contact: http://www.solidq.com
Alejandro has been working with SQL Server since 6.5, and with MS Access since Office 97. His core area is Datawarehousing and Business Intelligence, plus ETL. He Has been invited to participate as speaker, trainer , consultant and/or expert to Colombia, USA, Puerto Rico, Perú, Ecuador, Spain, Venezuela, Denmark, Chile, Argentina, Sri Lanka and others, in events like Launch of SQL Server, Developer Days, TechEd and PASS. Alejandro is also awarded with the Microsoft MVP since 2004
Contact: http://blogs.msdn.com/b/joaol/
Working at Microsoft since 2006 as SQL Server Support Escalation Engineer. Engaged on TAP programs for SQL Server beta programs. Providing SQL Server Third tier support and working in close relationship with SQL Server Product Group. Involved on SQL Server KB articles, hotfixes and Service Pack developments.
Contact: http://sqlblog.com/blogs/hugo_kornelis/default.aspx
Hugo is co-founder and RD lead of perFact BV, a Dutch company that improves analysis methods and develops computer-aided tools to generate completely functional applications from the analysis deliverable. The platform for this development is SQL Server. In his spare time, Hugo likes to share and enhance his knowledge of SQL Server by frequenting newsgroups and forums, reading and writing books and blogs, and attending and speaking at conferences. Hugo is also a SQL Server MVP since 2006.
Contact: http://www.jenstirrup.com
Jen is a database strategist, small business owner, SQLFamily and community advocate, public speaker and blogger. Jen was elected Director-At-Large (elect) for the Professional Association of SQL Server, responsible for the EMEA regions plus the Virtual Chapter portfolio. Jen is passionate about all things data and business intelligence, helping leaders derive value from their Microsoft SQL Server, SharePoint and Office365 investments.
Contact: http://pontonetpt.org/blogs/raposo
Virgílio Esteves is a consultant who loves cloud computing and everything else it empowers, having been awarded the Windows Azure MVP title by Microsoft. Speaker in some Microsoft Portugal events, he's also a regular speaker at some user groups meetings (NetPonto, AzurePT, SQLPort) and founder of Coimbra branch of NetPonto User Group.
Contact: http://www.bi4all.pt
Andro Moreira é licenciado em Informática e Gestão de Empresas pelo ISCTE, foi Gestor de Tecnologia Microsoft na Novabase Suporte à Decisão, tendo assumido posteriormente funções idênticas na Sinfic SA. Em 2004 fundou a BI4ALL Consultores de Gestão Lda, Empresa de Consultoria de Gestão focalizada nas áreas de Enterprise Planning, Performance Management, Risk Management e Business Intelligence, da qual é Partner e responsável pela área de serviços de Consultoria.
Bruno Basto, senior consultant #160;and senior trainer #160;at Rumos. SQL certified since 6.5 version (1998). Work with MSFT tools and infrastructure since 1992 in the financial, banking and insurance areas. Responsible for several training projects for software solutions with MSFT infrastructure. Speaker at several MSFT and SQLPass events.
LinkedIn: http://pt.linkedin.com/in/pedroazevedolopes/
Contact: http://aka.ms/sqlserverteam
Pedro Lopes is a Principal Program Manager in the Microsoft Azure SQL Server team, based in Redmond, WA, USA. He is currently responsible for Program Management of database engine features for SQL Server and Azure SQL, with special focus on the Relational Engine (Query Processor, Programmability, Performance). Regular speaker at conferences and user groups events, blogs about all things SQL at http://aka.ms/sqlserverteam, authored the book “Learn T-SQL Querying” (https://aka.ms/LearnTSQLQuerying), and tools in the TigerToolbox on GitHub: AdaptiveIndexDefrag (http://aka.ms/AID), BPCheck for SQL Best Practices and Performance checks (http://aka.ms/BPCheck), or usp_WhatsUp to get an instant view of perf issues (https://aka.ms/uspwhatsup).
LinkedIn: http://pt.linkedin.com/in/webcaravela/
Contact: http://www.nikoport.com
Niko Neugebauer is a Data Platform Consultant. A SQL Server MVP with over 20 years of experience in IT, he is passionate about the Microsoft Data Platform and community. Founder of the Portuguese SQL Server User Group and the main organizer of the first SQLSaturday event outside of North America (#78 Portugal), Niko speaks regularly at events such as PASS Summit, SQLRally, SQLBits, and SQLSaturday events around the world. Niko loves sharing information and knowledge and has authored over 130 blog posts on Columnstore Indexes, and regularly contributes to the open-sourced CISL library focused on Columnstore Indexes.
LinkedIn: http://at.linkedin.com/pub/milos-radivojevic/1b/7bb/290/de
Contact: https://milossql.wordpress.com/
Miloš Radivojević is a database consultant located in Vienna, Austria. He is Data Platform MVP and specializes in SQL Server for application developers and performance and query tuning. Currently, he works as a principal database consultant in bwin GVC (the largest regulated online gaming company in the world). He is co-founder of the SQL Pass Austria and speaks regularly at local and international database conferences. He is also co-author of the books SQL Server 2016 Developer's Guide, SQL Server 2017 Developer's Guide and Mastering SQL Server 2017.
Contact: http://www.solidq.com/blogs
Eladio es MVP en SQL Server desde el a#241;o 2002; es director de bases de datos relacionales de SolidQ en Espa#241;a donde dirige proyectos para clientes de SolidQ en todo el mundo. Ponente habitual en conferencias Microsoft MSDN/TechNet, SQLPASS o grupos de usuarios; participa con sus compa#241;eros en los blogs tecnol#243;gicos de su empresa http://blogs.solidq.com/bigdata-hadoop , http://blogs.solidq.com/elrincondeldba y http://blogs.solidq.com/bigdata-hadoop
Contact: http://www.solidq.com
Alejandro has been working with SQL Server since 6.5, and with MS Access since Office 97. His core area is Datawarehousing and Business Intelligence, plus ETL. He Has been invited to participate as speaker, trainer , consultant and/or expert to Colombia, USA, Puerto Rico, Perú, Ecuador, Spain, Venezuela, Denmark, Chile, Argentina, Sri Lanka and others, in events like Launch of SQL Server, Developer Days, TechEd and PASS. Alejandro is also awarded with the Microsoft MVP since 2004
The following is a list of sponsors that helped fund the event.
Universidade Autónoma de Lisboa
SQLServer 2012 SharePoint Connections


CURRENT TIER
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{{ else }}
{{/* Goal: round up to the nearest even number if odd. Hugo does integer division by default so we use this to accomplish the goal
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{{ range $bisIndex, $bisName := .bis }}
{{ $bisItemClass := "bis-item" }}
{{/* {{ if eq $bisIndex $bisLen }}
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{{ end }} */}}
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{{ end }}


|
Standard Reference Data Program |
Data Gateway |
Chemistry WebBook |
Go To: Top, Constants of diatomic molecules, References, Notes / Error Report
Data compilation copyright by the U.S. Secretary of Commerce on behalf of the U.S.A. All rights reserved.
Data compiled as indicated in comments:
LLK - S.G. Lias, R.D. Levin, and S.A. Kafafi
RDSH - H.M. Rosenstock, K. Draxl, B.W. Steiner, and J.T. Herron
View reactions leading to FGe+ (ion structure unspecified)
| IE (eV) | Method | Reference | Comment |
|---|---|---|---|
| 7.46 | EVAL | Huber and Herzberg, 1979 | LLK |
| ≤9.1 ± 0.2 | EI | Zmbov, Hastie, et al., 1968 | RDSH |
| ~7.28 | S | Johns and Barrow, 1958 | RDSH |
| Ion | AE (eV) | Other Products | Method | Reference | Comment |
|---|---|---|---|---|---|
| F+ | 33.0 ± 0.3 | ? | EI | Harland, Cradock, et al., 1973 | LLK |
Go To: Top, Gas phase ion energetics data, References, Notes / Error Report
Data compilation copyright by the U.S. Secretary of Commerce on behalf of the U.S.A. All rights reserved.
Data compiled by: K.P. Huber and G. Herzberg
| State | Te | ωe | ωexe | ωeye | Be | αe | γe | De | βe | re | Trans. | ν00 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| G 2Δr(4dδ) | 49412.89 1 | 710.37 Z | 2.82 H | 0.38408 | 0.00261 | [4.43E-7] 2 | 1.70411 | G → X V | 48523.4 HQ | |||
| Barrow, Butler, et al., 1959; missing citation | ||||||||||||
| G → X V | 49415.6 HQ | |||||||||||
| Barrow, Butler, et al., 1959; missing citation | ||||||||||||
| D 2Σ+(6sσ) | 48581.26 | 833.12 Z 3 | 6.52 H | 0.39972 3 | 0.00214 | [3.73E-7] 4 | 1.6704 | D → A V | 25473.30 Z | |||
| missing citation | ||||||||||||
| D → X V | 47726.6 HQ | |||||||||||
| missing citation | ||||||||||||
| D → X V | 48662.6 HQ | |||||||||||
| missing citation | ||||||||||||
| State | Te | ωe | ωexe | ωeye | Be | αe | γe | De | βe | re | Trans. | ν00 |
| D' 2p(4dπ) | 47920.73 5 | 803.96 6 Z | 3.38 H | 0.40068 6 7 | 0.00259 | 3.62E-7 6 | 1.6684 | D' → A V | 24798.98 6 Z | |||
| missing citation | ||||||||||||
| D' → X 8 V | 47043.0 H | |||||||||||
| missing citation | ||||||||||||
| D' → X 8 V | 47976.3 H | |||||||||||
| missing citation | ||||||||||||
| E 2Σ+ (5pσ) | 46645.41 | 760.08 Z | 2.967 | 0.39845 9 | 0.00290 | 4.33E-7 | 1.67310 | E → B 10 | 11616.26 Z | |||
| missing citation | ||||||||||||
| State | Te | ωe | ωexe | ωeye | Be | αe | γe | De | βe | re | Trans. | ν00 |
| C 2Δ | 43977.49 11 | [684.00] Z | 9.31 | 0.38835 | 0.00421 | [4.97E-7] 12 | 1.69472 | C → X V | 43059.27 13 Z | |||
| Barrow, Butler, et al., 1959; Uzikov and Kuzyakov, 1969; missing citation | ||||||||||||
| C → X V | 43994.43 13 Z | |||||||||||
| Barrow, Butler, et al., 1959; Uzikov and Kuzyakov, 1969; missing citation | ||||||||||||
| C' 2Π (5pπ) | 43369.61 14 | 796.88 Z | 3.415 | 0.39957 15 | 0.00258 | 4.13E-7 | 1.67075 | C' → A 16 R | 20244.31 | |||
| Martin and Merer, 1974 | ||||||||||||
| C' → X 17 V | 42547.8 H | |||||||||||
| Uzikov and Kuzyakov, 1969; missing citation | ||||||||||||
| C' → X 17 V | 43379.9 H | |||||||||||
| Uzikov and Kuzyakov, 1969; missing citation | ||||||||||||
| State | Te | ωe | ωexe | ωeye | Be | αe | γe | De | βe | re | Trans. | ν00 |
| a 4Σ- | 35194.68 18 | [628.31] Z | 6.66 19 | 0.36676 18 | 0.00369 | [4.94E-7] 20 | 1.7439 | a → X | 35181.77 Z | |||
| missing citation | ||||||||||||
| B 2Σ+(5sσ) | 35010.85 | 796.99 Z | 3.613 21 | 0.0124 | 0.39440 22 | 0.00255 | 3.88E-7 | 1.68167 | B → A V | 11885.56 Z | ||
| Barrow, Butler, et al., 1959 | ||||||||||||
| B → X 23 V | 34141.23 Z | |||||||||||
| missing citation; missing citation | ||||||||||||
| B → X 23 V | 35076.39 Z | |||||||||||
| missing citation; missing citation | ||||||||||||
| State | Te | ωe | ωexe | ωeye | Be | αe | γe | De | βe | re | Trans. | ν00 |
| A 2Σ+ | 23316.65 | 413.03 Z | 1.124 24 | 0.32039 25 | 0.00307 26 | 7.78E-7 27 | 1.86582 | A → X 23 R | 22255.67 Z | |||
| 413.03 Z | 1.124 24 | 0.32039 25 | 0.00307 26 | 7.78E-7 27 | 1.86582 | 23190.83 Z | ||||||
| X 2Π3/2 | 934.33 | 667.33 Z | 3.150 28 | 0.36660 | 0.002675 29 | 4.50 | 1.7452 | |||||
| X 2Π1/2 | 0 | 665.67 Z | 3.150 28 | 0.36578 30 | 0.002675 29 | 4.47 | 1.7452 |
| 1 | A0 = +22.20, A1 = +21.5: small J dependence. |
| 2 | D1 = 4.52E-7. |
| 3 | Rotational analysis of v=2, and tentative results for v=0. |
| 4 | D2 = 3.33E-7. |
| 5 | A2 = (+)4.23, A3 = (+)4.35. |
| 6 | Extrapolation from the rotationally analyzed levels v'=2 and 3; βe = +0.21E-7. |
| 7 | Large Λ-type doubling, also spin-rotation interaction; see Martin and Merer, 1974. |
| 8 | The 0-1 and 0-2 bands were previously considered by Barrow, Butler, et al., 1959 as 0-0 bands of their transitions F-X and E-X, respectively. |
| 9 | Spin-doubling constant γ(v=0) = -0.0358. |
| 10 | Δv=0 sequence of slightly V shaded bands, provisionally assigned in Barrow, Butler, et al., 1959 to a E(2Π)- B(2Σ) transition. |
| 11 | A0 = 13.88, A1 = 14.25. |
| 12 | D1 = 5.15E-7 |
| 13 | Referring in the upper state to the zero point of the Hill-Van Vleck expression. |
| 14 | A0 = 105.63, A1 = 105.88, A2 = 105.96; small J dependence. |
| 15 | Λ-type doubling in 2Π1/2, Δvfe(v=0)=-0.0373(J+1/2). |
| 16 | Extremely weak system consisting of a long 0-v" progression; not analyzed. |
| 17 | The 3-1 and 1-0 bands of C' 2Π1/2 - X 2Π1/2 correspond to the 0-0 subbands of a system D-X proposed earlier by Barrow, Butler, et al., 1959. The 2-1 band of the 3/2- 3/2 system was assigned by Barrow, Butler, et al., 1959 as 0-0 band of C'-X2Π1/2. |
| 18 | Spin-splitting constants for v=0: λ = +8.086, v=0 [4λ = T0(4Σ3/2) - T0(4Σ1/2), v=0]; γ1 ~ γ2 = 0.0119, v=0. See also 22. |
| 19 | Based on the interpretation of a perturbation in B 2Σ+, v=4. |
| 20 | D1 = 4.90E-7. |
| 21 | missing note |
| 22 | Spin=doubling constant γ(v=0) = +0.00100. Extensive perturbations between B 2Σ+, v=0, and, a 4Σ-, v=0. |
| 23 | Franck-Condon factors Singh, 1975. |
| 24 | missing note |
| 25 | Spin-doubling constant γ(v) = -[0.03662 + 0.000l4(v+1/2)]. |
| 26 | missing note |
| 27 | Dv= -0.22E-7(v+1/2) +... |
| 28 | missing note |
| 29 | missing note |
| 30 | Λ-type doubling, Δvfe = -[0.02184 + 0.000195 (v+1/2)](J+1/2). |
| 31 | Thermochemical value (mass-spectrom.) Ehlert and Margrave, 1964; see also Harland, Cradock, et al., 1973, 2. |
| 32 | Approximate limit of the ns (n = 5, 6, ...) Rydberg series Martin and Merer, 1974. Harland, Cradock, et al., 1973, 2 give 7.2 eV from electron impact mass spectrometry. |
Go To: Top, Gas phase ion energetics data, Constants of diatomic molecules, Notes / Error Report
Data compilation copyright by the U.S. Secretary of Commerce on behalf of the U.S.A. All rights reserved.
Huber and Herzberg, 1979
Huber, K.P.; Herzberg, G.,
Molecular Spectra and Molecular Structure. IV. Constants of Diatomic Molecules,, Van Nostrand Reinhold Co., 1979, ,1. [all data]
Zmbov, Hastie, et al., 1968
Zmbov, K.F.; Hastie, J.W.; Hauge, R.; Margrave, J.L.,
Formation of polymeric (GeF2)n in the vapor phase over GeF2,
Inorg. Chem., 1968, 7, 608. [all data]
Johns and Barrow, 1958
Johns, J.W.C.; Barrow, R.F.,
The band spectrum of silicon monofluoride, SiF,
Proc. Phys. Soc. (London), 1958, 71, 476. [all data]
Harland, Cradock, et al., 1973
Harland, P.W.; Cradock, S.; Thynne, J.C.J.,
Positive-and negative-ion formation due to the electron bombardment of germanium tetrafluoride,
Int. J. Mass Spectrom. Ion Phys., 1973, 10, 169. [all data]
Barrow, Butler, et al., 1959
Barrow, R.F.; Butler, D.; Johns, J.W.C.; Powell, J.L.,
Some observations on the spectra of the diatomic fluorides of silicon, germanium, tin, and lead,
Proc. Phys. Soc. London, 1959, 73, 317. [all data]
Uzikov and Kuzyakov, 1969
Uzikov, A.N.; Kuzyakov, Yu.Ya.,
Analysis of the vibrational structure of C-X2P and C'X2P-band systems and the energy of dissociation of GeF,
Moscow Univ. Chem. Bull. Engl. Transl., 1969, 24, 22, In original 30. [all data]
Martin and Merer, 1974
Martin, R.W.; Merer, A.J.,
Rotational structure in some higher excited states of the GeF molecule,
Can. J. Phys., 1974, 52, 1458. [all data]
Singh, 1975
Singh, J.,
Vibrational transition probabilities & r-centroids for diatomic fluorides of Si & Ge,
Indian J. Pure Appl. Phys., 1975, 13, 204. [all data]
Ehlert and Margrave, 1964
Ehlert, T.C.; Margrave, J.L.,
Mass-spectrometric studies at high temperatures. II. The dissociation energies of the monofluorides and difluorides of silicon and germanium,
J. Chem. Phys., 1964, 41, 1066. [all data]
Harland, Cradock, et al., 1973, 2
Harland, P.W.; Cradock, S.; Thynne, J.C.J.,
Bond dissociation energies, ionisation potentials and electron affinities of some germanium fluoride species,
Inorg. Nucl. Chem. Lett., 1973, 9, 53. [all data]
Go To: Top, Gas phase ion energetics data, Constants of diatomic molecules, References
| AE | Appearance energy |
Copyright for NIST Standard Reference Data is governed by the Standard Reference Data Act.
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Standard Reference Data Program |
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| Home | Libraries | People | FAQ | More |
The intent of this library is to implement the unordered containers in the
draft standard, so the interface was fixed. But there are still some implementation
decisions to make. The priorities are conformance to the standard and portability.
本库的目的是实现标准草案中的无序容器，所以库的接口是固定的。不过还是做一些实现上的决定。首先是与标准的一致性和可移植性。
The wikipedia article
on hash tables has a good summary of the implementation issues for
hash tables in general.
wikipedia article
on hash tables 上有关于通常的散列表实现问题的一个很好的总结。
By specifying an interface for accessing the buckets of the container the standard
pretty much requires that the hash table uses chained addressing.
通过指定一个接口用于访问容器中的桶，标准几乎就是要求散列表使用链接法。
It would be conceivable to write a hash table that uses another method. For
example, it could use open addressing, and use the lookup chain to act as a
bucket but there are a some serious problems with this:
可以想象编写使用其它方法的散列表。例如，它可以使用开放法，并使用查找链来作为桶，不过这样会存在一些严重的问题：
So chained addressing is used.
所以我们使用了链接法。
For containers with unique keys I store the buckets in a single-linked list.
There are other possible data structures (such as a double-linked list) that
allow for some operations to be faster (such as erasing and iteration) but
the possible gain seems small compared to the extra memory needed. The most
commonly used operations (insertion and lookup) would not be improved at all.
对于唯一键的容器，我将桶保存在一个单链表中。有一些其它的数据结构(如双链表)可以使得某些操作更快(如删除和迭代)，不过可能的收益似乎比所需的额外内存开销要小。毕竟最常用的操作(插入和查找)并没有得到提升。
But for containers with equivalent keys a single-linked list can degrade badly
when a large number of elements with equivalent keys are inserted. I think
it's reasonable to assume that users who choose to use unordered_multiset
or unordered_multimap
do so
because they are likely to insert elements with equivalent keys. So I have
used an alternative data structure that doesn't degrade, at the expense of
an extra pointer per node.
但对于非唯一键的容器，使用单链表会在插入大量相同键值的元素时性能严重退化。我认为，假设选择使用 unordered_multiset
或 unordered_multimap
的用户将很可能会插入相等键值的元素，这是很合理的。所以我对此种容器使用了另一种性能不会退化的数据结构，其代价是每个节点多用一个指针。
This works by adding storing a circular linked list for each group of equivalent
nodes in reverse order. This allows quick navigation to the end of a group
(since the first element points to the last) and can be quickly updated when
elements are inserted or erased. The main disadvantage of this approach is
some hairy code for erasing elements.
这是通过为每组相等的节点在相反顺序上增加一个循环的链表来实现的。这样可以快速地定位到该组节点的末尾(因为第一个元素指向最后一个)，并且在插入或删除元素时也可以快速更新。这种方法的主要缺点是删除元素的代码有点不太好看。
There are two popular methods for choosing the number of buckets in a hash
table. One is to have a prime number of buckets, another is to use a power
of 2.
有两种常见的方法来选择在散列表中的桶的数量。一种是让桶的数量为素数，另一种是使用2的幂数。
Using a prime number of buckets, and choosing a bucket by using the modulus
of the hash function's result will usually give a good result. The downside
is that the required modulus operation is fairly expensive.
使用素数个桶，并且通过对散列函数的结果取模来选择桶通常有不错的结果。缺点是模操作的代价有点高。
Using a power of 2 allows for much quicker selection of the bucket to use,
but at the expense of loosing the upper bits of the hash value. For some specially
designed hash functions it is possible to do this and still get a good result
but as the containers can take arbitrary hash functions this can't be relied
on.
使用2的幂则可以更快地选择要用的桶，但是它的代价是损失了散列值的高位信息。对于一些特殊设计的散列函数，这样是可以的，并且结果也还不错，但是考虑到容器可能使用任意的散列函数，所以不能依赖于它。
To avoid this a transformation could be applied to the hash function, for an
example see Thomas
Wang's article on integer hash functions. Unfortunately, a transformation
like Wang's requires knowledge of the number of bits in the hash value, so
it isn't portable enough. This leaves more expensive methods, such as Knuth's
Multiplicative Method (mentioned in Wang's article). These don't tend to work
as well as taking the modulus of a prime, and the extra computation required
might negate efficiency advantage of power of 2 hash tables.
为了避免这个问题，可以对散列函数进行一个转换，有关例子请见 Thomas
Wang 关于整数散列函数的论文。
不幸，象 Wang 所给出的转换方法要求知道散列值中的二进制位数，所以它的可移植性不够。还有一些更昂贵的方法，如 Knuth 的相乘法(在
Wang 的论文中提及)。它们和对素数取模一样不太好用，而且所需的额外计算可能还会消除2的幂的散列表所带来的性能优势。
So, this implementation uses a prime number for the hash table size.
所以，我们实现使用素数作为散列表的大小。
operator==
and operator!=
are not included in the standard, but I've added them as I think they could
be useful and can be efficiently implemented. They are specified differently
to the standard associative containers, comparing keys using the equality predicate
rather than operator==
. operator==
和 operator!=
并没有包含在标准中，不过我把它们加了进来，因为我认为它们可能是有用的，也可以高效地实现。它们与标准关联式容器是非常不同的，对键值的比较是使用等同性谓词而不是 operator==
的。
It's also different to the proposal n2944. which uses the equality operators for the whole of value_type
. This
implementation just uses the key equality function for the key, and mapped_type
's equality
operator in unordered_map
and unordered_multimap
for
the mapped part of the element.
这与建议书 n2944 也不一样。在建议书中是对整个 value_type
使用相等性操作符。而本实现则只对键值使用相等性比较函数，在 unordered_map
和 unordered_multimap
中则对元素中的映射部分使用 mapped_type
的相等性操作符。
Also, in unordered_multimap
, the mapped values for a group
of elements with equivalent keys are only considered equal if they are in the
same order, in n2944 they just need to be a permutation of each other. Since the
order of elements with equal keys is now defined to be stable, it seems to me
that their order can be considered part of the container's value.
还有，在 unordered_multimap
中，带有相等键值的一组元素的映射值被认为是相等的，如果它们具有相同的顺序，在 n2944 中它们只需要互为排列即可。由于具有相等键值的元素顺序现在被定义为稳定的，所以对我来说，它们的顺序可以被认为是容器值的一部分。
Recent drafts have included an overhaul of the allocators, but this was
dependent on concepts which are no longer in the standard. n2946 attempts to respecify them without concepts. I'll try to
implement this (or an appropriate later version) in a future version of boost,
possibly changed a little to accomodate non-C++0x compilers.
最新的草案已经包含了对分配器的一个修改，但它是依赖于尚未进入标准的一些概念的。n2946 试图不使用概念来重新实现它们。在以后的boost版本中，我会尽力实现它们(或是它们的一个近似版本)，也可能有一点改变，以适应非C++0x标准的编译器。
It isn't clear how to swap containers when their allocators aren't equal.
This is Issue 431: Swapping containers with unequal allocators. This has
been resolved with the new allocator specification, so this should be fixed when
support is added.
如果交换具有不同分配器的容器，这一点还不清楚。这正是 Issue
431: 交换具有不同分配器的容器。通过新的分配器规范，这个问题已经被解决，所以当可以支持时就可以修正该问题。
It wan't specified if unordered_multiset
and unordered_multimap
preserve the order of elements with equivalent keys (i.e. if they're stable
under insert
and
erase
). Since n2691 it's been specified that they do and this implementation
follows that.
没有规定 unordered_multiset
和 unordered_multimap
是否要保持相等键值元素的顺序(即在 insert
和 erase
时它们是否稳定)。从 n2691 起已规定如此，本实现也遵守这一点。


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nips nips2001 nips2001-89 nips2001-89-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Stella X. Yu, Jianbo Shi
Abstract: With the optimization of pattern discrimination as a goal, graph partitioning approaches often lack the capability to integrate prior knowledge to guide grouping. In this paper, we consider priors from unitary generative models, partially labeled data and spatial attention. These priors are modelled as constraints in the solution space. By imposing uniformity condition on the constraints, we restrict the feasible space to one of smooth solutions. A subspace projection method is developed to solve this constrained eigenproblema We demonstrate that simple priors can greatly improve image segmentation results. 1
[1] A. Amir· and M. Lindenbaum. Quantitative analysis of grouping process. In European Conference on Computer Vision, pages 371-84, 1996.
[2] A. Blum and S. Chawla. Learning from labeled and unlabeled data using graph mincuts, 2001.
[3] Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. In International Conference on Computer Vision, 1999.
[4] Y. Gdalyahu, D. Weinshall, and M. Werman. A randpmized algorithm for pairwise clustering. ill Neural Information Processing Systems, pages 424-30, 1998.
[5] S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6):721-41, 1984.
[6] H. Ishikawa and D. Geiger. Segmentation by grouping junctions. In IEEE Conference on Computer Vision and Pattern Recognition, 1998.
[7] I. H. Jermyn and H. Ishikawa. Globally optimal regions and boundaries. In International Conference on Computer Vision, 1999.
[8] M. Kass, A. Witkin, and D. Terzopoulos. Snakes: Active contour models. International Journal of Computer Vision, pages 321-331, 1988.
[9] J. Malik, S. Belongie, T. Leung, and J. Shi. Contour and texture analysis for image segmentation. International Journal of Computer Vision, 2001.
[10] M. Meila and J. Shi. Learning segmentation with random walk. ill Neural Information Processing Systems, 2001.
[11] P. Perona and W. Freeman. A factorization approach to grouping. In European Conference on Computer Vision, pages 655-70, 1998.
[12] J. Puzicha, T. Hofmann, and J. Buhmann. Unsupervised texture segmentation in a deterministic annealing framework. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8):803-18, 1998.
[13] S. Roy and I. J. Cox. A maximum-flow formulation of then-camera stereo correspondence problem. In International Conference on Computer Vision, 1998.
[14] E. Sharon, A. Brandt, and R. Basri. Fast multiscale image segmentation. In IEEE Conference on Computer Vision and Pattern Recognition, pages 70-7, 2000.
[15] J. Shi and J. Malik. Normalized cuts and image segmentation. In IEEE Conference on Computer Vision and Pattern Recognition, pages 731-7, June 1997.


I think, therefore I am
a peak into my brain.
April 05, 2015
According to the Gregoric Thinking Style test, my thinking style is abstract random. Now, when I first realized this. I was bit alarmed. But I thought about it and figured, you know what. It makes sense. I do like to listen to others, I have a friendly demeanor, I absolutely love working backwards or jumping around when working, and sometimes I feel like I have to do multiple things at once (although now, I'm learning multi-tasking is a no-no).
According to Gregoric, Abstract Random Thinkers organize information through reflection, and thrive in unstructured, people-oriented environments. Which is why writing about experience in learning or life, helps solidify the event for me. It also helps organize my thoughts. To get rid of the unnecessary and gather and keep the valuable moments from the experience.
I think my biggest struggle with learning code, having a thinking style such as mine, has been not having the human aspect, while learning.  While I work well alone, it's nice to have a classmate there to discuss something that's confusing or difficult to grasp.


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A discovery of witches by Deborah E. Harkness (All souls trilogy)
About the book
Author: Harkness, Deborah E., 1965-
Title: A discovery of witches; Series: All souls trilogy
- A discovery of witches (DA41609, ISBN9780670022410), 2011, read by Imogen Church (579 pages ; 25 hours, 31 minutes)
- Shadow of the night (DA45810, ISBN9780670023486), 2012, read by Karina Fernandez (584 pages ; 25 hours, 22 minutes)
- The book of life (DA49922, ISBN9780670025596 ), 2014, read by Jennifer Ikeda (561 pages; 23 hours, 55 minutes)
Publication: New York : Viking, 2011, 2012 & 2014.
About the story
This is a trilogy that involves vampires and time travel. No, wait! Keep reading! Not those kinds of vampires… well, yes, those kind, but you hardly notice, really.
I know, it sounds very cheesy but Deborah E. Harkness is an historian: the research into the time periods was done right and she writes very well. It’s very readable, and there is a tolerable amount of sex (in most vampire tales, the sex comprises most of the story and grammar is often eschewed).
So, starting over: This is a trilogy that involves vampires and time travel. And witches. And a magic house. Hmm.
Starting over: In A discovery of witches fictional historian Diana Bishop is researching her topic on the history of science in the Bodleian library at Oxford, when she comes across an unusual manuscript, one that has been lost for hundreds of years. Magically hidden in the special collections, Diana’s reading of the book sets in motion a maze of occult happenings. Coming as she does from a long line of New England witches, Diana is aware of the book’s power and sends it back straightaway. But, it’s too late. Weird stuff happens.
The second book, Shadow of night, is mostly set in the England of the 1590s. Diana’s love Matthew reclaims his identity as a poet and spy and member of the School of the Night, a secret society of learning that runs counter to the religious zeitgeist of the times. All very cloak and dagger.
The third and final book, The book of life, returns to present day and to the mysterious Ashmole 782 manuscript — specifically, to the missing pages thereof. The alchemical flavours underlying the previous two books are thrust to the fore and the imagery and its significance play a huge role in the story.
The books are each read by different narrators, which slows down the re-integration of the story if, like me, you read the books over a broad-ish period of time. Despite that, this is an excellent series that is good fun and mentally engaging to read. Or, I’m a sucker for period fiction (which is also true). I like the characters, I like the plots – which never seem overly long – and I like the way the story unfolds over several time periods.
Recommended?
Good for people who enjoy supernatural or historical fiction.