# Load packages
library(tidyverse); library(plotly); library(httr)
# Step 1: Load data
<- "https://raw.githubusercontent.com/mkmiecik14/mkmiecik14.github.io/master/data/corsicaData_5v4.csv"
link
# from https://stackoverflow.com/questions/60714074/download-csv-file-from-github-using-httr-get-request
<- GET(link)
data <- read_csv(content(data, "raw"))
data5v4
# Step 2: Clean up the data a little bit
# ATL moved to WPG for 2011-2012 season
$Team <- plyr::revalue(data5v4$Team, c(ATL = 'ATL.WPG', WPG = 'ATL.WPG')) data5v4
The Last Decade: NHL’s Best and Worst Power Play Teams
Note: This blog post presents a model of powerplay goals in the NHL over 10 years. However, this model is inaccurate. I encourage readers to read this post and then proceed to my follow-up blog post where I explain the source of these potential errors and provide a more accurate and informative model.
So you are watching your favorite NHL team and they finally draw a powerplay. Awesome! The ice opens up with the missing player and it shouldn’t be too hard to score…right? As a previous water polo player, I know what it is like to endure the wrath of a furious coach after a missed man advantage opportunity. In my experience, coaches semed convinced that the man-up should lead to a goal 100% of the time. But how important is this seemingly advantageous situation in the NHL?
As a first pass, I analyzed the NHL regular-season over the past ten years (2007-2017) on the standard 5 vs. 4 power play. These data were retrieved via Corsica shortly after the end of the 2017 regular season. The following will detail the analyses in R with steps along the way utilizing the wonderful R packages of ggplot2 (Wickham, 2009) and dplyr (Wickham & Francois, 2016).
First, I loaded the data and made sure to combine the entries of the Atlanta Thrashers and the Winnipeg Jets, as the Thrashers moved to Winnipeg after the 2011 season. Therefore, the Thrashers and the Jets are considered the same team in this analysis.
Next, I plotted the global trend of total regular-season power play goals for each of the 30 NHL teams over the last decade.
# Step 2a: Look at power play goals 5v4 for every team in the past
ggplot(data5v4, aes(factor(Season), GF, group = 1)) +
geom_line(aes(group = factor(Team)), alpha = 1/2) +
geom_line(stat = 'summary', fun.y = 'mean', colour = 'red') +
xlab('Season') +
ylab('Total Season Power Play Goals (5v4)') +
theme(axis.text.x = element_text(angle = 15, hjust = 1))
In this above plot, each team is a grey line, while the average total power play goals for each season is plotted in the red trend line. However, we see that this doesn’t control for the shortened lockout season that occurred in 2012-2013. Let’s control for this by dividing the total power play goals (GF) by the total games played (GP) each season.
# Step 2b: Control for games played due to partial lockout season (2012-2013)
ggplot(data5v4, aes(factor(Season), GF/GP, group = 1)) +
geom_line(aes(group = factor(Team)), alpha = 1/2) +
geom_line(stat = 'summary', fun.y = 'mean', colour = 'red') +
xlab('Season') +
ylab('Total Season Power Play Goals/Games Played (5v4)') +
theme(axis.text.x = element_text(angle = 15, hjust = 1))
We now see each team’s power play performance controlled for the games played that season. What is interesting about this above plot is the seemingly decrease in power play goals across time. Let’s model this with a linear regression that predicts the total season power play goals controlled for games played as a function of time/season.
# Step 3a: Linearly model this negative relationship between
# power play goals/game and season
<- lm((GF/GP) ~ factor(Season), data = data5v4)
mod summary(mod) # Very significant negative trend over time
>
> Call:
> lm(formula = (GF/GP) ~ factor(Season), data = data5v4)
>
> Residuals:
> Min 1Q Median 3Q Max
> -0.24268 -0.06463 -0.00878 0.05407 0.33333
>
> Coefficients:
> Estimate Std. Error t value Pr(>|t|)
> (Intercept) 0.64176 0.01890 33.953 < 2e-16 ***
> factor(Season)20082009 0.03628 0.02673 1.357 0.175726
> factor(Season)20092010 -0.04644 0.02673 -1.737 0.083394 .
> factor(Season)20102011 -0.09420 0.02673 -3.524 0.000494 ***
> factor(Season)20112012 -0.13776 0.02673 -5.154 4.73e-07 ***
> factor(Season)20122013 -0.12093 0.02673 -4.524 8.86e-06 ***
> factor(Season)20132014 -0.11779 0.02673 -4.407 1.48e-05 ***
> factor(Season)20142015 -0.12875 0.02673 -4.817 2.36e-06 ***
> factor(Season)20152016 -0.12591 0.02673 -4.710 3.84e-06 ***
> factor(Season)20162017 -0.12555 0.02673 -4.697 4.08e-06 ***
> ---
> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> Residual standard error: 0.1035 on 290 degrees of freedom
> Multiple R-squared: 0.2464, Adjusted R-squared: 0.223
> F-statistic: 10.54 on 9 and 290 DF, p-value: 4.082e-14
As we can see from the regression model and coefficients, power play goals are decreasing with each successive season. Here is a plot of this negative trend in blue.
# Step 3b: Plot this linear trend over time
ggplot(data5v4, aes(factor(Season), GF/GP, group = 1)) +
geom_line(aes(group = factor(Team)), alpha = 1/2) +
geom_line(stat = 'summary', fun.y = 'mean', colour = 'red') +
xlab('Season') +
ylab('Total Season Power Play Goals/Games Played (5v4)') +
stat_smooth(method = 'lm', col = 'blue', se = F) +
theme(axis.text.x = element_text(angle = 15, hjust = 1))
I then wondered which teams, despite this decrease in power play goals over time, had the best or worst power play across the last decade? To remove this negative trend over time, I extracted the residuals from the model. The residuals are simply the distance from the blue line (the predicted power play goals over time) to each team’s grey line (total power play goals/games played for each year).
# Step 4a: Remove this linear trend and focus on residuals
# This will examine teams' performance after controlling for this decrease in
# powerplay goals in the last decade
$rel_ppg <- resid(mod) data5v4
Next, I averaged these residuals for each individual team over ten years and ran one-sample t-tests to see the teams that significantly deviated from the average.
# Step 4b: Average the residuals for each team over the past decade. Then,
# perform a one-sample t-test (i.e., from zero) to see teams that were above
# or below the average power play goals/game despite the decrease in
# power play goals in the last decade
<- data5v4 %>%
bestPP group_by(Team) %>%
summarise_at('rel_ppg',
funs(mean = mean,
sd = sd,
sem = sd(.)/sqrt(n()),
tpval = t.test(.)$p.value)) %>%
mutate(sig = tpval < .05)
print(bestPP)
> # A tibble: 30 × 6
> Team mean sd sem tpval sig
> <chr> <dbl> <dbl> <dbl> <dbl> <lgl>
> 1 ANA 0.0296 0.0814 0.0257 0.280 FALSE
> 2 ARI -0.0556 0.0777 0.0246 0.0497 TRUE
> 3 ATL.WPG -0.0456 0.0735 0.0232 0.0811 FALSE
> 4 BOS -0.0250 0.102 0.0323 0.459 FALSE
> 5 BUF -0.0230 0.0987 0.0312 0.479 FALSE
> 6 CAR -0.0102 0.0896 0.0283 0.726 FALSE
> 7 CBJ -0.0532 0.0891 0.0282 0.0919 FALSE
> 8 CGY -0.0112 0.0604 0.0191 0.573 FALSE
> 9 CHI -0.000760 0.0836 0.0264 0.978 FALSE
> 10 COL -0.0783 0.0669 0.0212 0.00493 TRUE
> # … with 20 more rows
Finally, I plotted these results. Teams that have positive averages that are different from zero are teams that are above average in the power play, despite the decrease in power play goals scored in the last decade. The teams with negative residual averages are the opposite and are below average in the power play. Error bars represent the standard error of the mean.
# Step 4c: Plot the average residuals, their standard error of the mean,
# and color based on if significantly different from 0 (i.e., average PPG/Game)
# -- To get y.axis bold conditional -- #
<- arrange(bestPP, mean)
bestPPSort <- ifelse(bestPPSort$sig == T,'bold', 'plain')
axisFace # -- To get y.axis bold conditional -- #
# -- To get shading conditional -- #
<- bestPP %>% filter(mean > 0, sig == TRUE) %>% arrange(mean)
above <- bestPP %>% filter(sig == FALSE)
average <- bestPP %>% filter(mean < 0, sig == TRUE) %>% arrange(mean)
below # -- To get shading conditional -- #
ggplot(bestPP, aes(mean, reorder(Team, mean), colour = sig)) +
geom_errorbarh(aes(xmin = mean - sem, xmax = mean + sem)) +
geom_point(size = 2) +
scale_x_continuous(limits = (c(-.2, .2))) +
scale_color_manual(
values = c('grey', 'black'),
guide = guide_legend(
reverse = T, title = 'Above/Below Average', title.position = 'top')
+
) labs(
x = 'Mean Power Play Goals/Games Played (Residuals)',
y = 'NHL Team',
title = 'NHL 5v4 Power Play Team Performance 2007-2017',
caption = 'Error bars are SEM'
+
) theme_minimal() +
theme(
axis.text.y = element_text(face = axisFace),
plot.title = element_text(hjust = 0.5),
legend.position = 'bottom',
legend.key = element_rect(colour = "transparent", fill = "white"),
legend.key.width = unit(1, 'in'),
legend.key.height = unit(.5, 'in'),
legend.title.align = .5,
axis.title.x = element_text(vjust = -1)
+
) annotate(
'rect',
xmin = -Inf,
xmax = Inf,
ymax = above$Team[nrow(above)],
ymin = above$Team[1],
fill = '#33a02c',
alpha = 1/3
+
) annotate(
'rect',
xmin = -Inf,
xmax = Inf,
ymax = below$Team[nrow(below)],
ymin = below$Team[1],
fill = '#e31a1c',
alpha = 1/3
+
) annotate('text', x = -.1, y = above$Team[3], label = 'Best Teams') +
annotate('text', x = .1, y = below$Team[4], label = 'Worst Teams')
As we can see from this plot, the top power play teams in the past decade have been the Washington Capitals, Philadelphia Flyers, Detroit Red Wings, San Jose Sharks, and Pittsburgh Penguins. Meanwhile, the worst power play teams have been the New York Islanders, Phoenix Coyotes, Nashville Predators, New York Rangers, New Jersey Devils, Colorado Avalanche, and Florida Panthers.
Interestingly, only 5 different teams have won the Stanley Cup in this ten year span. Of the top power play teams from this analysis, only the Red Wings and the Penguins have won the Stanley Cup, while the Blackhawks, the Kings, the Bruins all had average regular-season power plays. It is unclear how regular-season power play performance relates to Stanley Cup wins; however, this analysis demonstrated 3 key findings for this last decade of hockey (2007 – 2017):
- League-wide total power play goals per season have decreased
- Top power play teams are:
- Washington Capitals
- Philadelphia Flyers
- Detroit Red Wings
- San Jose Sharks
- Pittsburgh Penguins
- Worst power play teams are:
- New York Islanders
- Phoenix Coyotes
- Nashville Predators
- New York Rangers
- New Jersey Devils
- Colorado Avalanche
- Florida Panthers
Acknowledgments
This analysis was inspired by Part III of ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham.
References:
Wickham, H. 2009. ggplot2: Elegant Graphics for Data Analysis. Book. Springer-Verlag New York. http://ggplot2.org.
Wickham, H., and R. Francois. 2016. dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.


--- layout: index title: Home permalink: / ---
Hey, I’m Łukasz! I specialise in digital security and the wider relationship between humans and technology.
Currently a digital security trainer at Internews, I have designed and led summer schools, conducted digital security trainings, and shared ideas through many workshops and conference speeches. For more details, check out some of my talks and major projects.
Here’s a copy-pasteable bio and photos!
I’m always looking for new projects and ideas. I’m reachable via lukasakrol at gmail dot com or Wire (lukaszkrol)


Sound Generator
Our monks have engineered this system with the following goals in mind:
- browser-based for maximum portability
- use solfeggio base frequencies
- use Pythagorean tuning or just intonation
- use colorful geometric diagrams to promote synesthesia
- animated yantras for faster memorization
- mantras to help attain mantra siddhi
- brainwave entrainment, including binaural, monaural, isochronic or phasing
- subliminal affirmations to help reprogram the subconscious mind


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RISE For Climate Justice On Monday, 29 September
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One Billion Rising is joining grassroots activists, Indigenous
leaders, business leaders, scientists, policy makers, farmers,
academics and culture-shapers in collaboration to RISE for Climate
Justice! Women’s Earth and Climate Action Network is
organizing the Global Women’s Climate Justice Day of Action for
Monday, 29 September.
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Join us and women worldwide in organizing decentralized local actions
- from marches & sit-ins, to educational workshops, art
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PLAN your action on (or before) 29 September! Actions can range from
simply creating a sign and taking a photo displaying your climate
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marches, sit-ins, educational events, art projects, etc.
TAKE action >
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SIGN the “Women’s Climate Declaration” to be
delivered to world governments at COP21 in Paris this December
>
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NYC: Special “Absolute Brightness” Ticket Offer; Eve To Recieve Award
Absolute Britness.gif
Join Eve for a special night of theater and celebration in
NYC! Wednesday, 23 September the V-Day & One Billion Rising
community is being offered a special 2-for-1 ticket
package provided by longtime friend & activist James Lecesne
and the producers, for selected performances of The Absolute
Brightness of Leonard Pelkey immediately followed by the
presentation of The Absolute Brightness award to Eve Ensler.
Purchase a ticket to Absolute Brightness and bring someone
to the show who helps you live as your truest self for free. We
hope to see you there.
PURCHASE tickets >
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Last Minute Chance To Support Her Story Webseries
http://action.vday.org/site/R?i=QJkufEJdE5nUwDzxCXLJnw
With less than 48 hrs left, don’t miss your chance to support
the exciting new web
series Her Story, that looks inside
the dating lives of trans & queer women as they navigate the
intersections
of desire & identity.  Former V-Staffers Kate Fisher and Laura Zak are Executive
Producing and
co/writer/actor respectively.
SUPPORT the series>
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Read an interview with the creators by Eve >
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JUMANJI A film review by Steve Rhodes Copyright 1996 Steve Rhodes
RATING (0 TO ****): ***
After electronically surveying friends and coworkers about whether JUMANJI would scare the daylights out of my son Jeffrey (soon to be 7), we finally agreed it was time to give it a try. The voting before we went was running about two to one that we should take him, but there was a vocal minority warning us that since Jeffrey can frighten easily, we should not get anywhere near the show. Since Jeffrey is a big movie buff like his Daddy, in the end he decided to go, so the whole family went on President's Day. Five minutes into the movie, Jeffrey was in my lap declaring, "We should have gone to MUPPET TREASURE ISLAND." Notwithstanding his periodic fear during the film, we all three loved it immensely. It is a great mixture of action, humor, special effects, and poignancy.
As JUMANJI starts, it is a dark, foggy, and spooky night in New Bedford, Massachusetts in the year 1869, and the coyotes are wailing away. Two children are burying a box. A hundred years later, a boy named Alan Parrish (Adam Hann-Byrd), finds this box and inside is a board game called Jumanji. He and his friend Sarah (Laura Bell Bundy) decide to play the game. When they throw the dice, the pieces move by themselves so Sarah figures "it must be magnets".
Soon the game turns out to be pretty terrifying. After each turn, ferocious jungle animals come out to attack them. After one turn, Alan gets the message, "In the jungle you must wait until the dice read five or eight." He is thereupon sucked into the game, and Sarah runs screaming out of the room since she is now scared out of her wits.
The story skips ahead to 1995. The old abandoned Parrish house is being occupied by a new set of young children, Peter (Bradley Michael Pierce) and Judy (Kirsten Dunst), along with their Aunt Nora (Bebe Neuwirth). The kids find Jumanji, and again, the pieces move by themselves which causes Judy to comment "it must be microchips." Soon however, all hell is unleashed on the town, but at least the first throw of the correct number frees Alan (now played by Robin Williams). To save the community from the destruction, they have to finish the game and to do this they must find Sarah (now played by Bonnie Hunt) and get her to play too.
The logo for this show should have been a whirlwind. Getting sucked into the game is pictured as a whirlwind, and the frantic and marvelous pacing by director Joe Johnston sweeps the audience in as well. A wonderful roller coaster of a good time at the movies to use another metaphor. And funny. The script by Jonathan Hensleigh, Jim Strain, and Greg Taylor, which is based on the book by Chris Van Allsburg, is naturally hilarious with great sight gags. Among my many favorites were the monkeys driving the police motorcycle and the one of the flood, but there were many more. The beauty of the story itself is that it naturally sets an horrific pace. Be sure and take everyone to the bathroom before entering, you will not be able to miss a minute of this.
After the story, the director is the one who deserves the most credit. He had his actors in control and everything set up perfectly. He knew exactly what he wanted to do and executed precisely. The cinematography (Thomas Ackerman) with the dark and shadowy interiors was excellent at evoking a far away jungle. Usually extraordinary special effects, as these are, dazzle the audience and focuses the audience's attention on the mechanics of the effects. Here, the special effects, i.e., all of the animals, were great, but they complimented the story rather than dominating it.
All of the actors were fine albeit not on par with the story or the directing. My favorite was Kirsten Dunst's performance of the little girl who told one tall tale after another. Robin Williams was well cast as a serious and sad character, and the director keep him in control so that Williams did slip into his comedic role.
JUMANJI runs a blazing fast 1:40. It is rated PG for violence, e.g., a stampede of rhinos, but no one gets killed in the show. There is no sex, nudity, smoking, etc. Except for the violence, and that is a big except, this would have been a G rated show. There are no swear words in the show, a minor miracle today. Jeffrey says he would recommend it for kids 5 or 6 and up, and he enthusiastically gives it two thumbs up. His dad recommends the movie, but still would caution parents of kids under 10 that the special effects can be terrifyingly real. What if you walked into your bedroom now and a real lion roared at you from your bed? Would you be scared? Finally, I award it ***.
**** = One of the top few films of this or any year. A must see film. *** = Excellent show. Look for it. ** = Average movie. Kind of enjoyable. * = Poor show.  Totally unbearable.
REVIEW WRITTEN ON: February 19, 1996
Opinions expressed are mine and not meant to reflect my employer's.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


When MultiMona encounters a problem you will often see an error message. This can be in a small dialog box or written in the status line at the bottom of MultiMona. Messages written to the status line are also repeated in the ' Messages' tab.
Like clues in a detective story you can often work out what the original problem was from these error messages. See if your error message / exception is listed below - if it is click on the link for more information.


Project Dependencies
compile
The following is a list of compile dependencies for this project. These dependencies are required to compile and run the application:
| GroupId |
ArtifactId |
Version |
Type |
Licenses |
| commons-io |
commons-io |
2.2 |
jar |
The Apache Software License, Version 2.0 |
test
The following is a list of test dependencies for this project. These dependencies are only required to compile and run unit tests for the application:
| GroupId |
ArtifactId |
Version |
Type |
Licenses |
| junit |
junit |
4.12 |
jar |
Eclipse Public License 1.0 |
provided
The following is a list of provided dependencies for this project. These dependencies are required to compile the application, but should be provided by default when using the library:
| GroupId |
ArtifactId |
Version |
Type |
Licenses |
| javax.servlet |
servlet-api |
2.4 |
jar |
- |
| portlet-api |
portlet-api |
1.0 |
jar |
- |
Project Transitive Dependencies
The following is a list of transitive dependencies for this project. Transitive dependencies are the dependencies of the project dependencies.
test
The following is a list of test dependencies for this project. These dependencies are only required to compile and run unit tests for the application:
| GroupId |
ArtifactId |
Version |
Type |
Licenses |
| org.hamcrest |
hamcrest-core |
1.3 |
jar |
New BSD License |
Project Dependency Graph
Dependency Tree
- commons-fileupload:commons-fileupload:jar:1.4
- junit:junit:jar:4.12 (test)
- org.hamcrest:hamcrest-core:jar:1.3 (test)
- javax.servlet:servlet-api:jar:2.4 (provided)
| servlet-api |
|
Description: There is currently no description associated with this project.
Project Licenses: No licenses are defined for this project. |
- portlet-api:portlet-api:jar:1.0 (provided)
| portlet-api |
|
Description: There is currently no description associated with this project.
Project Licenses: No licenses are defined for this project. |
- commons-io:commons-io:jar:2.2 (compile)
Licenses
Unknown: portlet-api, servlet-api
Eclipse Public License 1.0: JUnit
Apache License, Version 2.0: Apache Commons FileUpload
New BSD License: Hamcrest Core
The Apache Software License, Version 2.0: Commons IO
Dependency File Details
| Filename |
Size |
Entries |
Classes |
Packages |
Java Version |
Debug Information |
| commons-io-2.2.jar |
173.6 kB |
126 |
108 |
6 |
1.5 |
Yes |
| servlet-api-2.4.jar |
97.7 kB |
67 |
42 |
2 |
1.2 |
Yes |
| junit-4.12.jar |
314.9 kB |
323 |
286 |
30 |
1.5 |
Yes |
| hamcrest-core-1.3.jar |
45 kB |
52 |
45 |
3 |
1.5 |
Yes |
| portlet-api-1.0.jar |
17.1 kB |
31 |
27 |
1 |
1.1 |
Yes |
| Total |
Size |
Entries |
Classes |
Packages |
Java Version |
Debug Information |
| 5 |
648.3 kB |
599 |
508 |
42 |
1.5 |
5 |
| compile: 1 |
compile: 173.6 kB |
compile: 126 |
compile: 108 |
compile: 6 |
- |
compile: 1 |
| test: 2 |
test: 360 kB |
test: 375 |
test: 331 |
test: 33 |
- |
test: 2 |
| provided: 2 |
provided: 114.8 kB |
provided: 98 |
provided: 69 |
provided: 3 |
- |
provided: 2 |
|


Like a particular artist? Looking for someone new to listen to? Find your next favorite artist here. Enter an artist’s name and get a list of similar musicians.


{% extends "admin_layout.html" %} {% block page_header %}
When writing for services, you should aim to reduce the cognitive load on users. This helps to:
Through user research, data from search result data and analytics, it is possible to understand what words and terms our users use. We should aim to reflect that in our services to make them easier to understand and use.
Use the lexicon to see alternatives to words and phrases that we use.
Most people have a vocabulary of 15,000 words and terms. These are words a user is most likely to recognise and skip because they are so recognisable. Many are called high-frequency words such as prepositions, conjunctions and pronouns (and, the, on, but, me, it). low-frequency words are often nouns, verbs and adjectives take longer to read and understand.
By using high freuqency words, we are allowing users to take in more information, more quickly. This is called 'using plain English' or as some people will say, 'dumbing down'.
Contractions are a shortened version of one or more words. For example, don't (do not)
In general content, the use of contractions is encouraged.
[TK]
Don't use 'please' and 'thank you' in content, it isn't necessary in digital services. People use and understand language differently between physical interactions and digital interactions.


# File lib/active_record/connection_adapters/postgresql/schema_definitions.rb, line 127 def primary_key(name, type = :primary_key, options = {}) return super unless type == :uuid options[:default] = options.fetch(:default, 'uuid_generate_v4()') options[:primary_key] = true column name, type, options end
class ActiveRecord::ConnectionAdapters::PostgreSQL::TableDefinition
Public Instance Methods
Defines the primary key field. Use of the native PostgreSQL UUID type is supported, and can be used by defining your tables as such:
create_table :stuffs, id: :uuid do |t| t.string :content t.timestamps end
By default, this will use the +uuid_generate_v4()+ function from the
uuid-ossp
extension, which MUST be enabled on your database.
To enable the uuid-ossp
extension, you can use the
enable_extension
method in your migrations. To use a UUID
primary key without uuid-ossp
enabled, you can set the
:default
option to nil
:
create_table :stuffs, id: false do |t| t.primary_key :id, :uuid, default: nil t.uuid :foo_id t.timestamps end
You may also pass a different UUID generation function from
uuid-ossp
or another library.
Note that setting the UUID primary key default value to nil
will require you to assure that you always provide a UUID value before
saving a record (as primary keys cannot be nil
). This might be
done via the SecureRandom.uuid
method and a
before_save
callback, for instance.
Private Instance Methods
# File lib/active_record/connection_adapters/postgresql/schema_definitions.rb, line 142 def create_column_definition(name, type) PostgreSQL::ColumnDefinition.new name, type end


| File | Statements | Branches | Functions | Lines | |||||
|---|---|---|---|---|---|---|---|---|---|
| src/ | 78.81% | 93/118 | 74.36% | 58/78 | 63.64% | 7/11 | 78.45% | 91/116 | |
| src/knowledge/ | 24.64% | 17/69 | 100% | 0/0 | 13.33% | 4/30 | 24.64% | 17/69 | |
| src/language/ | 14.02% | 46/328 | 0% | 0/140 | 14.29% | 5/35 | 14.47% | 46/318 | |
| src/search/ | 53.78% | 64/119 | 0% | 0/76 | 32.35% | 11/34 | 53.78% | 64/119 | |
| src/speech/ | 23.38% | 18/77 | 0% | 0/10 | 18.18% | 4/22 | 23.38% | 18/77 | |
| src/vision/ | 42.86% | 93/217 | 0% | 0/4 | 42.11% | 40/95 | 42.86% | 93/217 |


This page is work in progress, none of the links work.
For now you can generate module docs locally:
v doc os
v doc http
...
V's predeclared functions and types (including strings, arrays, maps)
Simple benchmarking
Manipulating array of bits
V compiler as a module
AES encryption (formerly Rijndael), as defined in U.S. Federal Information Processing Standards Publication 197
Standard block cipher modes that can be wrapped around low-level block cipher implementations
Encoding and decoding of base64 as defined in RFC 4648
Simple translation between numbers and byte sequences and encoding and decoding of varints
Reads and writes comma-separated values (CSV) files
Hardware accelerated Graphics library using OpenGL (DirectX, Vulkan, Metal support soon)
OpenGL wrapper
Constants and helpers for drawing
Encoding and decoding of JSON as defined in RFC 7159
Basic constants and mathematical functions
Platform-independent interface to operating system functionality
Pseudo-random number generators
Measuring and displaying time
Cross-platform UI library


History of the Internet
Surf's up!
Home
About
Team
History of the Internet
Invention of the Internet
Browser Wars
Internet Search
Dot Com Bubble
Important People
Click here to surf our website!


Aggregation rules¶
David Hassell and Jonathan Gregory
Version 3.2.0 for version 1.8 of the CF conventions.
Aggregation is the combination of two field constructs to create a new field construct that occupies a “larger” domain. In practice, this means combining the two field constructs so that their data are concatenated along one or more domain axis constructs, as are the data of their metadata constructs which span those domain axis constructs.
These rules are be used for deciding whether or not two arbitrary field constructs may be aggregated into one, larger field construct. The rules are based solely on the field constructs’ metadata as recognised by the CF data model 1. For example, netCDF variable names are ignored during the aggregation process, meaning that having different netCDF variable names does not preclude the aggregation of two field constructs.
More than two field constructs are aggregated by repeated applications of the aggregation algorithm, and aggregations over multiple domain axis constructs are similarly possible.
Aggregation is implemented in the cf.aggregate
function, and is
applied by default by the cf.read
function.
If all of the following statements are true for two arbitrary field constructs then the two field constructs may be aggregated to a new field construct.
Both field constructs have identical standard name properties.
The treatment of other properties, in terms of how they should match between the field constructs and whether or not they should be included in the aggregated field, is beyond the scope of these rules and is at the user’s discretion. This is also the case for all other construct properties unnamed by these rules.
Coordinate construct
A coordinate construct is either a dimension coordinate construct or an auxiliary coordinate construct.
Pair of matching coordinate constructs
A pair of matching coordinate constructs is a coordinate construct from each field construct of the same type (dimension or auxiliary), with equivalent calendar properties (if present) and identical standard names.
Both field constructs have the same number of coordinate constructs, all of which have a standard name property, and each coordinate construct’s standard name is unique within its field construct. Each coordinate construct in one field construct forms a pair of matching coordinate constructs with a unique coordinate construct in the other field construct.
Domain axis’s associated coordinate constructs
A domain axis’s associated coordinate constructs are those coordinate constructs which span the domain axis.
Each domain axis in both field constructs has at least one associated 1-d coordinate construct.
Pair of matching axes
A pair of matching axes is a domain axis from each field construct chosen such that the two domain axes have associated coordinate constructs that are all matching pairs.
Each domain axis in one field construct forms a pair of matching axes with a unique domain axis in the other field construct.
A consequence of this rule is that within each pair of matching coordinate constructs, the corresponding domain axes are matching pairs.
Pair of aggregating axes
The pair of matching axes for which one or more of the 1D matching coordinate constructs have different values for their coordinate arrays and, if present, their boundary coordinate arrays is called the pair of aggregating axes.
There is exactly one pair of matching axes for which one or more of the 1D matching coordinate constructs have different values for their coordinate arrays and, if present, their boundary coordinate arrays.
A pair of matching non-aggregating axes must have identical sizes.
The aggregating axis may have either the same size or a different size in the two field constructs.
If the domain axes of the arrays of matching associated constructs are ordered differently or run in different senses then one of the arrays must be rearranged prior to comparison. This is true for all array comparisons in these rules.
If neither field construct has an aggregating axis then the field constructs are not aggregatable because their domains are identical.
If either field construct has two or more domain axes which could qualify as an aggregating axis then the field constructs are not aggregatable because it is not possible to choose the single domain axis for array concatenation.
Pair of matching cell measure constructs
A pair of matching cell measure constructs is a cell measure construct from each field construct with equivalent units and corresponding domain axes that are matching pairs.
Both field constructs have the same number of cell measure constructs, all of which have a units property. Each cell measure construct in either field construct forms a pair of matching cell measure constructs with a unique cell measure construct in the other field construct.
Each pair of matching coordinate constructs and matching cell measure constructs that do not span their aggregating axes have identical values for their coordinate arrays and, if present, their boundary coordinate arrays.
If the pair of matching aggregating axes has a pair of associated dimension coordinate constructs, then there are no common values in their coordinate arrays. If the matching dimension coordinate constructs have boundary coordinate arrays then no cells from one dimension coordinate construct lie entirely within any cell of the other dimension coordinate construct.
This does not preclude the coordinate arrays’ ranges from overlapping.
The condition on the boundary coordinate arrays prevents, for example, a monthly mean being aggregated with a daily mean from the same month.
The condition on the boundary coordinate arrays allows, for example, the aggregation of overlapping running means; or the aggregation of a monthly mean and a daily mean from a different month.
If one field construct has a cell methods construct then so does the other field, with the equivalent methods in the same order. Corresponding domain axes in each cell methods are matching pairs.
Pair of matching domain ancillary constructs
A pair of matching domain ancillary constructs is a domain ancillary construct from each field construct for identical standard coordinate conversion terms and corresponding domain axes that are matching pairs.
Both field constructs have the same number of domain ancillary constructs. Each domain ancillary construct in either field construct forms a pair of matching domain ancillary constructs with a unique domain ancillary construct in the other field construct.
Pair of matching field ancillary constructs
A pair of matching field ancillary constructs is a field ancillary construct from each field construct with identical standard names and corresponding domain axes that are matching pairs.
Both field constructs have the same number of field ancillary constructs. Each field ancillary construct in either field construct forms a pair of matching field ancillary constructs with a unique field ancillary construct in the other field construct.
Both field constructs have the same number of coordinate reference constructs. For each coordinate reference construct in one field construct there is a coordinate reference construct in the other field construct with identical name and the same set of terms, taking optional terms into account. Corresponding terms which are scalar or vector parameters are identical, taking into account equivalent units. Corresponding terms which are domain ancillary constructs form a pair of matching domain ancillary constructs.
- 1
Hassell, D., Gregory, J., Blower, J., Lawrence, B. N., and Taylor, K. E.: A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1), Geosci. Model Dev., 10, 4619-4646, https://doi.org/10.5194/gmd-10-4619-2017, 2017.


The Winslow Boy (1999) Reviewed by Eugene Novikov http://www.ultimate-movie.com Member: Online Film Critics Society
***1/2 out of ****
"How little you know about men."
Starring Nigel Hawthorne, Jeremy Northam, Rebecca Pidgeon, Gemma Jones. Rated G.
David Mamet has long been my favorite screenwriter and director. With his distinctive, more often than not ingenious dialogue, and his laid back style of direction nearly all of his movies are absolutely irresistible. Some of them tend to be thickly layered, deceptive productions that require the audience to look at the film in a less superficial manner than the plot seems to require in order to discern its concealed message, or sometimes even a concealed storyline. And although the real plot in his new project The Winslow Boy is slightly more conspicuous than in some of his other endeavors, it is still a brilliantly complex, consistently riveting motion picture about honor, about sacrifice, and about the difference between what is commonly known as justice and what is right. Oh, and it's rated G.
Incidentally, this is the first time that Mamet has decided to adapt someone else's work; namely a play by Terrence Rattigan, set in the 19th Century. He casts Nigel Hawthorne in the lead role as Arthur Winslow, a rich, aging man who finds out that his 14 year old son Ronnie has been kicked out of the Naval Academy for allegedly stealing a 5 shilling postal note. "Did you do it?" he asks his boy. "No father, I didn't," Ronnie answers. That's enough for Arthur, who, with his oldest daughter (Rebecca Pidgeon), immediately starts a crusade to bring his son's case to court. They enlist the help of Sir Robert Morton, a notorious attorney to help them achieve that formidably daunting task.
It all seems fairly frivolous, and no matter how you look at it, the Winslow Case is not the trial of the century. But Arthur is determined to keep his family's word clean and he is willing to go quite far to make sure of that. Soon enough, Sir Robert Morton along with the rest of the country becomes equally wrapped up in the proceedings. So do we.
All David Mamet does for the script is tighten and hone the dialogue, but his style is still fairly apparent. The characters still talk in his trademark staccatto lines and there is still tension present in conversations that no ordinary writer would be able to make tense. But this is not Mamet's norm, and it's refreshing to see Mamet deviate from his world of crooks, gangsters as con men, wonderful as those films were.
Nigel Hawthorne's performance is nearly flawless. His delivery is that of a dignified yet not pompous man who seems to be getting beaten at his own game. We pity the man, but we also like him. Rebecca Pidgeon, David Mamet's wife, who gave a fairly awful performance in The Spanish Prisoner is at the top of her game here as Arthur's oldest daughter, a flailing feminist who gives her all to the Winslow case as a way for making up for her lack of success in the Women's Suffrage Movement.
The Winslow Boy is a wonderful movie that avoids cliches such as a seemingly inevitable courtroom scene and shoots higher -- it wants to make a real impact rather than a phony one. Be honest: did you feel anything profound at or after the courtroom scenes in films like A Time To Kill? If you wanted to but didn't, this is a movie for you. It is the epitome of subtlety: it's powerful without being too emotional, sad without even trying to be depressing.
David Mamet can churn out some great scripts, but in this movie he proves to those who ever doubted it once and for all that he is a hell of a director too. He is almost a national treasure. His films deserve their own genre. ©1999 Eugene Novikov
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val randomStateSeed: Long?
Sets the seed of the random number generator. If a value is given, the seed will be set, and will produce deterministic runs. If null is given, a random seed will be chosen.
randomStateSeed
- Sets the seed of the random number generator. If a value is given, the seed will
be set, and will produce deterministic runs. If null is given, a random seed will be chosen.


There is a new generation of entrepreneurs that are flexing beyond what meet the eyes and the whole world is watching. Internet presence is all what you need to make it, especially if you know how to market yourself. Take Lagos born and bred merchant and business dude Pere. Hailing from Festac with an avid spirit for the arts and hustle, Pere is a true product of his environment finding everyday excellence quite comfortable.
If you need something for your household and your everyday outing. I can deliver quality product to you anytime, anywhere.


Magruder's American Government (California)
Magruder's American Government
United States Government: Principles in
United States Government: Our Democracy
Magruder's American Government
Magruder's American Government
United States Government: Our Democracy
United States Government: Democracy In
Magruder's American Government
Street Law: A Course in...
Magruder's American Government
United States Government: Democracy In
Street Law: A Course in...
Gateway to American Government: The...
Magruder's American Government
TEKS United States Government
Magruder's American Government
United States Government: Principles in...
Magruder's American Government (Texas)
Magruder's American Government


18 January 2000
Disease Outbreak Reported
The following case report is from the WHO Polio Eradication Programme:
The case was first reported to the County EPS in Geizi Township, Xunhua County, Haidong Prefecture, Qinghai Province, on 13 October 1999, and reported to the Provincial EPS on the following day. The case was born on 13 June 1998, had onset of paralysis on 12 October, after a day of fever on 11 October. The parents took the boy to a local private clinic in a neighbouring township when a sudden onset of flaccid paralysis made him unable to stand or walk (both of which he had been capable of before). Two stool samples were taken, the first on 14 October and the second on 25 October. They were analyzed in the provincial laboratory. Both samples yielded poliovirus isolates, which were later typed and differentiated as P1 wild viruses at the national laboratory in Beijing. At the time that the second sample was taken five contacts were sampled, one of which, a four year old cousin of the infected child, was also positive for wild poliovirus. The case child was unregistered and had received zero doses of polio vaccine.
The case belongs to the Sala minority group, a Muslim group of Turkic speaking people whose ancestors migrated to Qinghai from the area of Turkmenistan about seven hundred years ago. There are around 80 000 Sala in China, 60 000 in Qinghai Province (nearly all of which live in Xunhua Sala Autonomous County) and nearly all of the remainder in neighbouring Gansu Province. Adult male Sala travel widely as traders and workers, within Qinghai province and outside to other provinces, including Gansu, Sichuan,Xinjiang, and particularly Tibet, even as far as the border area with Nepal.
Neither the case nor the direct family reported a history of travel outside the county in the two months prior to onset. No visit to the family by a traveler from outside the county was reported to occur during the same period. However, the family, including the case, attended a major festival of Sala people in the county capital during the period 25 to 28 September 1999. Up to 30 000 Sala are reported to have attended this gathering.
Despite intensive investigation in the area of the case, including searches of health facilities, no evidence of wide-scale circulation of wild poliovirus has yet been found. Surveillance quality including laboratory proficiency in Qinghai Province and in neighbouring provinces is in general good. Indications are therefore that the virus has been recently imported.
The Ministry of Health of China is actively collaborating with the global laboratory network including CDC Atlanta, NIID Tokyo and the national laboratories in India. Initial sequencing information on the wild poliovirus show a close similarity to viruses recently circulating in India. The virus is significantly different from those that have been circulating in China up to the last case in 1994. Further genomic sequencing work is proceeding.
A combined MOH/WHO/UNICEF/JICA mission visited Qinghai Province from 20-25 December 1999 to review the response to the case. Initial case response immunization has been carried out, achieving high coverage of the target group. Extensive additional activities are planned, including large scale immunization across several provinces, intensified surveillance, retrospective review of hospital records at all levels in several provinces,and active search for cases of acute flaccid paralysis.


Allow drag & drop using allowDrag
flag in tree options
Enable dragging with allowDrag
option (default false) accepts either a boolean or a function:
Enable selective dropping with the allowDrop
option (default true) accepts either a boolean or a function:
The default behaviour of the tree is to copy the node when ctrl
is pressed while dropping the node
getNodeClone
option is supplied, it will be called to get a copy of the node. It receives a TreeNode object, and should return a node object (only the data).You can listen to moveNode
events and get the moved node, and its new location in the tree
listen to moveNode events
Use actionMapping.mouse.drop
to override the default drag behaviour.
You can also listen to all other drag events like dragEnd
, dragLeave
etc.:
Override drag & drop action in actionMapping
In the drop callback, you get a from
and to
objects:
from
to
This is enabled by default when dragging is enabled.
You can use the (treeDrop) directive to allow dragging nodes to an external element.
For example:
use (treeDrop) to drag an external element to the tree
Use $event.element
inside the callback.
Use [treeAllowDrop]
Input to specify a function that runs onDragOver, and decides if the dropping is allowed or not.
You can use the [treeDrag]
directive to allow dragging external elements into the tree.
Then use a custom action to handle the drop (see Action Mapping section).
For example:
The data that you pass to [treeDrag]
will be passed to the handler in the from
parameter.
Use to.parent
and to.index
to get the drop location.
Use to.dropOnNode
to distinguish between dropping on the node itself or the drop slot.
If you add a new node to the tree, you'll need to call tree.update()
afterwards.
Use [treeDragEnabled]
boolean Input to decide if the drag is enabled or not.
To drag to an empty tree, set your nodes to an empty array.
If nodes is undefined or null, the drop slot will not appear.
To support drag and drop on mobile, you can use DragDropTouch polyfill in your project to enable it.
Download the code from here: https://github.com/Bernardo-Castilho/dragdroptouch
Place it somewhere in your code, and import it from polyfills.ts, or just place a script tag to include it.
It there are performance issues on large trees while drag and drop it's possible to detach change detection during the drag.


--- layout: default ---
FFWD: A flexible metric forwarding agent
The overview of FFWD.
This document covers some of the major components in FastForward.
Various supported protocols and modules for FFWD. A module is a loadable component that extends the functionality of FastForward.
Write to a serial, peristed queue in OutputManager.


Format: Texture FrameName INHERITS FrameName {
Example: Texture "InfoPanelIconBackdrop" INHERITS "InfoPanelIconTemplate" {
Example: Texture "SimpleBuildQueueBackdrop" {
Purpose: A Texture is a child Frame used in Frames of category SIMPLE. It is used to display images for this group.
Used in: SIMPLEFRAME, SIMPLEBUTTON, SIMPLECHECKBOX, SIMPLESTATUSBAR


Tim Tebow Shares Heartbreaking Goodbye To His Dog
by Matt HladikHaving a pet is one of life’s greatest pleasures, but having to say goodbye to them when they pass away brings unbearable sadness. Millions of people can empathize with what Tim Tebow is going through right now.
 Bronco crossed the ‘Rainbow Bridge’ yesterday, and was undoubtedly a very good boy.
Here’s the message Tebow shared about his pup.
One of the toughest goodbyes. Wanted to make a special tribute to the sweetest boy ever – thank you for all the joy you brought and all the memories. Aunt Patty, thank you for loving him, and thank you to all the other people including Miss Shea and Miss Mindy for sharing so much love with Bronco. If you had the pleasure of meeting Bronco, one of the best dogs ever, I would love to hear your story.
Also, thank you for taking care of him every time he was sick – SEVO-Med, Blue Pearl Vet and UF Small Animal Vet Hospital (Go Gators)
In the video below, Tebow is visibly upset. It’s tough to watch, especially if you own a pet.
Our thoughts and prayers are with Tebow, who is hurting badly right now.
Rest in peace, Bronco. All dogs go to Heaven buddy.


You open up the Manual. the title pages reads Library Manual: you all in one guide for using all of The Library's services. Who needs a manual to use a library you think, as you turn the page. Table of contents, prequel, Who needs a manual to use The Library? Everyone does of course. Great, you think


Event Location:
Central Piedmont Community College (CPCC)
2800 Campus Ridge Road
Charlotte, North Carolina
Abstract:
SSIS package can be run in Parallel and Optimized .In this session we will learn how to improve SSIS Package performance using parallel processing and Optimization. This knowledge will encourage development of better performing packages and optimize package performance.When creating an efficient and robust solution for Extract, Transform and Load Process, Lot of things do matter especially when you are working with huge size of databases. Challenge is not only to meet functional requirements Also to look at the performance requirements. This session will also cover best practices to enhance and boost performance of a SSIS package. You will see all result of session in Power BI real time - Time and all Batch run details
Speaker(s): Vishal Pawar,
Abstract:
This session is an introduction to Service Broker basics and how you can use it to for an alternative to ETL, batch processing and other asynchronous operations. Service Broker allows you to process your data asynchronously in a single instance. It makes for a near real time ETL process. We will start off with the basic components of Service Broker. Then use what we have learned to configure it to process some real life data using American Football Plays on a single database deployment. In addition, we will then use Service Broker as a replication alternative to show the ETL capabilities using databases on two separate instances. At the end of this session, you will have the knowledge to use Service Broker for batch processing, ETL or even an alternative to replication.
Speaker(s): William Wolf,
Abstract:
One of the most common problems I have encountered as a DBA is the complaints from users that a particular query is slow, just one query. Many times one that used to run in acceptable time, but now has slowed down substantially. In this session we will show some of the causes for this, with emphasis on I/O requests being too high for the query. First part of the session will explain how to find these numbers, looking at a few queries with some major performance issues, including explaining why they have a problem. The second part will focus on how to reduce I/O requests. All of t his can be done without the need for any 3rd party tools, all within the scope of what is available to SQL Server, all versions, all editions, including Azure.
Speaker(s): Anders Pedersen,
Abstract:
Did you ever get woken up at night from a call from a user screaming about poor query performance, but didn’t really know how to begin attack the problem? In this session, we will explore the basics of execution plan analysis and how to single out issues that pertain to base table operations. In this presentation, we will identify what indexes are being used by the SQL Optimizer, the different type of index operations that are performed by SQL Server and which operations are generally considered the preferred method of data retrieval. We will discuss how to determine if the indexes used by the Optimizer are optimal, according to best practices, and the exceptions to those cases. Finally, we will cover the relationships between different types of index operations and joins within SQL Server, including how the SQL Optimizer determines which join type it will use, along with the pros and cons of each join operation
Speaker(s): Konstantin Melamud,
Abstract:
Are you familiar w/ SSAS Multidimensional and curious about Tabular? Having trouble deciding between cubes and tabular models? Then this is your session! Analysis Services Tabular was introduced back in 2012, and yet it has only just started to emerge from the Proof-Of-Concept phase to a legitimate production alternative in most IT departments. Based on xVelocity, a highly compressed in-memory technology, Tabular models provide considerable performance improvements over Multidimensional models in many scenarios. And with major advancements in 2016, Tabular has never been a more appealing solution. But, as is the case with any new technology, there are certain limitations of which to be aware. Join me in this session and learn what a Tabular model is, how it fits into the Microsoft BI stack, the major factors to consider when choosing between Tabular and Multidimensional technologies, and finally a live step-by-step demo where we'll build and deploy a Tabular model.
Speaker(s): Bill Anton,
Abstract:
By far the most important SQL Server release since the heady days of 2005, SQL Server 2016 represents a huge leap forward in a number of key areas. You've no doubt heard plenty about such features as Query Store, JSON support, Polybase, and the other big-ticket items. But there are also a number of very cool features that don't usually make the top 10 lists. In this talk you'll learn about enhancements to indexing (including both Columnstore and traditional b-tree indexes), DMVs, compression, string handling, encryption, configuration, and even timezone support. These smaller features may not be highlighted in the latest glossy ads from Microsoft's marketing department, but they're the kinds of things that add up to make your life easier. Attend this session to learn all sorts of new and different reasons to upgrade as quickly as possible.
Speaker(s): Adam Machanic,
Abstract:
"It's Slow!" are probably the two most dreaded words for any database professional (right behind "You're fired!") What's slow? Since when? Compared to what? Join me in this "something-everyone-should-be-doing-but-isn't" session, where we will discuss baselines and performance monitoring for your SQL Server Analysis Services (SSAS) environment. We'll cover what a baseline is, what information it needs to include, and how it can help you troubleshoot immediate problems and proactively avoid them in the future. Walk away from the session with a plan that you can implemented next week to help avoid being caught off guard by SSAS performance issues and to make troubleshooting a breeze.
Speaker(s): Bill Anton,
Abstract:
2016 is the year of All-Flash for primary storage and Dell EMC’s industry leading portfolio of All-Flash arrays unlock the speed, efficiency, and agility that your business stakeholders have been demanding. Whether supporting 1,000 or 10,000,000 IOPS, EMC has a broad portfolio to ensure consistent sub-millisecond response time regardless of workload type. Beyond just production database performance, EMC provides robust database repurposing via our iCDM or Integrated Copy Data Management capabilities. Creating and maintaining test/dev, near real-time analytic, or sandbox environments has never been simpler. Join us and hear how we can address the needs of all of your Microsoft SQL Server instances.
Speaker(s): John Holtz,
Abstract:
R is a pure data science and statistics scripting language. It is considered the best fit for Machine Learning. With more than 5000 packages all machine learning algorithms are covered and improving. R packages help you prepare the data, e.g.: packages for cross validation with N folds, build machine learning models, validation, create plots and to generate predictions. SQL Server 2016 and R Server are a perfect match for building enterprise grade solutions that support deployments for real time predictions and continuous model updates. Join this session for an overview based on sample cases about how to build enterprise grade machine learning solutions with SQL Server and R.
Speaker(s): Paco Gonzalez,
Abstract:
Learn how to create dashboards that surface key performance metrics in real time using the power of SQL Server and PowerBI. You will learn how to collect information for various data sources to publish data related to revenue, expenses, sales forecasts and human resources, all from one easy to use dashboard.
Speaker(s): Patrick Thompson,
Abstract:
There are several programming design patterns that work great in object oriented code but do not scale inside SQL Server. Things like Scalar-Value functions, Unions, CTEs, and row by row processing can make for more work then you would expect. In this session, we will focus on several anti-patterns like these and show how to rework them to improve scale in the database engine. By the end of the session, you will be able to quickly identify these common anti-patterns and analyze their sometimes unclear cost to the engine. Heading back to the office, you’ll be able to identify pain points in your longest running queries from a T-SQL perspective and become a code tuning rockstar!
Speaker(s): William Wolf,
Abstract:
Join us for a fun game of Sparkhound-themed Jeopardy, where we will choose three contestants from the audience for high-stakes game where your answers must be in the form of a query. The audience can win prizes too if our brave contestants can’t. Learn some PowerShell, SQL, and trivia while you’re at it!
Speaker(s): William Assaf,
Abstract:
PowerShell is always something you heard about and wanted to use but never did! Starting with the resources available for a PowerShell newbie, we will advance to what pro's use to automate their database activities and beyond, for both DBA's and developers. The goal of this session is to get you immersed in PowerShell. Ask questions and learn how PowerShell can enable you and more importantly do your job for you (the way, future is heading). We will also look at free libraries and tools to help you advance your automation abilities. Common automation scenarios is the theme! Anything done with with clicks can be automated.
Speaker(s): Jana Sattainathan,
Abstract:
With so many languages around to extract, transform, analyze, and visualize data, Microsoft introduced yet another language : U-SQL. In this session, we'll take a look at what U-SQL is, what it brings to the table, and how it differs from (or overlaps with) the existing constructs (T-SQL, SQL CLR, DAX/MDX, PowerQuery/M, R etc.,). We'll go over step-by-step in getting started with U-SQL, and review some use case scenarios.
Speaker(s): Azhagappan Arunachalam,
Abstract:
The importance of proper table design cannot be underestimated. Misconceptions could lead into serious problems not only in terms of database performance, but also it can effect disk space management and interactivity of user interface as well. In this session the audience will have a sensible walk-through of database and table design in detail, encompassing a number of key areas of focus ranging from Data Types to FILESTREAM implementation. Discuss most common a table design mistakes. Find out efficiency of one type over another, IDENTITY vs. SEQUENCE vs. GUID to support PK uniqueness, pros and cons of using composite Primary Key and SPARSE column, and more.
Speaker(s): Alex Grinberg,
Abstract:
Join Microsoft bloggers Chris Skorlinski, ReplTalk and Curt Mathews, SQLAlwaysOn in great debate, Replication v. AlwaysOn. They’ll go head to head on topics such as High Availability, Disaster Recovery, Performance, and Customization. Bring your own questions and join the discussion to help learn how these technologies can best be used in your company or organization.
Speaker(s): Chris Skorlinski,
Abstract:
Microsoft introduced the APPLY operator in SQL Server 2005. This under-used, non-standard operator can help us solve a number of common (and not-so-common) problems with elegant code which performs well. We will look several problem spaces in which the APPLY operator shines, such as joining to table-valued functions, getting the top N child records for each parent record, string splitting, and word matching. With the use of these examples, we will gain a deeper understanding of when to use the APPLY operator.
Speaker(s): Kevin Feasel,
Abstract:
A ground-floor introduction to SQL Server permissions starting with the basics and moving into the security implications behinds stored procedures, views, database ownership, application connections, consolidated databases, application roles and more. This class is perfect for DBA's, developers and system admins.
Speaker(s): William Assaf,
Abstract:
Getting a good 360 degree view of organizations (customers or vendors) or doing customer centric analysis is often complicated as a result of bad data/ duplicate data / inconsistent data coming from multiple systems. In addition, there are external factors like mergers and acquisitions, inter-company relationships and other nuances that may not be captured in source systems. Getting a clear picture of customer and vendor data can not only provide a clear picture for analysis of information, but also aid in critical risk management functions. In this session, you will learn about some techniques and data services from DB that you can use to clean and enhance your data.
Speaker(s): Rushabh Mehta,
Abstract:
We will go through Clustered and Non Clustered index internals. How Indexes are allocated and how SQL Server uses indexes in Select operations. In that session we will use DBCC IND and DBCC PAGE and dig into pages' binary code to understand how SQL Server uses different kinds of Indexes.
Speaker(s): Slava Murygin,
Abstract:
Make your SSIS Life easy by learning SSIS administration. There is lots of new stuff to learn in SSIS 2012 and 2014, to name a few: SSIS Catalog, Environmental Parameters, Encrypting passwords. We will cover new upcoming features in 2016.
Speaker(s): Ravi Kumar,
Abstract:
Join us for a practical look at the components of Cortana Intelligence Suite for information management, data storage, analytics, and visualization. Purpose, capabilities, and use cases for each component of the suite will be discussed. Expertise required for implementation of each component will be covered as well. If you are a technology professional who is involved with delivering business intelligence, analytics, data warehousing, or big data utilizing Azure services, this technical overview will help you gain familiarity with the components of Cortana Intelligence Suite and its potential for delivering value.
Speaker(s): Melissa Coates,
Abstract:
Microsoft’s Visual Studio 2013 provides a great way for database project teams to manage databases within the Software Development Lifecycle. Learn how it enables you to create, manage, source control, schema compare, and deploy database objects. In this presentation, I will break down the feature set of database development in Visual Studio 2013 then walk through a demo and offer some tips for SQL database developers to effectively manage their databases.
Speaker(s): Luke Donley,
Abstract:
Data breaches abound! Sql Server has several options to encrypt the data, Transparent Data Encryption, Cell Level Encryption and Sql Server 2016 Always Encrypted. Let’s review each option to hide the encrypt the data. Let’s also look at new options to hide data with Always Encrypted.
Speaker(s): Thomas Norman,
Abstract:
Azure SQL Database, No CDC No Problem! Come see how I solved that lack of CDC in SQL Azure Database by leveraging a new feature in SQL Azure database and SQL Server 2016- Temporal tables. In this in depth 300/400 level session I will demonstrate the techniques that I created to simulate CDC in SQL Azure Database. We will examine the code that implements the solution, the code that automatically writes code for you and a wizard that makes it simple to consume.
Speaker(s): boB Taylor,
Abstract:
If some of your databases have grown to over 1TB and it has become a challenge to maintain them - or if you are anticipating what to do in the future when they do - then this talk is for you! In this session, I will provide some practical advice on managing VLDBs with SQL server, focusing on common problem areas for new and experienced DBAs alike, including index maintenance, integrity checks, archival, backups and restores.
Speaker(s): Brian Carrig,
Abstract:
You’ve probably heard that it’s a data integration best practice to build small, unit-of-work SSIS packages. You’ve heard that with Biml you can create a lot of SSIS packages in very little time. You follow this advice and end up with dozens of SSIS packages. Now what? A metadata-driven SSIS Execution Framework will simplify the execution of several SSIS packages down to a simple, one-line stored procedure call. But then, how does one manage all the metadata required to populate the SSIS Execution Framework? Biml to the rescue!
Speaker(s): Andy Leonard,
Abstract:
This session will feature a real story of a customer who was running into performance and scaling issues on a big iron Oracle RAC system and why they decided to consider the magic of the Column Store for this situation. Discussion will focus on the existing environments for testing both SQL 2016 and Oracle as well as the methodologies used for testing. We will examine the decisions made for choosing indexes (both traditional and column store) for the main tables in the data warehouse as well as the partitioning strategies employed. We will wrap up the discussion with the results of the testing and where they are now.
Speaker(s): Rick Heiges,
Abstract:
T-SQL is one of a minority of computer languages where you describe the results rather than code an algorithm. Unfortunately, many people use habits and constructions learned in imperative languages such as C# or Java when writing T-SQL Code. In this all-demo session we will explore several such commonly used constructions that are detrimental to SQL Server performance and scalability.
Speaker(s): Geoff Hiten,
Abstract:
Recovery in the cloud is now easier and more convenient than ever. This session describes some common scenarios and reviews the steps needed to prepare for a disaster with a recovery in Azure. We will focus on the IAAS offerings.
Speaker(s): Carlos L Chacon,
Abstract:
This presentation covers massive waves of change throughout the analytics industry. We discuss how to keep track of key changes, planning or adapting your analytics career, and then we explore many "blue ocean" career opportunities in a new world of data. Cool topics such as Virtual Reality, Robotics, Automation, Predictive and Prescriptive Analytics, Internet of Things, Big Data, Cloud and Hybrid are explored.
Speaker(s): Jen Underwood,
Abstract:
The breadth of options available to data professionals today is staggering: between relational, object, graph, and document databases, we have a wide array of storage options, as well as a huge number of access and analysis tools. This talk will walk you through data platform, data analysis, and data processing options. We’ll discuss when to use (and when not to use) Spark, R, Storm, and SSAS, as well as a range of other tools and technologies. By the end of this talk, you may not have MongoDB or Neo4J, but you will know where they might fit.
Speaker(s): Kevin Feasel,
Abstract:
This session will take a look at the new Query Store feature in SQL Server 2016. Query Store tracks changes in execution plans, allowing you to easily view performance differences and revert to older plans with a few clicks of the mouse. We will take a hands on look at how to use Query Store to troubleshoot problems as well as some of the new DMVs you can use to find issues as they arise.
Speaker(s): Tracy Boggiano,
Abstract:
Let’s face it, using SQLPS prior to SQL Server 2016 was like installing training wheels on a Ducati. Then, some community feedback was incorporated before SQL 2016 shipped and presto, SQLPS is actually useful all of a sudden. There is a faster way to manage and even SQL Servers using PowerShell. I will show you what the new features in SQLPS and why you will probably find it useful, even if you aren't a "DBA".
Speaker(s): Aaron Nelson,
Abstract:
With the introduction of the SSIS Catalog in SQL Server 2012, we can now manage our SSIS package execution and monitor performance and logs in a much easier manner than executing packages from the file system. In this session, we'll walk through setting permissions for SSIS read-only access, executing packages from T-SQL or stored procedures, viewing the logs generated during a package execution, writing custom messages to the logs, and many other tips to make your use of the catalog easier!
Speaker(s): Jonathan Buller,
Abstract:
From the basics to the advanced, we will explore techniques that will ensure you leave a positive impression and help you land that role!
Speaker(s): Leveat Ruse,
Abstract:
We will start with the importance of backup for a database administrator. The second part will cover backup/recovery strategy and the three backup types including a demo. The last part will cover the other techniques not considered as a normal backup process but can be used for it like attach/detach, import/export, replication and disaster recovery (log shipping).
Speaker(s): Thomas Chan,
Abstract:
This session will focus on the brand new SQL Server 2016 feature called PolyBase. PolyBase#160;is a technology that accesses and combines both non-relational and relational data, all from within SQL Server. It allows you to run queries on external data in Hadoop or Azure blob storage. The queries are optimized to push computation to Hadoop. In this session, you will learn how to install and set up PolyBase, and how to use it once you've got it configured.
Speaker(s): Sean Werick,
Abstract:
This session will consist of two 15 minute quot;Lightning Talksquot; from two different presenters: Geoff Hiten amp; William Assaf.
Speaker(s): Angela Henry,
This is a list of speakers from the XML Guidebook records. The details and URLs were valid at the time of the event.
LinkedIn: https://www.linkedin.com/in/williamjwolf
Contact: http://www.sqlwarewolf.com
Bill is the Manager of SQL Server Performance Tuning for RDX. He works with over 400 customers on solutions within the SQL Server environment and specialized in code tuning and mentoring the team within RDX. He has become a SQL Saturday veteran speaking at nearly 40 SQL Saturdays in the last 3 years. He has also spoken at Summit and is an Idera Ace class of 2018. Bill is active in the Pittsburgh SQL Server User Group. He is also the chapter lead for the DevOps Virtual User Group. He is passionate about educating users on SQL Server and mentoring others.
LinkedIn: https://www.linkedin.com/in/adammachanic
Contact: http://sqlblog.com/blogs/adam_machanic
Adam Machanic is a Boston-based SQL Server developer, writer, and speaker. He focuses on large-scale data warehouse performance and development, and is the author of the award-winning SQL Server monitoring stored procedure, quot;sp_WhoIsActive.quot; He has contributed to several books on SQL Server, including quot;T-SQL Queryingquot; (Microsoft Press, 2015). He is a Microsoft Most Valuable Professional (MVP) for SQL Server.
LinkedIn: https://www.linkedin.com/pub/kevin-feasel/7/716/504
Contact: http://www.catallaxyservices.com
Kevin Feasel is a Microsoft Data Platform MVP and CTO at Envizage, where he specializes in data analytics with T-SQL and R, forcing Spark clusters to do his bidding, fighting with Kafka, and pulling rabbits out of hats on demand. He is the lead contributor to Curated SQL (https://curatedsql.com), president of the Triangle Area SQL Server Users Group (https://www.meetup.com/tripass), and author of PolyBase Revealed (https://www.apress.com/us/book/9781484254608). A resident of Durham, North Carolina, he can be found cycling the trails along the triangle whenever the weather's nice enough.
LinkedIn: https://www.linkedin.com/in/sqlvariant
Contact: http://sqlvariant.com/
Aaron Nelson is an author, public speaker, blogger, and leader in the community. He has over 15 years of experience in architecture, Business Intelligence, development, and performance tuning of SQL Server. Aaron has received many certifications and is a six time winner of the Microsoft MVP Award. Aaron leads the PowerShell Virtual Group of PASS (SQLPS.io), is a board member of PowerShell.org, and is a volunteer at his PASS Local Group, AtlantaMDF.
LinkedIn: https://www.linkedin.com/in/carloslchacon
Contact: http://sqldatapartners.com/blog
Carlos Chacon is the managing partner of SQL Data Partners LLC and co-host of the popular SQL Data Partners Podcast. While getting his start as a typical accidental DBA, Carlos has sat on Microsoft exam review panels, served as regional mentor and chapter leader. He enjoys traveling and has been to four continents, speaks Spanish and can eat his weight in raspberries (not yet proven). He and his family live in Richmond, Virginia.
LinkedIn: http://www.linkedin.com/in/melissacoatesprofile
Contact: https://www.CoatesDataStrategies/blog
Melissa Coates is a data architect with a background in data warehousing and business intelligence. Her current professional focus is enterprise-level Power BI governance, deployment, security, and administration. As the owner of Coates Data Strategies, Melissa produces training and consults to help companies strengthen and sustain their data-driven initiatives. Melissa is big supporter of the technical community, and has been a Microsoft Data Platform MVP since 2013. You can visit Melissa's website at CoatesDataStrategies.com, which includes community resources like diagrams, blogs, and videos.
LinkedIn: https://www.linkedin.com/in/williamjwolf
Contact: http://www.sqlwarewolf.com
Bill is the Manager of SQL Server Performance Tuning for RDX. He works with over 400 customers on solutions within the SQL Server environment and specialized in code tuning and mentoring the team within RDX. He has become a SQL Saturday veteran speaking at nearly 40 SQL Saturdays in the last 3 years. He has also spoken at Summit and is an Idera Ace class of 2018. Bill is active in the Pittsburgh SQL Server User Group. He is also the chapter lead for the DevOps Virtual User Group. He is passionate about educating users on SQL Server and mentoring others.
I got my start with SQL Server almost 15 years ago when I was tasked with converting Access databases to SQL Server based applications. I have been working with SQL Server and SSIS ever since! I am now focused on designing and implementing Data Warehouse solutions with multiple complex data sources. I lead a team of 4 other ETL developers, serving as the architect and Senior ETL developer, and work closely with the business to develop warehouse and analytic solutions.
LinkedIn: http://www.linkedin.com/in/angelahenrydba
Contact: http://SQLSwimmer.com
Angela is a Solution Architect, living in High Point, NC and loves what she does. She's worked with all versions of SQL Server worn all the hats that come with dealing with SQL Server throughout the years: developer, administrator, data architect and BI developer. She has her MSCE in Data and Analytics (f.k.a., Business Intelligence), is a Data Platform MVP, and a Microsoft Certified Trainer (MCT). She is the former chapter leader for the PASS Triad SQL User Group in Greensboro, NC. In her spare time you can probably find her in or at the pool, she's an avid US Masters Swimmer, Coach and Instructor.
Chris Skorlinski has been with Microsoft for 17 years. He is a Support Escalation Engineer at the Microsoft Charlotte NC office specializing in performance tuning and troubleshooting Replication. His is a contributor to SQLShare.com training as well as his own BLOGs on Replication Performance at http://blogs.msdn.com/ReplTalk/. #160;
LinkedIn: http://www.linkedin.com/pub/bill-anton/12/526/4bb
Contact: http://byobi.com/blog/
Bill Anton is an expert in Analysis Services and an experienced data warehouse practitioner. In 2013, he founded Opifex Solutions, a consulting firm with deep expertise in enterprise-scale architecture, design, and performance optimization of Analysis Services and Power BI solutions. He loves eating and spends most of his free time convincing his beloved wife to adopt more golden retrievers.
LinkedIn: https://www.linkedin.com/in/luke-donley-7aa7799
Luke Donley is a senior data and analytics consultant at CapTech with over 10 years of data management experience. He has spent much of his career working in Microsoft SQL Server environments designing, building, and delivering SQL Server data warehouses and analytical applications. Luke specializes in data strategy, data architecture, data integration, and business intelligence.
LinkedIn: https://www.linkedin.com/in/rushabhbmehta
Contact: http://blogs.solidq.com/RushabhMehta
Rushabh Mehta is a Business Intelligence Mentor with SolidQ and the Managing Director for Solid Quality India Pvt. Ltd. He served as PASS President for 2010/2011 term. Rushabh has worked with MS Data Platform since 1998 and has designed and developed BI systems since 2001. He has been the lead architect for many large-scale and complex BI solutions for clients such as Publix, Raymond James, Jackson Hewitt, US Dept. of Defense, and Microsoft. He is an MVP with Microsoft, Damp;B amp; Melissa Data
LinkedIn: http://www.linkedin.com/pub/bill-anton/12/526/4bb
Contact: http://byobi.com/blog/
Bill Anton is an expert in Analysis Services and an experienced data warehouse practitioner. In 2013, he founded Opifex Solutions, a consulting firm with deep expertise in enterprise-scale architecture, design, and performance optimization of Analysis Services and Power BI solutions. He loves eating and spends most of his free time convincing his beloved wife to adopt more golden retrievers.
I have over 12 years of experience working with SQL Server and Business Intelligence Technology. My current focus is cloud technologies: Power BI, Azure Analysis Services, Azure SQL Data Warehouse, Azure Data Factory. As a PFE at Microsoft, I regularly deliver 2 - 4 day training workshops.
LinkedIn: http://www.linkedin.com\in\sqlbobt
Contact: http://aka.ms\sqlbobt
In his 45-year IT career, boB Taylor has pretty much done it all – Fortran 66 on Hollerith Cards, assembly language programming on drum-based computers, and LOB application development. He served as a VP of Software Development and was Director of Database Technologies for Avanade West Region. Then he joined Microsoft’s Partner Advantage practice, advising ISVs on effectively using SQL Server. After becoming a Microsoft Certified Architect, boB moved to PFE and is now with the Oracle Compete Team. Besides passing 40+ certification exams on the first attempt, boB helped develop both SQL Server 2000 certification exams, SQL Server 2005 developer exams, and all MCSD.NET exams.
Az Arunachalam has been working with SQL server since v4.21/SQLNT. He started his career with troubleshooting at chip level, worked as a network engineer managing Novell NetWare networks, graduated into managing Windows NT, moved onto development, administered databases, and is currently working as a database architect. He#39;s excited about all the cloud offerings that Microsoft and others have made possible, and in sharing his findings with aspiring data enthusiasts.
LinkedIn: https://www.linkedin.com/in/williamdassaf/
Contact: http://www.sqltact.com
William Assaf, Data Platform MVP, is a principal consultant in Baton Rouge, LA. Initially a .NET developer, and later into database administration and architecture, William currently works with clients on SQL Server and Azure SQL platform optimization, management, disaster recovery and high availability, and leads a crack multi-city team of consulting SQL DBA's. William has written for Microsoft SQL certification exams since 2011 and was the lead author of the 2017 and 2019 editions of "SQL Server Administration Inside Out" by Microsoft Press. William is a member of the Baton Rouge User Groups board, a regional mentor for PASS, and past lead of the annual SQLSaturday Baton Rouge Planning Committee, having run one of the largest SQLSaturdays.
LinkedIn: https://www.linkedin.com/in/williamdassaf/
Contact: http://www.sqltact.com
William Assaf, Data Platform MVP, is a principal consultant in Baton Rouge, LA. Initially a .NET developer, and later into database administration and architecture, William currently works with clients on SQL Server and Azure SQL platform optimization, management, disaster recovery and high availability, and leads a crack multi-city team of consulting SQL DBA's. William has written for Microsoft SQL certification exams since 2011 and was the lead author of the 2017 and 2019 editions of "SQL Server Administration Inside Out" by Microsoft Press. William is a member of the Baton Rouge User Groups board, a regional mentor for PASS, and past lead of the annual SQLSaturday Baton Rouge Planning Committee, having run one of the largest SQLSaturdays.
Konstantin has over 15 years of SQL Server experience and demonstrates his expertise and commitment to helping SQL Server DBAs succeed through his position as Director of Service Delivery at RDX. In his role, Konstantin is responsible for designing and implementing RDX’s service delivery strategies, including those for SQL Server. Since joining RDX in 2009, Konstantin has proven to be a strong leader and expert in SQL Server database administration, architecture, performance tuning, and business intelligence. He is also a very active member of the SQL Server community and frequently presents at SQL Saturdays and user groups throughout the year. In addition, Konstantin presented during 24 Hours of PASS and at the PASS Summits.
LinkedIn: https://www.linkedin.com/in/geoffhiten
Geoff Hiten is a Technical Solutions Professional at Microsoft. Geoff began working with SQL Server in 1992 with version 4.2 and has used every version since. He specializes in highly available SQL systems, performance tuning, and systems migration. Geoff is heavily involved in the Microsoft SQL Server Community and was initially awarded MVP status in 2004. Geoff was appointed in 2011 to fill an interim Director position at the National PASS organization.
LinkedIn: https://www.linkedin.com/in/pacosql/
Paco Gonzalez is the CEO of SolidQ North America, and a Microsoft Data Platform MVP. Focused on Business Analytics and Artificial Intelligence, he specializes in helping organizations become data driven from a strategic and technical perspective. Paco is a speaker at small and large conferences such as PASS Summit, Ignite, and Business Applications Summit, and he has published several books and whitepapers. He is based in Atlanta, GA.
LinkedIn: https://www.linkedin.com/in/williamdassaf/
Contact: http://www.sqltact.com
William Assaf, Data Platform MVP, is a principal consultant in Baton Rouge, LA. Initially a .NET developer, and later into database administration and architecture, William currently works with clients on SQL Server and Azure SQL platform optimization, management, disaster recovery and high availability, and leads a crack multi-city team of consulting SQL DBA's. William has written for Microsoft SQL certification exams since 2011 and was the lead author of the 2017 and 2019 editions of "SQL Server Administration Inside Out" by Microsoft Press. William is a member of the Baton Rouge User Groups board, a regional mentor for PASS, and past lead of the annual SQLSaturday Baton Rouge Planning Committee, having run one of the largest SQLSaturdays.
LinkedIn: https://www.linkedin.com/in/leveatruse
As an Executive Technical Recruiter with Ctrl+f, I currently develop effective recruitment strategies to attract top talent at all levels of an organization. I am an IBM certified trainer and began my career performing general human resources duties. I quickly worked my way up through the ranks of technical recruiting from Lead Recruiter to Senior Recruiter to Business Development.
LinkedIn: http://www.linkedin.com/pub/rick-heiges/1/b93/777/
Contact: http://sqlblog.com/blogs/rick_heiges/default.aspx
Rick is a Former Data Platfrom MVP and Sr. Consultant for Microsoft where he focuses on working with customers on their data estate. His career includes work as a DBA, University full-time faculty member, Systems Analyst, Project Leader, and Developer. He has found his passion in working with data and the community. Rick served nine years on the PASS Board of Directors and spearheaded the popular 24 Hours of PASS event. He also started his local User Group in 2002.
LinkedIn: http://www.linkedin.com/in/AndyLeonard
Contact: https://andyleonard.blog
Andy Leonard is founder and Chief Data Engineer at Enterprise Data Analytics, Microsoft Data Platform MVP, creator of the DILM (Data Integration Lifecycle Management) Suite, an SSIS trainer, consultant, developer, Business Intelligence Markup Language (Biml) developer and BimlHero, SQL Server database and data warehouse developer, community mentor, engineer, and farmer. He is a co-author of "The Biml Book and SQL Server Integration Services Design Patterns" and author of "Managing Geeks - A Journey of Leading by Doing", "Data Integration Lifecycle Management with SSIS", "Building Custom Tasks for SSIS", and the "Stairway to Integration Services". Andy blogs at andyleonard.blog where you can learn more on the About Andy page.
LinkedIn: https://www.linkedin.com/in/geoffhiten
Geoff Hiten is a Technical Solutions Professional at Microsoft. Geoff began working with SQL Server in 1992 with version 4.2 and has used every version since. He specializes in highly available SQL systems, performance tuning, and systems migration. Geoff is heavily involved in the Microsoft SQL Server Community and was initially awarded MVP status in 2004. Geoff was appointed in 2011 to fill an interim Director position at the National PASS organization.
LinkedIn: https://www.linkedin.com/in/jenunderwood
Contact: http://www.jenunderwood.com
Jen Underwood is a Senior Director at DataRobot. Over the past 20 years, Jen has held worldwide product management roles at Microsoft and served as a technical lead for system implementation firms. She has a unique blend of product management and “hands-on” experience in data warehousing, reporting, visualization, and advanced analytics. In addition to keeping a constant pulse on industry trends, she enjoys digging into oceans of data to solve complex problems with machine learning. Jen has a Bachelor of Business Administration – Marketing, Cum Laude from the University of Wisconsin, Milwaukee and a post-graduate certificate in Computer Science – Data Mining from the University of California, San Diego.
LinkedIn: http://www.linkedin.com/in/armordba
Contact: https://armordba.com/
Tom Norman is a Database Architect at KPA with a strong fervor to protect data. He works daily to review and improve data protection methodologies while reviewing governing laws affecting finance, healthcare, and personal data. His areas of expertise include encryption, auditing, data identification, and database object deployment. He is the current leader of the PASS Virtualization Group and Vice President of the TRIPASS user group. You can read his blog at https://armordba.com/ and reach him on twitter at @armordba. Tom speaks at a number of SQLSaturday events and SQL Server user groups.
LinkedIn: https://www.linkedin.com/in/johnqholtz
For the past 17 years John has served as a technology advocate for his clients, helping guide them on their journey to the modern data center. John is currently a Senior Account Manager for Dell EMC’s "All Flash” Primary Storage Platforms which includes XtremIO, VMAX All Flash and Unity All Flash. Prior, John spent ten years in the storage virtualization space with IBM and another four years as a Mid-Market Account Manager at EMC. In 2014 John left the world of data storage to develop his skills in visual data analytics at Tableau Software. Ultimately, his fondness for the data center led him back to his current role with EMC.
Brian Carrig is a Microsoft Certified Master of SQL Server and is currently a Senior Program Manager on the SQL Server Tiger Team at Microsoft focusing primarily on persistent memory technology and replication. In a previous life, Brian spent some time as an academic and holds a PhD in Computer Science. He is a native of Dublin, Ireland but now lives with his wife and two daughters in Redmond, Washington. If he is not talking about, thinking about or working with technology you will usually find him watching Chelsea FC.
LinkedIn: https://www.linkedin.com/in/alex-grinberg-36773a6/
I have more than 20 years of IT experience. My primary focus is with the latest Microsoft technologies including .NET (VB and C#), SSRS and SSIS. I provide services in tuning, optimization, analysis and development for the creation of new applications, converting legacy technologies (SQL Server, VB.NET and C# ) and site training. I have worked for CSC, VerticalNet, IDP, LSAC , and TMNAS (to name a few) and I am currently employed with Cox Automotive. I am Apress author book "XML and JSON Recipes for SQL Server", also wrote number of articles for Simple-Talk and SQLServerCentral.com
LinkedIn: https://www.linkedin.com/pub/slava-murygin/9/b95/858
Contact: http://slavasql.blogspot.com/
Started working with SQL Server 7.0 as DBA and Developer. Since then went through all SQL Server versions, migrations and multiple companies of different sizes and industries. Currently working as a contractor with all aspects of SQL Server, including, but not limited to: troubleshooting performance, designing/developing: databases, systems, processes, data warehouses, cubes, reports, ETLs; Refactoring old code; Migrating data from other systems to SQL Server; Securing the data on enterprise level.
LinkedIn: https://www.linkedin.com/in/pawarvishal
Contact: http://bimentalist.com/
Vishal Pawar is a Microsoft Data Platform MVP, Microsoft Certified Trainer (MCT), founder of the Global Power BI User Group and co-leader of the PASS Business Analytics Virtual Group. He is a Principal BI Architect in Green House Data, with decades of experience in BI solution architecture and best practices. He has published more than 1000 articles since 2011 in SQL and BI. Vishal teaches a free Power BI course through Udemy, which has more than 65K students from the glob. Vishal is an exceptional technology community of leaders worldwide who actively share their high quality, real-world expertise with the technical communities.
LinkedIn: https://www.linkedin.com/pub/kevin-feasel/7/716/504
Contact: http://www.catallaxyservices.com
Kevin Feasel is a Microsoft Data Platform MVP and CTO at Envizage, where he specializes in data analytics with T-SQL and R, forcing Spark clusters to do his bidding, fighting with Kafka, and pulling rabbits out of hats on demand. He is the lead contributor to Curated SQL (https://curatedsql.com), president of the Triangle Area SQL Server Users Group (https://www.meetup.com/tripass), and author of PolyBase Revealed (https://www.apress.com/us/book/9781484254608). A resident of Durham, North Carolina, he can be found cycling the trails along the triangle whenever the weather's nice enough.
LinkedIn: https://www.linkedin.com/in/patrick-thompson-749a8659
Patrick Thompson is a Partner and General Manager of Sparkhound, overseeing operations and business development and growing Sparkhound’s footprint in Louisiana, Arkansas, Alabama, Mississippi and Florida. With more than 27 years of experience, he has held senior leadership positions at Fortune 500 and 1000 companies, including Accenture, Turner Industries, CBamp;I (formerly the Shaw Group) and Amedisys. In his spare time, he enjoys movie time and hanging on the beach with his family.
LinkedIn: https://www.linkedin.com/in/anders-pedersen-993676
Contact: http://blog.AndersOnSQL.com
20+ years of experience with SQL Server, from version 6.0 to 2017. Funnily enough, the only certification I have is on SQL 2012, the one version I have never used. Mostly worked at medium sized companies, by choice. Was a consultant for 6+ years, working on anything from facility security systems, background checks, retail PoS systems, to major financial systems. Passion for performance, and teaching people easy ways to better SQL performance.
LinkedIn: https://www.linkedin.com/pub/chiu-yuen-chan/b/a67/b07
I work with SQL Server since 2001 with version 7.
LinkedIn: https://www.linkedin.com/pub/janakiraman-jana-sattainathan/a/a07/b95
Contact: http://www.sqljana.wordpress.com
Jana works as a Senior Oracle and SQL Server DBA at SCOR, a leading Reinsurance company in the world. He has spent most of his professional life around databases. At work, he has automated most aspects of his job using PowerShell. Most recently, he created PowerPump, a “PowerShell Database Copy Server” for his company to copy 50 billion rows (> 20 TB,) from 4000+ of Oracle/SQL Server tables on a recurring basis to SQL Server targets for migration/testing. He has also fully automated the database deployment which supports all components of SQL Server and Oracle using PowerShell (with about 150 functions) that is tracked extensively.
LinkedIn: https://www.linkedin.com/in/seanwerick
Contact: http://www.seanwerick.com
I have been in the data industry for 15 years, specializing in Microsoft business intelligence for the last decade. I have architected, developed and integrated end to end solutions for customers in various sectors including finance, government, health care and retail. I am extremely passionate about data and the endless possibilities for companies utilizing it in the best ways. I enjoy data science and business intelligence because the possibilities and magnitudes of impact are immeasurable.
LinkedIn: https://www.linkedin.com/in/tracyboggiano
Contact: http://databasesuperhero.com
Tracy Boggiano is a Senior Database Administrator for DocuSIgn. She has spent over 20 years in IT, using SQL Server since 1999, and is currently certified as an MCSE Data Platform. Tracy has worked on SQL Server since 6.5. She has spoken at local user groups and numerous SQLSaturdays. She is currently a co-leader of a TriPASS Local Group in Raleigh, NC. Tracy also tinkered with databases in middle school to keep her sports card collection organized. She blogs at databasesuperhero.com. Her passion outside of SQL Server is volunteering with foster children as their advocate in court through volunteerforgal.org and being a mental health advocate as part of the PAIMI NC Advisory Council.
The following is a list of sponsors that helped fund the event.


When pagination is enabled, the data is displayed in pages that can be browsed using the built in pagination selector.
For external pagination, implement the gridApi.pagination.on.paginationChanged
callback function. The callback
may contain code to update any pagination state variables your application may utilize, e.g. variables containing
the pageNumber
and pageSize
. The REST call used to fetch the data from the server should be called from within
this callback. The URL of the call should contain query parameters that will allow the server-side code to have
sufficient information to be able to retrieve the specific subset of data that the client requires from the
entire set.
It should also update the $scope.gridOptions.totalItems
variable with the total count of rows that exist (but
were not all fetched in the REST call mentioned above since they exist in other pages of data).
This will allow ui-grid to calculate the correct number of pages on the client-side.
For better performance with the following example, you can choose to load the ui-grid.core.js and specific feature files instead:
<script src="/release/ui-grid.core.min.js"></script> <script src="/release/ui-grid.pagination.min.js"></script>
This shows combined external pagination and sorting.


AddressClaim
public struct AddressClaim : Codable
Struct representing an AddressClaim as defined in the OpenID specs.
-
Full mailing address, formatted for display or use on a mailing label. This field MAY contain multiple lines, separated by newlines. Newlines can be represented either as a carriage return/line feed pair (“\r\n”) or as a single line feed character (“\n”).
Declaration
Swift
public var formatted: String?
-
Full street address component, which MAY include house number, street name, Post Office Box, and multi-line extended street address information. This field MAY contain multiple lines, separated by newlines. Newlines can be represented either as a carriage return/line feed pair (“\r\n”) or as a single line feed character (“\n”).
Declaration
Swift
public var street_address: String?
-
City or locality component.
Declaration
Swift
public var locality: String?
-
State, province, prefecture, or region component.
Declaration
Swift
public var region: String?
-
Zip code or postal code component.
Declaration
Swift
public var postal_code: String?
-
Country name component.
Declaration
Swift
public var country: String?