randomizr is a port of the R package randomizr for Stata that simplifies the design and analysis of randomized experiments. In particular, it makes the random assignment procedure transparent, flexible, and most importantly reproduceable. By the time that many experiments are written up and made public, the process by which some units recieved treatments is lost or imprecisely described. The randomizr package makes it easy for even the most forgetful of researchers to generate error-free, reproduceable random assignments.
A hazy understanding of the random assignment procedure leads to two main problems at the analysis stage. First, units may have different probabilities of assignment to treatment. Analyzing the data as though they have the same probabilities of assignment leads to biased estimates of the treatment effect. Second, units are sometimes assigned to treatment as a cluster. For example, all the students in a single classroom may be assigned to the same intervention together. If the analysis ignores the clustering in the assignments, estimates of average causal effects and the uncertainty attending to them may be incorrect.
Throughout this vignette, we'll pretend we're conducting an experiment among the 592 individuals in R's HairEyeColor dataset. As we'll see, there are many ways to randomly assign subjects to treatments. We'll step through five common designs, each associated with one of the five randomizr functions: simple_ra, complete_ra, block_ra, cluster_ra, and block_and_cluster_ra.
Typically, researchers know some basic information about their subjects before deploying treatment. For example, they usually know how many subjects there are in the experimental sample (N), and they usually know some basic demographic information about each subject.
Our new dataset has 592 subjects. We have three pretreatment covariates, Hair, Eye, and Sex, which describe the hair color, eye color, and gender of each subject. We also have potential outcomes. We call the untreated outcome Y0 and we call the treated outcome Y1.
. clear all
. use HairEyeColor
(Written by R. )
. des
Contains data from HairEyeColor.dta
obs: 592 Written by R.
vars: 6 28 Aug 2017 01:24
size: 18,944
------------------------------------------------------------------------------------------
storage display value
variable name type format label variable label
------------------------------------------------------------------------------------------
Hair long %9.0g Hair Hair
Eye long %9.0g Eye Eye
Sex long %9.0g Sex Sex
Y1 double %9.0g Y1
Y0 double %9.0g Y0
id float %9.0g
------------------------------------------------------------------------------------------
Sorted by:
. list in 1/5
+---------------------------------------------------+
| Hair Eye Sex Y1 Y0 id |
|---------------------------------------------------|
1. | Black Brown Male -2.983882 -14.98388 1 |
2. | Black Brown Male 6.616561 -5.383439 2 |
3. | Black Brown Male 4.711323 -7.288677 3 |
4. | Black Brown Male -.2332402 -12.23324 4 |
5. | Black Brown Male 1.940893 -10.05911 5 |
+---------------------------------------------------+
. set seed 324437641
Imagine that in the absence of any intervention, the outcome (Y0) is correlated with out pretreatment covariates. Imagine further that the effectiveness of the program varies according to these covariates, i.e., the difference between Y1 and Y0 is correlated with the pretreatment covariates.
If we were really running an experiment, we would only observe either Y0 or Y1 for each subject, but since we are simulating, we have both. Our inferential target is the average treatment effect (ATE), which is defined as the average difference between Y0 and Y1.
Simple random assignment assigns all subjects to treatment with an equal probability by flipping a (weighted) coin for each subject. The main trouble with simple random assignment is that the number of subjects assigned to treatment is itself a random number - depending on the random assignment, a different number of subjects might be assigned to each group.
The simple_ra function has no required arguments. If no other arguments are specified, simple_ra assumes a two-group design and a 0.50 probability of assignment.
. simple_ra Z
. tab Z
Z | Freq. Percent Cum.
------------+-----------------------------------
0 | 294 49.66 49.66
1 | 298 50.34 100.00
------------+-----------------------------------
Total | 592 100.00
To change the probability of assignment, specify the prob argument:
. simple_ra Z, replace prob(.3)
. tab Z
Z | Freq. Percent Cum.
------------+-----------------------------------
0 | 423 71.45 71.45
1 | 169 28.55 100.00
------------+-----------------------------------
Total | 592 100.00
If you specify num_arms without changing prob_each, simple_ra will assume equal probabilities across all arms.
. simple_ra Z, replace num_arms(3)
. tab Z
Z | Freq. Percent Cum.
------------+-----------------------------------
1 | 186 31.42 31.42
2 | 193 32.60 64.02
3 | 213 35.98 100.00
------------+-----------------------------------
Total | 592 100.00
You can also just specify the probabilites of your multiple arms. The probabilities must sum to 1.
. simple_ra Z, replace prob_each(.2 .2 .6)
. tab Z
Z | Freq. Percent Cum.
------------+-----------------------------------
1 | 138 23.31 23.31
2 | 110 18.58 41.89
3 | 344 58.11 100.00
------------+-----------------------------------
Total | 592 100.00
You can also name your treatment arms.
. simple_ra Z, replace prob_each(.2 .2 .6) condition_names(control placebo treatment)
. tab Z
Z | Freq. Percent Cum.
------------+-----------------------------------
control | 105 17.74 17.74
placebo | 119 20.10 37.84
treatment | 368 62.16 100.00
------------+-----------------------------------
Total | 592 100.00
Complete random assignment is very similar to simple random assignment, except that the researcher can specify exactly how many units are assigned to each condition.
The syntax for complete_ra is very similar to that of simple_ra. The argument m is the number of units assigned to treatment in two-arm designs; it is analogous to simple_ra's prob. Similarly, the argument m_each is analogous to prob_each.
If you specify no arguments in complete_ra, it assigns exactly half of the subjects to treatment.
. complete_ra Z, replace
. tab Z
Z | Freq. Percent Cum.
------------+-----------------------------------
0 | 296 50.00 50.00
1 | 296 50.00 100.00
------------+-----------------------------------
Total | 592 100.00
To change the number of units assigned, specify the m argument:
. complete_ra Z, m(200) replace
. tab Z
Z | Freq. Percent Cum.
------------+-----------------------------------
0 | 392 66.22 66.22
1 | 200 33.78 100.00
------------+-----------------------------------
Total | 592 100.00
If you specify multiple arms, complete_ra will assign an equal (within rounding) number of units to treatment.
. complete_ra Z, num_arms(3) replace
. tab Z
Z | Freq. Percent Cum.
------------+-----------------------------------
1 | 197 33.28 33.28
2 | 197 33.28 66.55
3 | 198 33.45 100.00
------------+-----------------------------------
Total | 592 100.00
You can also specify exactly how many units should be assigned to each arm. The total of m_each must equal N.
. complete_ra Z, m_each(100 200 292) replace
. tab Z
Z | Freq. Percent Cum.
------------+-----------------------------------
1 | 100 16.89 16.89
2 | 200 33.78 50.68
3 | 292 49.32 100.00
------------+-----------------------------------
Total | 592 100.00
You can also name your treatment arms.
. complete_ra Z, m_each(100 200 292) replace condition_names(control placebo treatment)
. tab Z
Z | Freq. Percent Cum.
------------+-----------------------------------
control | 100 16.89 16.89
placebo | 200 33.78 50.68
treatment | 292 49.32 100.00
------------+-----------------------------------
Total | 592 100.00
When should you use simple_ra versus complete_ra? Basically, if the number of units is known beforehand, complete_ra is always preferred, for two reasons: 1. Researchers can plan exactly how many treatments will be deployed. 2. The standard errors associated with complete random assignment are generally smaller, increasing experimental power. See this guide on EGAP for more on experimental power.
Since you need to know N beforehand in order to use simple_ra(), it may seem like a useless function. Sometimes, however, the random assignment isn't directly in the researcher's control. For example, when deploying a survey exeriment on a platform like Qualtrics, simple random assignment is the only possibility due to the inflexibility of the built-in random assignment tools. When reconstructing the random assignment for analysis after the experiment has been conducted, simple_ra() provides a convenient way to do so.
To demonstrate how complete_ra() is superior to simple_ra(), let's conduct a small simulation with our HairEyeColor dataset.
. local sims=1000
. matrix simple_ests=J(`sims',1,.)
. matrix complete_ests=J(`sims',1,.)
. forval i=1/`sims' {
. local seed=32430641+`i'
. set seed `seed'
. qui simple_ra Z_simple, replace
. qui complete_ra Z_complete, replace
. qui tempvar Y_simple Y_complete
. qui gen `Y_simple' = Y1*Z_simple + Y0*(1-Z_simple)
. qui gen `Y_complete' = Y1*Z_complete + Y0*(1-Z_complete)
. qui reg `Y_simple' Z_simple
. qui matrix simple_ests[`i',1]=_b[Z_simple]
. qui reg `Y_complete' Z_complete
. qui matrix complete_ests[`i',1]=_b[Z_complete]
. }
The standard error of an estimate is defined as the standard deviation of the sampling distribution of the estimator. When standard errors are estimated (i.e., by using the summary() command on a model fit), they are estimated using some approximation. This simulation allows us to measure the standard error directly, since the vectors simple_ests and complete_ests describe the sampling distribution of each design.
. mata: st_numscalar("simple_var",variance(st_matrix("simple_ests")))
. mata: st_numscalar("complete_var",variance(st_matrix("complete_ests")))
. disp "Simple RA S.D.: " sqrt(simple_var)
Simple RA S.D.: .62489587
. disp "Complete RA S.D.: "sqrt(complete_var)
Complete RA S.D.: .60401434
In this simulation complete random assignment led to a
. disp round(((simple_var) - (complete_var))/(simple_var)*100, 2)
6
% decrease in sampling variability. This decrease was obtained with a small design tweak that costs the researcher essentially nothing.
Block random assignment (sometimes known as stratified random assignment) is a powerful tool when used well. In this design, subjects are sorted into blocks (strata) according to their pre-treatment covariates, and then complete random assignment is conducted within each block. For example, a researcher might block on gender, assigning exactly half of the men and exactly half of the women to treatment.
Why block? The first reason is to signal to future readers that treatment effect heterogeneity may be of interest: is the treatment effect different for men versus women? Of course, such heterogeneity could be explored if complete random assignment had been used, but blocking on a covariate defends a researcher (somewhat) against claims of data dredging. The second reason is to increase precision. If the blocking variables are predicitive of the outcome (i.e., they are correlated with the outcome), then blocking may help to decrease sampling variability. It's important, however, not to overstate these advantages. The gains from a blocked design can often be realized through covariate adjustment alone.
Blocking can also produce complications for estimation. Blocking can produce different probabilities of assignment for different subjects. This complication is typically addressed in one of two ways: "controlling for blocks" in a regression context, or inverse probabilitity weights (IPW), in which units are weighted by the inverse of the probability that the unit is in the condition that it is in.
The only required argument to block_ra is block_var, which is a variable that describes which block a unit belongs to. block_var can be a string or numeric variable. If no other arguments are specified, block_ra assigns an approximately equal proportion of each block to treatment.
. block_ra Z, block_var(Hair) replace
. tab Z Hair
| Hair
Z | Black Brown Red Blond | Total
-----------+--------------------------------------------+----------
0 | 54 143 35 64 | 296
1 | 54 143 36 63 | 296
-----------+--------------------------------------------+----------
Total | 108 286 71 127 | 592
For multiple treatment arms, use the num_arms argument, with or without the condition_names argument
. block_ra Z, block_var(Hair) num_arms(3) replace
. tab Z Hair
| Hair
Z | Black Brown Red Blond | Total
-----------+--------------------------------------------+----------
1 | 36 95 24 43 | 198
2 | 36 96 24 42 | 198
3 | 36 95 23 42 | 196
-----------+--------------------------------------------+----------
Total | 108 286 71 127 | 592
. block_ra Z, block_var(Hair) condition_names(Control Placebo Treatment) replace
. tab Z Hair
| Hair
Z | Black Brown Red Blond | Total
-----------+--------------------------------------------+----------
Control | 36 96 23 42 | 197
Placebo | 36 95 23 43 | 197
Treatment | 36 95 25 42 | 198
-----------+--------------------------------------------+----------
Total | 108 286 71 127 | 592
block_ra provides a number of ways to adjust the number of subjects assigned to each conditions. The prob_each argument describes what proportion of each block should be assigned to treatment arm. Note of course, that block_ra still uses complete random assignment within each block; the appropriate number of units to assign to treatment within each block is automatically determined.
. block_ra Z, block_var(Hair) prob_each(.3 .7) replace
. tab Z Hair
| Hair
Z | Black Brown Red Blond | Total
-----------+--------------------------------------------+----------
0 | 75 201 49 88 | 413
1 | 33 85 22 39 | 179
-----------+--------------------------------------------+----------
Total | 108 286 71 127 | 592
For finer control, use the block_m_each argument, which takes a matrix with as many rows as there are blocks, and as many columns as there are treatment conditions. Remember that the rows are in the same order as seen in tab block_var, a command that is good to run before constructing a block_m_each matrix. The matrix can either be defined using the matrix define command or be inputted directly into the block_m_each option.
. tab Hair
Hair | Freq. Percent Cum.
------------+-----------------------------------
Black | 108 18.24 18.24
Brown | 286 48.31 66.55
Red | 71 11.99 78.55
Blond | 127 21.45 100.00
------------+-----------------------------------
Total | 592 100.00
. matrix define block_m_each=(78, 30\186, 100\51, 20\87,40)
. matrix list block_m_each
block_m_each[4,2]
c1 c2
r1 78 30
r2 186 100
r3 51 20
r4 87 40
. block_ra Z, replace block_var(Hair) block_m_each(block_m_each)
. tab Z Hair
| Hair
Z | Black Brown Red Blond | Total
-----------+--------------------------------------------+----------
0 | 30 100 20 40 | 190
1 | 78 186 51 87 | 402
-----------+--------------------------------------------+----------
Total | 108 286 71 127 | 592
. block_ra Z, replace block_var(Hair) block_m_each(78, 30\186, 100\51, 20\87,40)
. tab Z Hair
| Hair
Z | Black Brown Red Blond | Total
-----------+--------------------------------------------+----------
0 | 30 100 20 40 | 190
1 | 78 186 51 87 | 402
-----------+--------------------------------------------+----------
Total | 108 286 71 127 | 592
Clustered assignment is unfortunate. If you can avoid assigning subjects to treatments by cluster, you should. Sometimes, clustered assignment is unavoidable. Some common situations include:
Clustered assignment decreases the effective sample size of an experiment. In the extreme case when outcomes are perfectly correlated with clusters, the experiment has an effective sample size equal to the number of clusters. When outcomes are perfectly uncorrelated with clusters, the effective sample size is equal to the number of subjects. Almost all cluster-assigned experiments fall somewhere in the middle of these two extremes.
The only required argument for the cluster_ra function is the clust_var argument, which indicates which cluster each subject belongs to. Let's pretend that for some reason, we have to assign treatments according to the unique combinations of hair color, eye color, and gender.
. egen clust_var=group(Hair Eye Sex)
. tab clust_var
group(Hair |
Eye Sex) | Freq. Percent Cum.
------------+-----------------------------------
1 | 32 5.41 5.41
2 | 36 6.08 11.49
3 | 11 1.86 13.34
4 | 9 1.52 14.86
5 | 10 1.69 16.55
6 | 5 0.84 17.40
7 | 3 0.51 17.91
8 | 2 0.34 18.24
9 | 53 8.95 27.20
10 | 66 11.15 38.34
11 | 50 8.45 46.79
12 | 34 5.74 52.53
13 | 25 4.22 56.76
14 | 29 4.90 61.66
15 | 15 2.53 64.19
16 | 14 2.36 66.55
17 | 10 1.69 68.24
18 | 16 2.70 70.95
19 | 10 1.69 72.64
20 | 7 1.18 73.82
21 | 7 1.18 75.00
22 | 7 1.18 76.18
23 | 7 1.18 77.36
24 | 7 1.18 78.55
25 | 3 0.51 79.05
26 | 4 0.68 79.73
27 | 30 5.07 84.80
28 | 64 10.81 95.61
29 | 5 0.84 96.45
30 | 5 0.84 97.30
31 | 8 1.35 98.65
32 | 8 1.35 100.00
------------+-----------------------------------
Total | 592 100.00
. cluster_ra Z_clust, cluster_var(clust_var)
. tab clust_var Z_clust
group(Hair | Z_clust
Eye Sex) | 0 1 | Total
-----------+----------------------+----------
1 | 32 0 | 32
2 | 36 0 | 36
3 | 11 0 | 11
4 | 9 0 | 9
5 | 0 10 | 10
6 | 5 0 | 5
7 | 0 3 | 3
8 | 2 0 | 2
9 | 53 0 | 53
10 | 0 66 | 66
11 | 50 0 | 50
12 | 0 34 | 34
13 | 0 25 | 25
14 | 0 29 | 29
15 | 0 15 | 15
16 | 14 0 | 14
17 | 0 10 | 10
18 | 0 16 | 16
19 | 0 10 | 10
20 | 0 7 | 7
21 | 0 7 | 7
22 | 7 0 | 7
23 | 7 0 | 7
24 | 7 0 | 7
25 | 0 3 | 3
26 | 4 0 | 4
27 | 30 0 | 30
28 | 64 0 | 64
29 | 5 0 | 5
30 | 0 5 | 5
31 | 0 8 | 8
32 | 0 8 | 8
-----------+----------------------+----------
Total | 336 256 | 592
This shows that each cluster is either assigned to treatment or control. No two units within the same cluster are assigned to different conditions.
As with all functions in randomizr, you can specify multiple treatment arms in a variety of ways:
. cluster_ra Z_clust, cluster_var(clust_var) num_arms(3) replace
. tab clust_var Z_clust
group(Hair | Z_clust
Eye Sex) | 1 2 3 | Total
-----------+---------------------------------+----------
1 | 32 0 0 | 32
2 | 36 0 0 | 36
3 | 0 0 11 | 11
4 | 9 0 0 | 9
5 | 10 0 0 | 10
6 | 0 0 5 | 5
7 | 0 3 0 | 3
8 | 0 0 2 | 2
9 | 0 53 0 | 53
10 | 66 0 0 | 66
11 | 0 50 0 | 50
12 | 0 34 0 | 34
13 | 0 0 25 | 25
14 | 0 0 29 | 29
15 | 0 15 0 | 15
16 | 14 0 0 | 14
17 | 0 0 10 | 10
18 | 0 0 16 | 16
19 | 0 10 0 | 10
20 | 0 7 0 | 7
21 | 7 0 0 | 7
22 | 7 0 0 | 7
23 | 0 0 7 | 7
24 | 0 0 7 | 7
25 | 3 0 0 | 3
26 | 0 4 0 | 4
27 | 0 30 0 | 30
28 | 64 0 0 | 64
29 | 0 0 5 | 5
30 | 0 5 0 | 5
31 | 0 0 8 | 8
32 | 0 8 0 | 8
-----------+---------------------------------+----------
Total | 248 219 125 | 592
...or using condition_names.
. cluster_ra Z_clust, cluster_var(clust_var) condition_names(control placebo treatment) replace
. tab clust_var Z_clust
group(Hair | Z_clust
Eye Sex) | 1 2 3 | Total
-----------+---------------------------------+----------
1 | 32 0 0 | 32
2 | 0 0 36 | 36
3 | 0 0 11 | 11
4 | 0 0 9 | 9
5 | 10 0 0 | 10
6 | 0 0 5 | 5
7 | 3 0 0 | 3
8 | 0 2 0 | 2
9 | 0 53 0 | 53
10 | 0 66 0 | 66
11 | 50 0 0 | 50
12 | 34 0 0 | 34
13 | 25 0 0 | 25
14 | 0 29 0 | 29
15 | 0 0 15 | 15
16 | 0 14 0 | 14
17 | 0 0 10 | 10
18 | 0 16 0 | 16
19 | 0 10 0 | 10
20 | 7 0 0 | 7
21 | 0 7 0 | 7
22 | 0 7 0 | 7
23 | 7 0 0 | 7
24 | 0 0 7 | 7
25 | 3 0 0 | 3
26 | 0 0 4 | 4
27 | 0 0 30 | 30
28 | 64 0 0 | 64
29 | 5 0 0 | 5
30 | 0 5 0 | 5
31 | 0 0 8 | 8
32 | 0 8 0 | 8
-----------+---------------------------------+----------
Total | 240 217 135 | 592
... or using m_each, which describes how many clusters should be assigned to each condition. m_each must sum to the number of clusters.
. cluster_ra Z_clust, cluster_var(clust_var) m_each(5 15 12) replace
. tab clust_var Z_clust
group(Hair | Z_clust
Eye Sex) | 1 2 3 | Total
-----------+---------------------------------+----------
1 | 0 32 0 | 32
2 | 0 0 36 | 36
3 | 0 0 11 | 11
4 | 0 0 9 | 9
5 | 0 10 0 | 10
6 | 5 0 0 | 5
7 | 0 0 3 | 3
8 | 0 2 0 | 2
9 | 0 53 0 | 53
10 | 0 66 0 | 66
11 | 0 0 50 | 50
12 | 0 0 34 | 34
13 | 0 0 25 | 25
14 | 0 0 29 | 29
15 | 0 15 0 | 15
16 | 0 0 14 | 14
17 | 0 0 10 | 10
18 | 0 0 16 | 16
19 | 0 10 0 | 10
20 | 0 7 0 | 7
21 | 0 7 0 | 7
22 | 7 0 0 | 7
23 | 0 7 0 | 7
24 | 7 0 0 | 7
25 | 0 3 0 | 3
26 | 4 0 0 | 4
27 | 0 30 0 | 30
28 | 0 0 64 | 64
29 | 0 5 0 | 5
30 | 5 0 0 | 5
31 | 0 8 0 | 8
32 | 0 8 0 | 8
-----------+---------------------------------+----------
Total | 28 263 301 | 592
The power of clustered experiments can sometimes be improved through blocking. In this scenario, whole clusters are members of a particular block -- imagine villages nested within discrete regions, or classrooms nested within discrete schools.
As an example, let's group our clusters into blocks by size
. bysort clust_var: egen cluster_size=count(_n)
. block_and_cluster_ra Z, block_var(cluster_size) cluster_var(clust_var) replace
. tab clust_var Z
group(Hair | Z
Eye Sex) | 0 1 | Total
-----------+----------------------+----------
1 | 32 0 | 32
2 | 0 36 | 36
3 | 0 11 | 11
4 | 0 9 | 9
5 | 0 10 | 10
6 | 5 0 | 5
7 | 3 0 | 3
8 | 0 2 | 2
9 | 53 0 | 53
10 | 0 66 | 66
11 | 0 50 | 50
12 | 0 34 | 34
13 | 0 25 | 25
14 | 29 0 | 29
15 | 0 15 | 15
16 | 14 0 | 14
17 | 0 10 | 10
18 | 16 0 | 16
19 | 10 0 | 10
20 | 7 0 | 7
21 | 0 7 | 7
22 | 7 0 | 7
23 | 0 7 | 7
24 | 7 0 | 7
25 | 0 3 | 3
26 | 4 0 | 4
27 | 30 0 | 30
28 | 0 64 | 64
29 | 0 5 | 5
30 | 0 5 | 5
31 | 8 0 | 8
32 | 0 8 | 8
-----------+----------------------+----------
Total | 225 367 | 592
. tab cluster_size Z
cluster_si | Z
ze | 0 1 | Total
-----------+----------------------+----------
2 | 0 2 | 2
3 | 3 3 | 6
4 | 4 0 | 4
5 | 5 10 | 15
7 | 21 14 | 35
8 | 8 8 | 16
9 | 0 9 | 9
10 | 10 20 | 30
11 | 0 11 | 11
14 | 14 0 | 14
15 | 0 15 | 15
16 | 16 0 | 16
25 | 0 25 | 25
29 | 29 0 | 29
30 | 30 0 | 30
32 | 32 0 | 32
34 | 0 34 | 34
36 | 0 36 | 36
50 | 0 50 | 50
53 | 53 0 | 53
64 | 0 64 | 64
66 | 0 66 | 66
-----------+----------------------+----------
Total | 225 367 | 592


Authentic Coffee & Churros at Cocoa Cinnamon
DURHAM, N.C. -- Cocoa Cinnamon is a Latin style cafe that offers authentic churros and coffee imported from multiple Latin American countries.
DURHAM, N.C. -- Cocoa Cinnamon is a Latin style cafe that offers authentic churros and coffee imported from multiple Latin American countries.


--- layout: post authors: ["Greg Wilson"] title: Getting Credit date: 2012-10-22 time: "11:00:00" tags: ["Community"] ---
A recurring theme in our discussion with scientists is how hard it is to get academic credit for building software, but there are some hopeful signs. As Carole Goble pointed out to me a couple of weeks ago, Nucleic Acids Researchruns two special issues every year: one devoted to web servers, and one to databases. As the introduction to the former makes clear, the journal's definition of "web servers" is pretty broad:
The 2012 Web Server Issue of Nucleic Acids Research is the 10th in a series of annual special issues dedicated to web-based software resources for analysis and visualization of molecular biology data...The present issue reports on 102 web servers.
I'd be very interested to find out what kinds of software skills the contributors to these special issues have...


Congratulations, you've just installed and built Cards. It wasn't that hard right? Cards is a little, opiniated and pretty simplistic content-management-system. The default theme is inspired by Google's Material-Design guidelines with some little changes here and there.
My name is Leonard Schuetz. I was born in 2000 and live in Switzerland. I'm currently doing an apprentice at Siemens Schweiz AG. You can find more about myself on my personal website, also running on a slightly modified version of cards.
Cards started as my own personal homepage. It featured different categories, a simple to use tag parser and a pretty looking design. I quickly thought about making it accessible to anyone. In this version however, i cut out a lot of features that i felt we're unnecessary for the average user.
For example, i removed the support for my own tag parser, which i hadn't updated since September 2015. I also removed a lot of stuff that i built for myself.
Just launch the setup.php file via the browser or command-line. After everything finished, it will disable itself.
Create a new folder inside the storage/articles folder and name it after your category.
Inside the newly created category folder, create a new folder for your article. PHP sorts these articles based on alphanumeric sorting done by the filesystem, so you should use names similar to this format: 2016_03_16_sometitle.
Next, place a file called article.html inside the newly created directory. This will is a standard html file where you can write your article.
If you want to embed images, videos or other file-system related stuff, use the following syntax: %%THIS%%/image.jpg for the src path.
Inside this file, a div represents a card. You can have multiple cards inside one article. Play around with different tags to see how they are being formatted.


Everybody has been in a meeting, listened to a conference keynote, or sat in a lecture hall, where many words and phrases are constantly repeated. The repetition of these buzzwords can become tiresome and frustrating, making it hard to pay attention, especially when not everyone wants to be there in the first place. This is where Buzzword Bingo comes in to alleviate the boredom.
This iOS app allows you to play Bingo using a custom board of different buzzwords for any topic you want. A solo play option is available for passing the time, but competing with other colleagues or friends in the same meeting through multiplayer is even more fun. Once you get Bingo on the board, in order to complete your win, you are given a random word that you must find a way to say in the meeting. Buzzword Bingo is a great way to liven up every meeting, lecture, and more.


| data-beets {pbkrtest} | R Documentation |
Yield and sugar percentage in sugar beets from a split plot experiment. The experimental layout was as follows: There were three blocks. In each block, the harvest time defines the "whole plot" and the sowing time defines the "split plot". Each plot was 25 m^2 and the yield is recorded in kg. See 'details' for the experimental layout. The data originates from a study carried out at The Danish Institute for Agricultural Sciences (the institute does not exist any longer; it became integrated in a Danish university).
beets
A dataframe with 5 columns and 30 rows.
Experimental plan Sowing times 1 4. april 2 12. april 3 21. april 4 29. april 5 18. may Harvest times 1 2. october 2 21. october Plot allocation: Block 1 Block 2 Block 3 +-----------|-----------|-----------+ Plot | 1 1 1 1 1 | 2 2 2 2 2 | 1 1 1 1 1 | Harvest time 1-15 | 3 4 5 2 1 | 3 2 4 5 1 | 5 2 3 4 1 | Sowing time |-----------|-----------|-----------| Plot | 2 2 2 2 2 | 1 1 1 1 1 | 2 2 2 2 2 | Harvest time 16-30 | 2 1 5 4 3 | 4 1 3 2 5 | 1 4 3 2 5 | Sowing time +-----------|-----------|-----------+
Ulrich Halekoh, Søren Højsgaard (2014)., A Kenward-Roger Approximation and Parametric Bootstrap Methods for Tests in Linear Mixed Models - The R Package pbkrtest., Journal of Statistical Software, 58(10), 1-30., https://www.jstatsoft.org/v59/i09/
data(beets) beets$bh <- with(beets, interaction(block, harvest)) summary(aov(yield ~ block + sow + harvest + Error(bh), beets)) summary(aov(sugpct ~ block + sow + harvest + Error(bh), beets))


Minnesota hunter, 19, with Down syndrome bags his first buck
by Vandana RambaranA family from Twin Cities in Minnesota celebrated a father-son victory after their 19-year-old with Down syndrome shot his first buck over the weekend, an accomplishment made possible by a change in state hunting and firearm legislation.
Steve Pennaz told Fox News on Wednesday that his son Pierce has Down syndrome and has been hunting under his supervision through a state apprenticeship that allowed him to hunt with a parent or guardian for two years. But after the two-year window closed, the Minnesota Department of Natural Resources told Steve that his son would have to take and pass an extensive firearms safety exam in order to continue to hunt.
MICHIGAN HUNTERS USE BRANCH SAW TO FREE 2 BUCKS AFTER ANTLERS BECOME LOCKED DURING FIGHT
The eight-week course and 100-question exam were too arduous for someone with Pierce's condition, Steve said.
"There was just no way with Pierce having Down syndrome that he would pass the test," Steve said. "Even if he could pass the test, I didn't want him to be able to hunt legally without direct supervision."
Steve's concerns that Pierce might not be able to hunt again -- a family tradition -- caused him to reach out to Rep. Jim Nash in January 2018. Nash eventually championed the cause to change the laws and extend the apprenticeship program.
"He felt there was a need for a law for people with disabilities to embrace hunting," Steve said of Nash. "He cared about it because I cared about it."
MICHIGAN HUNTER USE BRANCH SAW TO FREE 2 BUCKS AFTER ANTLERS BECOME LOCKED DURING FIGHT
Finally, on Saturday at 5 a.m., on a weekend hunting trip and after lots of preparation that included prior practice shooting pellets at targets and examining photos of deers, Pierce saw a deer in the woods.
"The deer was running and it stopped at about 120 yards in front of us," Steve said.
"[Pierece] said, 'I see the deer.' I asked him if he wanted it and he said 'yes,'" Steve said. His son took the shot and nabbed the buck.
"I got him! I got him! I did it! I did it!" Pierce had said, according to his father.
"I think we continue as a society to underestimate what people with disabilities can do," Steve said. "He's an amazing young man, he's done amazing things and this is just another chapter that he's accomplished in terms of breaking boundaries."
MONTANA HUNTER REPORTS KILLY GRIZZLY BEAR IN SELF-DEFENSE
Nash said he's "very happy to see that Pierce was able to harvest a buck after the bill we passed removed an unrealistic limitation on him."
"My hope is that this bill will allow many special needs hunters to spend time in the field with their parent or someone who takes them hunting," he continued. "Getting the picture from Pierce’s dad was such a thrill, I hope to see many more.”
CLICK HERE FOR THE FOX NEWS APP
"I see Pierce as an example to others," Steve added. "Don't underestimate these very important people. "


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Contents
classdef (Abstract) IoPort < handle
% IOPORT Interface for the I/O ports on Zaber controllers. % This class defines the general-purpose interface for the % analog and digital I/O ports found on some Zaber devices. % % See also Zaber.Device.IO % Author: Zaber Technologies Software Team<EMAIL_ADDRESS>Public instance properties
properties (SetAccess = protected) % ANALOGINPUTCOUNT The number of analog inputs available. AnalogInputCount % ANALOGOUTPUTCOUNT The number of analog outputs available. AnalogOutputCount % DIGITALINPUTCOUNT The number of digital input bits available. DigitalInputCount % DIGITALOUTPUTCOUNT The number of digital output bits available. DigitalOutputCount end
Protected instance properties
properties (Access = protected) % DEVICE The device this set of ports is associated with. Device end
Public instance methods
methods (Abstract) % READANALOGINPUT Read the value of an analog input. % value = io.READANALOGINPUT(index); % % index - The 1-based index of the analog input to read. % value - The voltage read on the specified input. % % Will throw an error if the index is out of range or there is a % communication error. % % See also AnalogInputCount, writeanalogoutput, readanalogoutput, % readdigitalinput value = readanaloginput(obj, aIndex) % WRITEANALOGOUTPUT Set the value of an analog output % io.WRITEANALOGOUTPUT(index, value); % % index - 1-based index of the port to write to. % value - New voltage to output. % % NOTE no devices currently support analog outputs. This method % will always throw an error. % % See also AnalogOutputCount, readanalogoutput, readanaloginput, % writedigitaloutput writeanalogoutput(obj, aIndex, aValue) % READANALOGOUTPUT Read back the value of an analog output. % value = io.READANALOGOUTPUT(index); % % index - The 1-based index of the analog output to read. % value - The voltage currently being output on the specified port. % % NOTE no devices currently support analog outputs. This method % will always throw an error. % % See also AnalogOutputCount, writeanalogoutput, readanaloginput, % readdigitalinput value = readanalogoutput(obj, aIndex) % READDIGITALINPUT Read one or more digital input bits. % bits = io.READDIGITALINPUT(); % bit = io.READDIGITALINPUT(index); % % index - Optional 1-based index of the bit to read. If not given, % the output will be an array of bits starting with the % lowest-numbered input bit. If specified, the output will % be a single bit value. % % See also DigitalInputCount, writedigitaloutput, % readdigitaloutput, readanaloginput bits = readdigitalinput(obj, aIndex) % WRITEDIGITALOUTPUT Set the value of one or more digital output bits. % io.WRITEDIGITALOUTPUT(startIndex, bits); % % startIndex - 1-based index of the first bit to write. % bits - Array of bit values to write. The first entry will % be written to the output bit corresponding to % startIndex, end next entry to the next higher % numbered output bit, and so on. % % See also DigitalOutputCount, readdigitaloutput, readdigitalinput, % writeanalogoutput writedigitaloutput(obj, aStartIndex, aBits) % READDIGITALOUTPUT Read back one or more digital output bits. % bits = io.READDIGITALOUTPUT(); % bit = io.READDIGITALOUTPUT(index); % % index - Optional 1-based index of the bit to read. If not given, % the output will be an array of bits starting with the % lowest-numbered output port bit. If specified, the output % will be a single bit value. % % See also DigitalOutputCount, writedigitaloutput, % readdigitalinput, readanaloginput bits = readdigitaloutput(obj, aIndex) end
Protetcted instance methods
methods (Access = protected) function obj = IoPort(aDevice) % IOPORT Initializes properties to their default values. % This constructor is meant to be called by subclass constructors % only. obj.Device = aDevice; obj.AnalogInputCount = 0; obj.AnalogOutputCount = 0; obj.DigitalInputCount = 0; obj.DigitalOutputCount = 0; end end
end


Main Page Namespace List Class Hierarchy Alphabetical List Compound List File List Namespace Members Compound Members File Members Related Pages
libpqxx Class Hierarchy
This inheritance list is sorted roughly, but not completely, alphabetically:
Generated on Sat Jun 7 02:58:05 2003 for libpqxx by
1.3-rc3


Autocomplete - Test Case 2
Test Case - Twitter typeahead
Implementation details
Based on Twitter's typeahead.js.
Results
Colour blindness
Awaiting result
Colour contrast
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Inverted Colours
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Increased font size
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Hit area
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Keyboard only (chrome)
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Screen readers
Jaws 16 / IE 11
Awaiting result
Jaws 13 / IE 9
Awaiting result
NVDA / Firefox
Awaiting result
VoiceOver / Safari (OS X)
Awaiting result
VoiceOver / Safari (iOS 9.2.1)
Awaiting result
Talkback / Firefox (Android Lollipop)
Awaiting result
Screen Magnifier / ZoomText
Awaiting result
Speech Recognition / Dragon
Awaiting result
Valid markup
Awaiting result
Progressive Enhancement
No Javascript
Awaiting result
No Javascript and no CSS
Awaiting result


Dashboard
cam001
Status :
Online
Uptime :
45m 32s
Last Detection :
3m ago
Camera Info
General
Height: ${WIDTH}px
Width: ${HEIGHT}px
FrameRate: ${FRAMERATE}
VerticalFlip : ${VFLIP}
HorizontalFlip : ${HFLIP}
Saturation
Saturation
Hue
Contrast
Brigtness
Motion Detection
Detection Threshold
Timeband
DeadBand
Live Stream
Status
Connected Clients
MQTT Connection
EndPoint
Certificate
Last Message At
Security
Status
Last Login
Logs Encrypted


Pierre DUGUE
Full stack developper
Abstract
French software engineer, passionate by new technologies, photography, drawing and guitare. I have a 4 years experience in full stack development technologies like Java/J2EE, but I am also very interested in new Javascript framework such as angularJS. I enjoy going on meetups, learning and talking about my favorite hobbies (code, guitare music...).
My skills
My skills
Coding and software skills
- Java/J2EE
- Spring
- Javascript
- AngularJS
- HTML/CSS
- Photoshop/Illustrator
- Eclipse
Languages
- French : mother tong
- English : fluent
My experience
Docapost
March 2014 - October 2015: Developer for DOCAPOST
(Digital arm of LA POSTE group. DOCAPOST propose a range of offers for the managing of professional document exchange). Part of a team, I was working on severals “from scratch” projects.
More informations on my resume, or on desktop version.
MGEN : big health care project in Java/J2EE and Liferay with a team of 15 peoples. Implementation of major functionalities and bug correction. LR COPRO : electronic registered letters application in Java/J2EE. I was in charge of both front-end (responsive design using Bootstrap 3 and Thymeleaf) and back-end (using Java/J2EE, with Spring 3, Hibernate and SpringBoot) in a team of 3 peoples. NEWTON : Successfully developed a proof of concept for a connected objects project in Java/J2EE and Grails.
Capgemini
Septembre 2011 - Jully 2013: Software engineer for CAPGEMINI
(Cap Gemini is a French multinational management consulting corporation headquartered in Paris, France. It provides IT services and is one of the world's largest consulting, outsourcing and professional services companies with 180,000 employees in over 40 countries)
More informations on my resume, or on desktop version.
Working on a Tierce Maintenance Applicative (TMA) for VIRBAC (Virbac is a French company dedicated exclusively to animal health). My role was to debug several applications from different technologies such as Java/J2EE, Lotus Script and Windev. - Customer relationship management : Elaborate evolutions for existing applications with the client. Keep the client aware of the progression.
My formation
2011 ESIL (Ecole supérieur d’ingénieur de Luminy): Master degree in IT, network and multimedia. Major 6 months project: Develop a car renting web application in Java/J2EE
2008 IUT R&T: HND in Network and telecommunications
My hobbies
I am passionated about graphics. I draw on Intuos 4L tablet, and use Photoshop CS6. I am also interested in photography (Canon EOS 550D with a SIGMA 18-250) and I play guitar (2 electrics Fender/Washburn and a folk guitar Fender). Don't hesitate to visit my DeviantArt gallery to see my work!


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Provides a way to group multiple animation effects together, similar to GroupEffect.
When animated it synchronises effects to play one after the other has finished, as per
var group = new SequenceEffect(effects);
var effects = [KeyframeEffect, GroupEffect, SequenceEffect /*, ... */];
/// Constructing and playing a SequenceEffect made of two effects
var element1 = document.querySelector('.animate1');
var element2 = document.querySelector('.animate2');
var keyframes = [ { opacity: 1 }, { opacity: 0 } ]; // Fade Out
var timings = { duration: 500 };
var effects = [
new KeyframeEffect(element1, keyframes, timings),
new KeyframeEffect(element2, keyframes, timings)
];
var group = new SequenceEffect(effects );
document.timeline.play(group);


handleException() - Can either be registered as fallback exception handler with set_exception_handler for more friendly uncatched Exceptions or you can pass an Exception directly to it, like in the handleRequest Method
handleRequest() - This is where the heavy lifting in the Front Controller is done. Routes the request, loads the command, throws an exception if something goes wronng and triggers events for you to handle them.


Event Location:
Nova Southeastern University
3301 College Ave
Davie, Florida
Abstract:
There is runaway growth in the data volumes many organizations are facing today. The bad news is that much of this data is unstructured which means your traditional RDBMS just isn't capability of helping you deal with it. As a result significant emphasis has been put on technologies like Hadoop, No SQL and other distributed databases which are better suited to handling unstructured data. With the latest release SQL Server 2012 however, Microsoft has provided new features which will help tame some of this unstructured data. This session will dive into the new FileTable and Statistical Semantic Search features. We will show you how they work and highlight real world examples for integrating these exciting new features into your organization.
Speaker(s): Christopher Price,
Abstract:
In this session I will cover how SSAS tabular and PowerPivot compliment each other and make the slicing and dicing of large amounts of data a breeze for Power Users. I will give a brief explanation and history of each, then will let the audience choose their own adventure in creating a SSAS tabular model or PowerPivot model from scratch. I will walk everyone through how to import data into it, create data relationships from a regular database model, then expose the elements from the model for users to slice and dice data with ease with a Pivot table then a Power View visualization. I will demonstrate a few DAX commands that are very powerful yet very easy.
Speaker(s): Anthony Sammartino,
Abstract:
Databases are growing. The way we use data is changing and growing. When you have a lot of change, it has to be managed, or bad things happen to your data and your job! A common problem with database change management is... database change management. This session walks the user through the concepts of database change management, why it's important, and how SQL Server Data Tools (SSDT) makes this process easy. There are a lot of great features in this frequently overlooked product, and if you are not using it, your job is a lot harder than it needs to be. Some things we will discuss: schema compare, database versions in source control, renaming objects, and how your job is pushing the button to deliver it the EASY way with SSDT.
Speaker(s): James McAuliffe,
Abstract:
Documentation is the bane of every DBA's existence. We all know we should do it but it's time consuming; by the time you're finally done it's probably out of date. Throw in the new servers you just found out about last week and keeping documentation up to date can turn into a full time job. And with everything we have to do in our day, who has time for that? You need a way to work smarter, not harder, and automate your documentation. It can be done, and in this session you'll learn how to discover all the SQL Servers on your network, document their configuration from top to bottom (including OS details), and diagnose common problems before they turn into big headaches - all while you're on a coffee break. You'll look like a rock star without writing a line of code on your own or having to ask management to sign a check. Attend this session and walk away with everything you need to generate documentation for your entire SQL Server environment on your first day back at the office
Speaker(s): Kendal Van Dyke,
Abstract:
This session introduces Data Quality Services, a feature introduced in SQL Server 2012. One of the challenges in developing Business Intelligence solutions is ensuring that the data does not have inconsistencies, incomplete records, invalid or inaccurate values, and data duplication. Sometimes when consolidating data from more than one source you have to transform data to create one standard set of domain values in a standard format. SQL 2012 answers this need with Data Quality Services (DQS). In this session, you will learn the basics of what DQS is and how it can be used to clean your data. You will see how to build a domain both manually and from an existing data table. You will then see how to use these domains to validate tables in SQL Server and Excel preparing them for use in SSAS and PowerPivot. After this session, you will understand how by putting quality back into your data, you can be your organization's BI hero.
Speaker(s): Michael Antonovich,
Abstract:
HIPAA puts a lot of responsibility on our companies and compliance can be hard to maintain. Today medical data is more valuable on the black market than a social security number or a credit card. As DBAs we're charged with the security of our data and thus act as front line defense. In this hour long session We'll delve into the Health Insurance Portability and Accountability Act (HIPAA) and what implications it has for us as data professionals. We'll discuss SQL Server best practices that can help protect ourselves, our company, and the people whom we serve. We'll also dive into features in SQL Server that can help in this endeavor.
Speaker(s): Brandon Leach,
Abstract:
Using BIDS we are able to answer questions in two contradictory worlds the OLTP and the OLAP in order to do that BIDS is presented with the three services SSIS, SSRS and SSAS and a variety of languages such as SQL, T-SQL, MDX, DMX, etc. It is impossible to explain even one aspect of one of the tools that comprise BIDS (Database Tools) even less to explain the main components of the MS BI stack SSIS, SSAS, SSRS in a single presentation. So in this presentation we are trying to explain the mind set of these three services and paradigms from the perspective of the problems they are trying to solve.
Speaker(s): Frank Quintana,
Abstract:
Come and learn how the new SQL Server 2012 feature, ColumnStore Indexes, can parallelize your Data Warehouse aggregations. I will be doing a deep dive into the inner workings of ColumnStore indexes; how they work, key characteristics, dos and don'ts, restrictions, and more. I will have some insight into how CSI's will work in SQL Server vNext, as well. If you are a production DBA who needs to find out more about some of the additional hot features that have been included with CSI's, like batch mode processing and data segments, then this session is for you. If you are a BI DBA or developer who needs to find out how CSI's can increase the performance of your aggregations and star-joins by up to 40 times, then this session is also for you.
Speaker(s): Gareth Swanepoel,
Abstract:
Do SQL Server Administrators or Developers really know how to configure the hard- and software components of a SQL Server in order to have it running at peak performance ? This is not their fault and is purely due to the fact that they have not been exposed to these options before. If you are a DBA or developer and find yourself wondering how hardware changes affect a system or have questions relating to the performance implications of partitioning or compression, then this session is for you. In this session, I will be exploring topics such as Memory configuration, CPU and cache options, IO considerations, partitioning, compression and database file placement and many other performance optimisation tricks.
Speaker(s): Gareth Swanepoel,
Abstract:
This session explores the tabular approach to building cubes in SQL Server Analysis Services 2012 for those DBAs and developers who are more comfortable working with relational databases than cubes. Starting with a ‘normal’ relational database, the attendee will learn how to build a basic model by loading data from several data sources, establish relations between these data sources, define measures and dimensions, build hierarchies, create KPIs and deploy functional cubes that can be used to answer management’s questions about what is really going on with their data. For users already familiar with PowerPivot, this session provides a good transition into the world of SSAS as both use essentially the same techniques.
Speaker(s): Michael Antonovich,
Abstract:
Wondering how all that big data is going to be moved around? Or how you are going to move any of it into your SQL Server environment? Come to this session, and learn about some of the familiar and not so similar tools you can use for moving big data. We’ll compare the options, discuss how they can work with your existing Microsoft technologies, and provide some guidance on when to use each of the tools.
 Are you wondering what opportunities and changes big data may bring to your data warehouse architecture? In this session you will learn about HDinsight, Microsfot’s big data platform, capabilities and tool set. We will address common myths and develop recommendations to successfully bring big data capabilities to your data warehouse and business intelligence implementation. After this presentation you will better prepared to make architectural and functional design decisions when considering Microsoft HDInsight as part of your solution.
Speaker(s): Rafael Salas,
Abstract:
You’ve got your Analysis Services cube created, and deployed in production. However, you notice that every night, the cube is taking longer and longer to process, and users are starting to complain about their data not being ready when they arrive in the morning. If you’ve found yourself in this situation, or want to avoid being in it in the first place, come to this session. We’ll cover how to benchmark processing performance, track down bottlenecks, and how to tune things to get the best performance for processing your cube.
Speaker(s): John Welch,
Abstract:
Text mining is projected to dominate data mining, and the reasons are evident: we have more text available than numeric data. Microsoft introduced a new technology to SQL Server 2012 called Semantic Search. This session's detailed description and demos give you important information for the enterprise implementation of Tag Index and Document Similarity Index. The demos include a web-based Silverlight application, and content documents from Wikipedia. We'll also look at strategy tips for how to best leverage the new semantic technology with existing Microsoft data mining.
Speaker(s): Mark Tabladillo,
Abstract:
As the number of SQL Server versions and features grow over time so does the the difficulty in maintaining a set of tools for working with all of them on your local machine. Add in that some features require multiple physical instances and suddenly a single installation isn't enough. Need to quickly switch between versions and editions of SQL Server? Want to learn how to set up a cluster? Or maybe you want to try out the latest features in SQL 2012...virtualization offers a convenient way to do any or all of these. In this session we'll show you how by covering the fundamentals of desktop virtualization and demonstrating how to set up a virtual playground on your own machine.
Speaker(s): Kendal Van Dyke,
Abstract:
Come and learn some basic technique on how to use PowerShell to work with your XML file. XML files are everywhere and you need to be prepare to use your tools that already are available at your fingertip. In this case, using PowerShell with SMO and Sapien XML editor which is a great tools. Also, I will be demostrating one particular scenario on how to convert UTC XML generated Dates to PowerShell w/SMO and T-SQL DateTime. You will get some PowerShell, T-SQL, and creating/viewing XML schemas in this session.
Speaker(s): Maximo Trinidad,
Abstract:
Unit testing is a widely accepted best practice, yet it is difficult to do well with SSIS packages. This session will focus on both the practice of test driven development as it applies to SSIS, and the tools for actually implementing it. It will cover in depth how to set up your development environment to support test driven development and what techniques can be used to make this practical for SSIS. You will also see how to automate the testing of SSIS packages, using the open source ssisUnit framework. The session will cover several of the common questions about automated unit testing, including how to handle test data , how to set up the environment for multiple developers, and how to make tests flexible.
Speaker(s): John Welch,
Abstract:
Are you looking to create more stability in your professional future in uncertain economic times? Developing a social media platform is a challenge for highend professionals and consultants. The presenter has established a blog (http://marktab.net), cofounded an online journal (http://solidq.com/sqj), become a paid video presenter, secured a spot at Microsoft TechEd, and earned credit toward his first Microsoft MVP. This presentation introduces the basic elements for 2013 of a successful web strategy. The presenter shares his own statistics and first-hand experiences with website development, WordPress blog hosting, leveraging social media services (including Twitter, Linked in, YouTube and Facebook), and working with Microsoft.
Speaker(s): Mark Tabladillo,
Abstract:
This presentation includes a complex business analytics solution using elements across the Microsoft Business Intelligence technology. This talk will not have all the steps spelled out. Therefore you should prepare -- for example, by looking at the SQL Server 2012 Developer Kit -- since attendees will be expected to have working knowledge of SQL Server relational database, SSIS, SSAS, SSRS, SSMS and SSDT. Key elements of this solution will highlight features new to SQL Server 2012. At the end of the demonstration, you will be expected to participate in groups to promote one idea which builds on the demo, and everyone will be voting for their favorite groups. Come ready to create and celebrate your peers.
Speaker(s): Mark Tabladillo,
Abstract:
In the past, High Availability and Disaster Recovery (HADR) had many limitations. Clustering and Mirroring are great, but the mirror/secondary database is not very usable since it is not online. Finally, AlwaysOn solves this limitation by merging both multi-node Clustering and mirroring. AlwaysOn also allows the secondary database to remain ONLINE, so we can use it for reporting purposes without resorting to a Snapshot! Come see this lively session with extensive demos of setting up, configuring and testing AlwaysOn. We'll also test automatic fail-over using a real web app to see how well this feature works.
Speaker(s): Jim Murphy,
Abstract:
This session covers the new features and functions introduced to T-SQL in SQL Server 2012. Our time will be spent reviewing enhancements to T-SQL specifically for SQL Server 2012 with attention to demonstrations and code samples. This is a basic session, but ttendees should have a general knowledge of T-SQL.
Speaker(s): David Dye,
Abstract:
In this session I will cover how PowerPivot and BISM Tabular complement each other in the domains of self-service and corporate BI. Let's face it, The Finance department does not have the expertise to model a database for reporting, and IT doesn't have the expertise in advanced financial calculations that the executive team is interested in. To bridge the gap from a large requirements process with the risk of functionality lost in translation, I will show you how you can roll out a PowerPivot model for business units in your organization and when it makes sense to centralize it in a BISM Tabular model.
Speaker(s): Anthony Sammartino,
Abstract:
With replication being a source for many data warehouse systems, this session will cover aspects regarding your data and business that should drive design decisions when architecting a replication topology. Discussed will be design decisions for snapshot, transactional, and merge replication. Also discussed will be agent profiles and optimizations for replication. What type of replication to use to solve business needs will also be covered.
Speaker(s): Chad Churchwell,
Abstract:
There are many operations performed internally by SQL Server engine that use TempDB besides the objects created by developers (temporary tables or table variables). This session will present situations where SQL Server uses TempDB under the hood, and what can be done to prevent your TempDB to be your bottleneck.
Speaker(s): Sam Mesel,
Abstract:
This session will introduce column level encryption using t-sql. We will discuss the introduction of cryptographic services in SQL Server and how this enables native encryption as well as real life scenarios and uses of column level encryption.
Speaker(s): David Dye,
Abstract:
Cluster MVP David Bermingham will walk you through the process of creating high availability and disaster recovery configurations using the new features of SQL Server 2012 AlwaysOn Availability Groups as well as AlwaysOn Failover Clusters and Multisite Clusters. And then finally David will demonstrate how Availability Groups and Failover Clusters can be used together for a combination of High Availability and Disaster Recovery. This session will make use of VMs to demonstrate the technology in a "hands-on" fashion.
Speaker(s): David Bermingham,
Abstract:
This session focuses on error handling in SQL Server Integration Services 2012. Different techniques and strategies will be introduced for error handling in control flow, data flow, and script tasks as well as how to dynamically handle errors.
Speaker(s): David Dye,
Abstract:
Forget AlwaysOn and Columnstore Indexes the really fun features of SQL Server 2012 are in T-SQL. In this session you'll get the grand tour of these new features. You'll be introduced to the powerful new analytic window functions, discover new ways to generate numeric sequences and explore the awesome new query paging feature. You'll also be enamored with the new error handling features and new string functions such as the awesome Format function. Upon completion you'll have a great understanding of these great new features and how you can utilize them in your environment.
Speaker(s): Richie Rump,
Abstract:
Not every system has a database architect available to design it. But what happens when a database is created the proper care wasn't taken into designing it? This session will take a real-world look into my experiences with some database design choices. We'll take a deep look into these design choices including a detailed discussion on why they didn't work and what could have been done better. We may even take a peek into SQL Server internals. This session is primarily for data professionals that are new to database design.
Speaker(s): Richie Rump,
Abstract:
Let's talk database! SQL Server 2012 IaaS is a new offering, while SQL Database (previously SQL Azure) is a PaaS relational database engine. How do they compare? Which one should you use? Let's discuss pros and cons, and see how easy it is to build a database in the cloud.
Speaker(s): Herve Roggero,
Abstract:
This session will demostrate how you can reuse a PowerShell script in SSIS "Script Task" component as part on a ETL flow. I'll be showing some basic .NET Script code in both C# and VB. I'll be adding some useful tips when re-using existing Powershell code. Integrating different .NET technologies in a SSIS package: C#, VB.NET, XML, and PowerShell. Everyone is welcome to attend.
Speaker(s): Maximo Trinidad,
Abstract:
Take your relational data warehouse query performance all the way to ELEVEN!! The Column Store Index is a new feature that allows for stunning performance. We will cover how it works, why it is so fast as well as the v1.0 limitations and gotchas.
Speaker(s): Kevin Boles,
Abstract:
Are you currently using Master Data Services? if the answer is no, you can't afford to miss this presentation. In this session Luis will provide an overview of MDS 2012. What is it? What can you use it for? Whether you are looking for a better solution for your business users than the common spread-marts or you are implementing a Master Data Management strategy to go along with your EDW, this session is for you.
Speaker(s): Luis Figueroa,
Abstract:
Powered by Fusion-io, Protected by SIOS: Learn how to eliminate the bottlenecks and costs of SAN-based clustering. Leverage the performance of high-speed Fusion ioMemory and achieve higher levels of availability with SIOS. It is the best of both worlds - performance and protection – at a price you can afford. Join us and you could win a $100 American Express gift card.
Speaker(s): Sios Technology and Allan Hirt,
Abstract:
In a world where executives/CXOs are keen to perform analytics and data consolidation with lightning speed, there is a strong push towards incorporating the principles of Agile manifesto in the BI world to deliver solutions faster than ever before. In this session, I will share techniques that will help a consultant deliver BI solutions with a rapid prototyping approach and how Agile features of TFS can be leveraged to run projects. The session will involve case studies and practical demos using Team Foundation Server and SQL Server 2012's BI tools - SSIS, SSRS, SSAS (tabular) and Excel PowerPivot.
Speaker(s): Mohamed Kabiruddin,
Abstract:
This is a wonderful session for those that don't yet grok the three types of joins available to the SQL Server engine or the scenarios for which each join type is optimal. All will be explained here with some very slick visual demonstrations of internal data flow as well as TSQL code samples. We will also show how bad performance can get when suboptimal joins are used.
Speaker(s): Kevin Boles,
Abstract:
In this session I will give a brief history of Self-Service Visualization and some tools out there today that facilitate it. I will then introduce Power View and the two ways it can be used; SharePoint 2010 (or 2013) and now Excel 2013. I will then cover what sources can be used to create a Power View report then jump into a demo and create a few visualizations from a blank canvas to demonstrate how easy, yet powerful this visualization tool really is. I will also show the SSAS Tabular model that houses the data for the Power View reports and will enhance it as part of the session to make Power View shine.
Speaker(s): Anthony Sammartino,
Abstract:
This presentation will focus on the concepts behind AlwaysOn Availability Groups and new improvements to Failover Clusters, benefits it provides, and a demo of setting up AlwaysOn Availability groups
Speaker(s): Chad Churchwell,
Abstract:
On this session you will learn how to find patterns and generic processes to create an ETL framework to automate the most common task within your organization using SSIS, from metadata driven package execution to ETL tracking of file loading processes.
Speaker(s): Jorge Novo,
Abstract:
This presentation describes the tasks a DBA needs to perform to do a great job. Formal training classes and certifications only take you so far; this session talks from experience about everything from preventive maintenance to planning to scaling to communication. Great for beginner to intermediate DBAs, as well as Developers who think this is a direction in which they might want to move.
Speaker(s): Jeffrey Garbus,
Abstract:
Businesses depend on reliable, consistent and correct data that can be managed effectively and shared through-out the enterprise. Master Data Services provides a powerful, feature-rich platform for arriving at this destination but the successful journey to this end requires a proven integrated approach. This session will discuss how MDS fits in the overall EIM architecture within your organization including real-world strategies for integrating (cleansing, matching and loading) Master Data into MDS and for making it available organization-wide.
Speaker(s): Christopher Price,
Abstract:
Learn all you wanted to know about index design but were afraid to ask. This session focuses on physical index structures as well as how the server chooses indexes. Note: Join optimization is a separate session
Speaker(s): Jeffrey Garbus,
Abstract:
The term "big data" dominates the headlines in technology news these days, but what is the fuss really all about? What are the implications for traditional data warehousing and reporting? What kind of technology and skill sets do you need to successfully implement to a big data strategy? Come to this session for a gentle, business-focused introduction to big data and leave with some inspiration and practical steps for tackling your first big data project.
Speaker(s): Stacia Varga,
Abstract:
This fun and informative session covers best practices for data visualization and even infographics using Microsoft BI: Excel, Power View, the Office 2013 Web Apps Framework, Reporting Services, Visio and other open source and third-party data visualizations.
Speaker(s): Jen Underwood,
Abstract:
Learn how to create metadata driven SSIS Packages using .NET code. Create a complex or simple framework to generate SSIS templates for custom configurations and automate most ETL processes. Traverse, modify and audit your current SSIS packages.
Speaker(s): Jorge Novo,
Abstract:
The Microsoft BI Stack provides many different options when it comes to choosing a presentation layer. From PerformancePoint to PowerView the options are plentiful; however each option presents different benefits and is intended for different use cases. In this session I will cover the different presentation options provided by Microsoft and more importantly dive into when and where it is proper to use the different options. I will also demonstrate ways to integrate the different options like including a Reporting Services report in a PerformancePoint dashboard.
Speaker(s): David Ortiz,
Abstract:
In this introductory session we'll delve into the basics of the resource governor feature in SQL Server. We'll look at the types of issues this feature can help resolve. We'll then delve into various components of the feature, such as a classifier function, workload groups and resource pools. Finally we'll bring all this together with an example implementation. From there, if time allows, we'll look at best practices and questions to answer to ensure a successful implementation.
Speaker(s): Brandon Leach,
Abstract:
SharePoint makes it easy for users to create their own dashboards but there's more to dashboards than just fast and easy set-up. This session will explore not only the characteristics of an effective BI dashboard using SharePoint 2013, but also the design flaws to avoid. Attend this session and learn how to perform a dashboard makeover. Starting with a review of a dashboard that uses technology correctly but not as effectively as it could, you’ll explore common problems found in dashboards, learn the characteristics of good dashboard design and discover how to implement a dashboard that communicates information clearly and concisely.
Speaker(s): Stacia Varga,
Abstract:
In this session we'll start the day by a Welcome from Nova's Associate Dean of Information Tecnology followed by a Women in Technology panel discussion with prominent IT women from our community at large, among them we have Jen Underwood and Lesley Frohman, in which we'll be talking about "How it is to be a woman in IT today" and how we can attract and retain more women in this field. Diana Betancourt will be moderating this discussion with a guided Q/A format with interaction from our audience.
Speaker(s): Diana Betancourt,
This is a list of speakers from the XML Guidebook records. The details and URLs were valid at the time of the event.
LinkedIn: http://www.linkedin.com/in/johncwelch/
Contact: http://agilebi.com/jwelch
John Welch joined SentryOne in spring 2018 as VP of Engineering with the acquisition by SentryOne of the software division of Pragmatic Works. John leads the development of a suite of products that make developing, managing, and documenting data solutions easier. John has been successfully delivering IT solutions to business problems since 1994, and has been focused on business intelligence and data warehousing technologies since 2001. He is a Microsoft Most Valued Professional (MVP), and a frequent presenter on SQL Server and data topics. He also contributes to several open source projects and community initiatives targeted toward making data professionals work more productive.
David is a Sergeant with the Cape Coral Police Department and currently works as a database administrator and developer in the Administrative Services Division. He began his career with the police department in 1990 in the patrol division and worked various assignments until being promoted to Sergeant in 1998. Based on his education and experience David was assigned to his current position in 2002 and is responsible for database administration, software integration, and development for public sa
LinkedIn: http://www.linkedin.com/in/johncwelch/
Contact: http://agilebi.com/jwelch
John Welch joined SentryOne in spring 2018 as VP of Engineering with the acquisition by SentryOne of the software division of Pragmatic Works. John leads the development of a suite of products that make developing, managing, and documenting data solutions easier. John has been successfully delivering IT solutions to business problems since 1994, and has been focused on business intelligence and data warehousing technologies since 2001. He is a Microsoft Most Valued Professional (MVP), and a frequent presenter on SQL Server and data topics. He also contributes to several open source projects and community initiatives targeted toward making data professionals work more productive.
David is a Sergeant with the Cape Coral Police Department and currently works as a database administrator and developer in the Administrative Services Division. He began his career with the police department in 1990 in the patrol division and worked various assignments until being promoted to Sergeant in 1998. Based on his education and experience David was assigned to his current position in 2002 and is responsible for database administration, software integration, and development for public sa
LinkedIn: http://www.linkedin.com/profile/view?id=11710667amp;trk=tab_pro
James McAuliffe is a Cloud Solution Architect with CCG Analytics, a Microsoft Gold Partner. He is also a Microsoft Preferred Partner Solutions expert and has been worked with clients of all sizes, from local businesses to Fortune 500 companies. Today’s mix of Big Data and cloud offerings make yesterday’s dream today’s possibility. Let’s talk about it!
Contact: http://mygareth.com
Gareth Swanepoel is a sysadmin-turned-SQL Server DBA. He has been working in the IT industry doing support and administration for over 25 years. He enjoys solving the complex problems that customers encounter when performance tuning SQL Server or when deploying SQL Server in large data warehouse environments. He is originally from South Africa and is currently a Dedicated Support Engineer (PFE) with Microsoft. He is a passionate member of the SQL Server community and is an accomplished author and speaker having presented at various user groups, SQLSaturdays, the PASS Summit and other technical conferences.
LinkedIn: http://www.linkedin.com/in/apsammartino
Contact: http://www.sqlant.me
Anthony Sammartino is the Vice President of the BI Solutions division for TekPartners. Anthony has been building Business Intelligence solutions as a consultant for many fortune 500 companies for over 13 years. For the last 11 years he has almost exclusively used the Microsoft SQL Server BI stack and focused on the following technologies: DB Engine, SSIS, SSAS, SSRS, PowerPivot, Power View, and SharePoint. Anthony currently holds certifications as an: MCP, MCTS(BI), MCITP (BI), and PMP#174;
LinkedIn: http://www.linkedin.com/in/johncwelch/
Contact: http://agilebi.com/jwelch
John Welch joined SentryOne in spring 2018 as VP of Engineering with the acquisition by SentryOne of the software division of Pragmatic Works. John leads the development of a suite of products that make developing, managing, and documenting data solutions easier. John has been successfully delivering IT solutions to business problems since 1994, and has been focused on business intelligence and data warehousing technologies since 2001. He is a Microsoft Most Valued Professional (MVP), and a frequent presenter on SQL Server and data topics. He also contributes to several open source projects and community initiatives targeted toward making data professionals work more productive.
Contact: http://www.rafael-salas.com
Rafael Salas is a recognized speaker and published author with more than 17 years of experience in business intelligence and information architecture. He is the recipient of multiple industry recognition, including the Microsoft MVP Award and The Data Warehouse Institute Best Practices Award. Rafael is also an active member of the SQL Server technical community and blogs regularly at www.rafael-salas.com.
David is a Sergeant with the Cape Coral Police Department and currently works as a database administrator and developer in the Administrative Services Division. He began his career with the police department in 1990 in the patrol division and worked various assignments until being promoted to Sergeant in 1998. Based on his education and experience David was assigned to his current position in 2002 and is responsible for database administration, software integration, and development for public sa
LinkedIn: http://www.linkedin.com/in/FrankQuintanaDataArchitect
Contact: http://www.businessintelligencematters.us
Doctor Quintana has been involved in optimization and math modeling since the ’80s. He is an IT University professor of Data Science, Databases, Data Mining, and Knowledge Discovery. He is a former head of an RD department. He worked as a research engineer at the Memorial University of Newfoundland. He also worked as a consultant in the role of a technical team leader and software architect for EDS responsible for critical parts of the SDLC for multi-million dollar projects. He is one of the Software Architects that designed and built the "Health and Benefits System for the of the Veteran Affairs of Canada" He is a regular speaker at national and international events In 2004 he started his consulting practice in South and Central Flori
Contact: http://mygareth.com
Gareth Swanepoel is a sysadmin-turned-SQL Server DBA. He has been working in the IT industry doing support and administration for over 25 years. He enjoys solving the complex problems that customers encounter when performance tuning SQL Server or when deploying SQL Server in large data warehouse environments. He is originally from South Africa and is currently a Dedicated Support Engineer (PFE) with Microsoft. He is a passionate member of the SQL Server community and is an accomplished author and speaker having presented at various user groups, SQLSaturdays, the PASS Summit and other technical conferences.
LinkedIn: Linkedin: http://www.linkedin.com/pub/max-trinidad/12/47/775/
Contact: http://www.maxtblog.com
Maximo Trinidad (Florida Aka – Mr. PowerShell), currently work as a SAPIEN Technologies Evangelist and specialing in scripting automation on SQL Server Microsoft Cloud Technologies. Also a Microsoft MVP (PowerShell) since 2009 and active speaker since 2007 participating at SQLSaturday, IT Pro .NET Code Camps events.
Contact: http://blog.extremebi.net
Luis Figueroa is an IT professional focused on delivering business solutions that leverage the Microsoft SQL Server platform. His area of expertise is Data Warehousing and Business Intelligence but he is passionate about all things SQL Server. With 10+ years of experience in the IT field, Luis has delivered data management solutions for companies in the Security, Online Marketing and Healthcare Industries. He is also the chapter leader of the MS BI user group of South Florida.
Contact: http://www.sqlha.com
Cluster MVP Allan Hirt is a consultant, published author, speaker, and trainer who has been using Microsoft SQL Server in various guises since 1992. He is currently based in the Boston, MA, area and travels all over the world to work with and train clients. His most recent book is "Pro SQL Server 2008 Failover Clustering" (Apress) and is currently working on its sequel, Mission Critical SQL Server due in 2014.
LinkedIn: http://au.linkedin.com/pub/mohamed-kabiruddin/32/639/8b8/
Contact: http://www.nobrainerlab.com
Mohamed is a Cloud Solutions Architect with extensive experience in designing BI / Data Analytics solutions with expertise in analysis, design, modelling and implementation. He is a keen data enthusiast with a flair for managing and consolidating data.
LinkedIn: http://www.linkedin.com/in/apsammartino
Contact: http://www.sqlant.me
Anthony Sammartino is the Vice President of the BI Solutions division for TekPartners. Anthony has been building Business Intelligence solutions as a consultant for many fortune 500 companies for over 13 years. For the last 11 years he has almost exclusively used the Microsoft SQL Server BI stack and focused on the following technologies: DB Engine, SSIS, SSAS, SSRS, PowerPivot, Power View, and SharePoint. Anthony currently holds certifications as an: MCP, MCTS(BI), MCITP (BI), and PMP#174;
LinkedIn: http://www.linkedin.com/in/kendalvandyke
Contact: http://www.kendalvandyke.com
Kendal is a database strategist, community advocate, public speaker, and blogger. A practiced IT professional with over 15 years of SQL Server experience, Kendal excels at disaster recovery, high availability planning/implementation, amp; debugging/troubleshooting mission critical SQL Server environments. Kendal is a Senior Consultant on the Microsoft Premier Developer Support team and President of MagicPASS (http://magicpass.sqlpass.org), the Orlando, FL based chapter of PASS. Before joining Microsoft, Kendal was a SQL Server/Data Platform MVP from 2011-2016.
LinkedIn: http://www.linkedin.com/in/apsammartino
Contact: http://www.sqlant.me
Anthony Sammartino is the Vice President of the BI Solutions division for TekPartners. Anthony has been building Business Intelligence solutions as a consultant for many fortune 500 companies for over 13 years. For the last 11 years he has almost exclusively used the Microsoft SQL Server BI stack and focused on the following technologies: DB Engine, SSIS, SSAS, SSRS, PowerPivot, Power View, and SharePoint. Anthony currently holds certifications as an: MCP, MCTS(BI), MCITP (BI), and PMP#174;
will be provided later on
LinkedIn: http://www.linkedin.com/in/chadchurchwell
Contact: http://www.sqlchad.com
Seasoned SQL Server DBA Professional with over 18 years IT experience with 15 years experience in databases. I have managed an enterprise DBA team and am currently in a strategic consulting role specializing in HA, DR, Replication, and Performance Tuning and Optimization. I am currently speaking at several SQLSaturday’s as well as local user group meetings.
LinkedIn: https://www.linkedin.com/profile/view?id=176471amp;trk=nav_responsive_tab_profile
Contact: http://mssqlperformance.blogspot.com/
Jeff has been consulting on and performing technical training for MS SQL Server since 1989, Version 4, on OS/2. Since then, he has worked extensively with each release of the product, specializing in performance and tuning, from concept through legacy system, on systems of all sizes, from small start-up company applications to multi-terabyte data warehouses. In addition to hands-on consulting, he speaks at SQLSaturdays several times/year, has a youtube channel with free training, and has written 20 books on database management systems. His latest book is "Transact SQL the Definitive Guide."
LinkedIn: http://www.linkedin.com/in/chadchurchwell
Contact: http://www.sqlchad.com
Seasoned SQL Server DBA Professional with over 18 years IT experience with 15 years experience in databases. I have managed an enterprise DBA team and am currently in a strategic consulting role specializing in HA, DR, Replication, and Performance Tuning and Optimization. I am currently speaking at several SQLSaturday’s as well as local user group meetings.
LinkedIn: https://www.linkedin.com/in/sqlservernerd
A Microsoft Data Platform MVP, Brandon manages the DB Operations team for financial company x. With a data estate of several petabytes, he puts a high focus on performance tuning and automation. He is a frequent speaker at events like conferences and user groups.
LinkedIn: http://www.linkedin.com/in/thesqlguru
Kevin Boles is a SQL Server expert, working exclusively with the product since v6.5. With over 25 years of database experience and over 45,000 man hours of SQL Server engine experience, he holds many related certifications, is an MCT and was a SQL Server MVP from 2007 to 2012. Kevin has been a very successful independent consultant for over 20 years. His passion is the relational engine, especially designing, building, analyzing and tuning high-performance database applications.
LinkedIn: Linkedin: http://www.linkedin.com/pub/max-trinidad/12/47/775/
Contact: http://www.maxtblog.com
Maximo Trinidad (Florida Aka – Mr. PowerShell), currently work as a SAPIEN Technologies Evangelist and specialing in scripting automation on SQL Server Microsoft Cloud Technologies. Also a Microsoft MVP (PowerShell) since 2009 and active speaker since 2007 participating at SQLSaturday, IT Pro .NET Code Camps events.
LinkedIn: http://www.linkedin.com/in/kendalvandyke
Contact: http://www.kendalvandyke.com
Kendal is a database strategist, community advocate, public speaker, and blogger. A practiced IT professional with over 15 years of SQL Server experience, Kendal excels at disaster recovery, high availability planning/implementation, amp; debugging/troubleshooting mission critical SQL Server environments. Kendal is a Senior Consultant on the Microsoft Premier Developer Support team and President of MagicPASS (http://magicpass.sqlpass.org), the Orlando, FL based chapter of PASS. Before joining Microsoft, Kendal was a SQL Server/Data Platform MVP from 2011-2016.
LinkedIn: http://www.linkedin.com/in/staciavarga
Contact: http://blog.datainspirations.com
Stacia Varga is a Microsoft Data Platform MVP and SSAS Maestro with a Bachelor’s Degree in Social Sciences. A consultant, educator, author, and principal of Data Inspirations, her career spans more than 30 years, with a focus on improving business practices through technology. Since 2000, Stacia has provided consulting and education services for Microsoft's Business Intelligence technologies. As Stacia Misner, she also authored several books covering the Microsoft BI stack.
LinkedIn: https://www.linkedin.com/in/sqlservernerd
A Microsoft Data Platform MVP, Brandon manages the DB Operations team for financial company x. With a data estate of several petabytes, he puts a high focus on performance tuning and automation. He is a frequent speaker at events like conferences and user groups.
LinkedIn: http://www.linkedin.com/in/thesqlguru
Kevin Boles is a SQL Server expert, working exclusively with the product since v6.5. With over 25 years of database experience and over 45,000 man hours of SQL Server engine experience, he holds many related certifications, is an MCT and was a SQL Server MVP from 2007 to 2012. Kevin has been a very successful independent consultant for over 20 years. His passion is the relational engine, especially designing, building, analyzing and tuning high-performance database applications.
LinkedIn: https://www.linkedin.com/in/michaelpantonovich?
Contact: http://sharepointmike.wordpress.com
Michael Antonovich is the assistant director of Web and Technology Services in the Communications Division for Orange County Public Schools in Orlando, FL, where he manages the Internet and intranet infrastructure. He has published seven books, ranging from an Apple II User Guide to Office and SharePoint 2010 User's Guide. His blog has focused mainly on PowerPivot and Power BI, but for the last year Mike has taken a break to pursue some other activities. He hopes to restart his blog later this year focusing more on style and presentation. Mike has used SQL Server since 1998 and, for the past 7 years, has been promoting SSAS, PowerPivot, and Power BI for the masses.
LinkedIn: https://www.linkedin.com/in/marktab
Contact: http://marktab.net
Mark Tabladillo Ph.D. works for Microsoft as a Cloud Solution Architect for the US CTO Customer Success Team. He has a science doctorate from Georgia Tech. Based in Atlanta, GA, Mark provides cloud enterprise solutions for companies in the US for Microsoft's strategic clients. He is a chapter leader for the PASS Data Science virtual chapter.
LinkedIn: https://www.linkedin.com/in/marktab
Contact: http://marktab.net
Mark Tabladillo Ph.D. works for Microsoft as a Cloud Solution Architect for the US CTO Customer Success Team. He has a science doctorate from Georgia Tech. Based in Atlanta, GA, Mark provides cloud enterprise solutions for companies in the US for Microsoft's strategic clients. He is a chapter leader for the PASS Data Science virtual chapter.
LinkedIn: https://www.linkedin.com/profile/public-profile-settings?trk=prof-edit-edit-public_profile
Contact: http://clusteringformeremortals.com/
David Bermingham is recognized within the community as a high availability expert and has been honored by his peers by being elected to be a MVP in Clustering since 2010 and most recently has been recognized as a Cloud and Datacenter MVP. David’s work as technical evangelist at SIOS has him focused on evangelizing Microsoft high availability and disaster recovery solutions for both on premise and cloud deployments. He lives in New Jersey with his wife and three kids where he a school board member, scout leader and musical director of an 8-piece jazz band, M-Town.
Contact: http://bluewatersql.wordpress.com/
Chris Price is a Sr. BI Consultant with Pragmatic Works based in Tampa, FL. He has a B.S. in MIS and a MBA, both from the Univ. of South Florida. He previously worked as a software architect before being bitten by the BI bug. His current focus is on ETL and Data Integration, Data Quality and MDM, SSAS, SharePoint and Big Data. He regularly speaks at SQLSaturdays, Code Camps and other community events. He has authored multiple whitepapers and books and served as technical editor.
LinkedIn: http://linkedin.com/in/richierump
Contact: http://jorriss.net
Richie Rump is a dataveloper with Brent Ozar Unlimited, where he writes code to solve data problems. He developed statisticsparser.com and sp_DataProfile, two tools that help data professionals tune SQL Server. Richie is also the co-host of the Away From The Keyboard Podcast and founded dotNet Miami, a community focused on enterprise development with .NET. He holds the Project Management Professional (PMP) and Certified Scrum Master (CSM) certifications as well as certifications in various Microsoft development technologies. He can be found on Twitter at @Jorriss or at http://jorriss.com.
LinkedIn: https://www.linkedin.com/profile/view?id=176471amp;trk=nav_responsive_tab_profile
Contact: http://mssqlperformance.blogspot.com/
Jeff has been consulting on and performing technical training for MS SQL Server since 1989, Version 4, on OS/2. Since then, he has worked extensively with each release of the product, specializing in performance and tuning, from concept through legacy system, on systems of all sizes, from small start-up company applications to multi-terabyte data warehouses. In addition to hands-on consulting, he speaks at SQLSaturdays several times/year, has a youtube channel with free training, and has written 20 books on database management systems. His latest book is "Transact SQL the Definitive Guide."
Contact: http://etldevelopernotes.blogspot.com/
Jorge Novo is a database developer with more than 10 years of experience in the design, implementation, and maintenance of OLAP, ROLAD database system for the enterprise. Jorge specialize himself on ETL processes using automation, and dynamic programming for SSIS. Currently Jorge is working in the programming and development of Data Master Service for the organization.
Contact: http://bluewatersql.wordpress.com/
Chris Price is a Sr. BI Consultant with Pragmatic Works based in Tampa, FL. He has a B.S. in MIS and a MBA, both from the Univ. of South Florida. He previously worked as a software architect before being bitten by the BI bug. His current focus is on ETL and Data Integration, Data Quality and MDM, SSAS, SharePoint and Big Data. He regularly speaks at SQLSaturdays, Code Camps and other community events. He has authored multiple whitepapers and books and served as technical editor.
LinkedIn: http://linkedin.com/in/richierump
Contact: http://jorriss.net
Richie Rump is a dataveloper with Brent Ozar Unlimited, where he writes code to solve data problems. He developed statisticsparser.com and sp_DataProfile, two tools that help data professionals tune SQL Server. Richie is also the co-host of the Away From The Keyboard Podcast and founded dotNet Miami, a community focused on enterprise development with .NET. He holds the Project Management Professional (PMP) and Certified Scrum Master (CSM) certifications as well as certifications in various Microsoft development technologies. He can be found on Twitter at @Jorriss or at http://jorriss.com.
David has spent the last 10 years focusing on enterprise data solutions. He has implemented large scale BI projects at companies such as Office Depot, AutoNation, Citrix, Bank Atlantic and most recently Ultimate Software, where he served as a Data Architect. David currently holds certifications as a: Microsoft Certified Professional for SQL Server (MCP), Microsoft Certified Technology Specialist for SQL Server 2008 BI (MCTS), Microsoft Certified IT Professional for SQL Server 2008 BI (MCITP).
LinkedIn: http://www.linkedin.com/in/sqlwatchmen
Contact: http://sqlwatchmen.com/blogs/jim/
Jim Murphy has been using Microsoft SQL Server since version 6.0 back in the 90#39;s. He is the CEO of SQLWatchmen, LLC., a managed DBA service provider. He runs the Austin SQL Server User Group: CACTUSS Central. He has been a professional DBA and developer for over 25 years and has run a consulting company full time since 2001.
LinkedIn: http://www.linkedin.com/in/staciavarga
Contact: http://blog.datainspirations.com
Stacia Varga is a Microsoft Data Platform MVP and SSAS Maestro with a Bachelor’s Degree in Social Sciences. A consultant, educator, author, and principal of Data Inspirations, her career spans more than 30 years, with a focus on improving business practices through technology. Since 2000, Stacia has provided consulting and education services for Microsoft's Business Intelligence technologies. As Stacia Misner, she also authored several books covering the Microsoft BI stack.
Contact: http://geekswithblogs.net/hroggero
Herve Roggero, Microsoft Azure MVP, is the founder of Enzo Unified (www.enzounified.com). Herve’s experience includes software development, architecture, database administration and senior management with both global corporations and startup companies. He holds multiple certifications, including an MCDBA, MCSE, MCSD. He is a Pluralsight author, runs the Azure Florida Association, and co-authored two Azure book with Apress.
LinkedIn: https://www.linkedin.com/in/michaelpantonovich?
Contact: http://sharepointmike.wordpress.com
Michael Antonovich is the assistant director of Web and Technology Services in the Communications Division for Orange County Public Schools in Orlando, FL, where he manages the Internet and intranet infrastructure. He has published seven books, ranging from an Apple II User Guide to Office and SharePoint 2010 User's Guide. His blog has focused mainly on PowerPivot and Power BI, but for the last year Mike has taken a break to pursue some other activities. He hopes to restart his blog later this year focusing more on style and presentation. Mike has used SQL Server since 1998 and, for the past 7 years, has been promoting SSAS, PowerPivot, and Power BI for the masses.
LinkedIn: https://www.linkedin.com/in/jenunderwood
Contact: http://www.impactanalytix.com/
Jen Underwood, a Senior Director at DataRobot, has held worldwide analytics product management roles at Microsoft and served as a technical lead for system implementation firms. She has over 20 years of experience launching new products and turning around failed projects in enterprise data warehousing, reporting, and advanced analytics. Today she designs product and helps analytics professionals learn how to solve complex problems with machine learning in the emerging citizen data science segment. 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.
Contact: http://etldevelopernotes.blogspot.com/
Jorge Novo is a database developer with more than 10 years of experience in the design, implementation, and maintenance of OLAP, ROLAD database system for the enterprise. Jorge specialize himself on ETL processes using automation, and dynamic programming for SSIS. Currently Jorge is working in the programming and development of Data Master Service for the organization.
LinkedIn: http://www.linkedin.com/in/dianabeta
Former graduate of the quot;Universidad Santa Maria La Antiguaquot; School of Computer Science. I have been involved in the IT world since 1992, since then I have been working with many OS platforms, development languages and databases, among them Unix, Oracle, SUN, Informix, FoxPro, Microsoft SQL Server, just to mention a few. Diana enjoys solving complex performance tuning issues, examining your data using Databricks, designing in Azure SQL Datawarehouse, developing cool visuals using Power Bi, or evangelizing about Microsoft Data technologies at community events like SQLSaturday and local user group meetings.
LinkedIn: https://www.linkedin.com/in/marktab
Contact: http://marktab.net
Mark Tabladillo Ph.D. works for Microsoft as a Cloud Solution Architect for the US CTO Customer Success Team. He has a science doctorate from Georgia Tech. Based in Atlanta, GA, Mark provides cloud enterprise solutions for companies in the US for Microsoft's strategic clients. He is a chapter leader for the PASS Data Science virtual chapter.
The following is a list of sponsors that helped fund the event.


August 20
Behold, I give unto you power to tread on serpents and scorpions, and over all the power of the enemy: and nothing shall by any means hurt you. Luke 10:19
Somebody's done you wrong! Sooner or later it happens to all of us. Somewhere along the way, we all get hurt or cheated or lied to or abused. It's as predictable as it is painful. Yet when it happens most of us find ourselves strangely unprepared. In our outrage, we often cry out to God against the one who wronged us. We ask for justice, or even vengeance, and end up making things tougher on everyone involved--including ourselves. If that's been true of you, it's time you found out how you can put the power of God to work for you the next time somebody does you wrong.
Step one: Identify the enemy! Right here's where the majority of us make our biggest blunder. We mistakenly identify our enemy as the person who hurts us. Don't waste your energy ranting and raving or plotting and scheming against people who cause you pain. They're simply under the devil's influence. Aim your spiritual ammunition at the right target. It's the devil who's behind it all. Go after him!
Step two: Fire! Once you've pointed your spiritual guns in the right direction, fire! Hit the devil fast and furiously with the Word of God. Use the Name of Jesus and the power that's been given you as a believer and bind the devil from doing you any further harm in that area. Then move on to the next and most important part of this spiritual battle.
Step three: Pray the prayer of intercession.
Crying out for the vengeance of God to strike like a lightning bolt when somebody does us wrong isn't acting like our Father. Remember God has great, great mercy. Not just for me and you, but for everyone! The devil will probably think twice before he bothers you again. Next time someone causes you pain, put the power of God to work for you. Identify the real enemy. Hit him hard with the authority you've been given as a believer. Then pray the prayer of intercession.
Scripture Reading: Matthew 6:6-15


Giadi1833.github.io
BARTEC GmbH Max-Eyth-Straße 16 D-97980 Bad Mergentheim Tel.: +49 7931 597-0 Fax: +49 7931 597-119<EMAIL_ADDRESS>www.bartec.de Reservation Technical data subject to change without notice. Changes, errors and misprints may not be used as a basis for any claim for damages. 2/4 The connection capacity of the automatic circuit breaker. heruntergeladen werden. Print out this manual If you want to print out this manual please ensure that the original size is remained and the print out is of good quality. ÎUnscrew and remove the screws 32 and carefully detach the housing 2 from the cover 4; -Caution! The parts are under preload. ÎDetach casing 2 and filter insert 8 and all other internally located parts from co-ver 4; take care with o-rings Wartungsanleitung Mercury 4PS MERC 4 2-Takt, 2 Zylinder, ab 1979 hier die Arbeiten Impeller und Getriebeöl wechseln: https://www.youtube.com/watch?v=aLH1J90aWV4.
PDF User manual (translation) - PM 420ex Process Monitor - BARTEC. PDF IDMxxx corded Hand-held Scanners - BARTEC. BARTEC reserves the right to alter the contents of this document without notice. No guarantee is given for the correctness of the information. In case of doubt, the German Process Monitor must be installed properly and securely if it is to work perfectly and correctly. BARTEC D-97980 Bad Mergentheim Typ 27-6B 1-5 / CE . Dateiname: KTE-d Betriebsanleitung_Rev-D_EN.doc Technische Änderungen vorbehalten 9. Installation 9.1. Models with one connection cable The models with a connecting cable can be installed HYGROPHIL F Moisture Analyzer, Type 5673-10, -11 - BARTEC. HYGROPHIL F Moisture Analyzer, Type 5673-10, -11, -12, -13. HYGROPHIL F 5673 was particularly designed for the natural gas industry. In the meantime it is used for moisture measurement in a diversity of gases and liquids. Bartec 8Sd9007- -2 a Betriebsanleitung.
PDF Betriebsanleitung + Bedienungsanleitung. Mercury 4PS Betriebsanleitung und Wartung, original - YouTube. 2 Congratulations! You have made an excellent choice. JULABO thanks you for the trust you have placed in us. This operating manual has been designed to help you gain an understanding of the principles.


WITNESS LIST Public Safety Committee March 10, 1997-4:00P HB 414 For: Rep. Dale Tillery (Co-Author of HB 414) Rep. Henry Cuellar (Author of HB 414) Richard W. Cooper (Himself) Scott Ashley (Himself) Against: Charles Wirth (State Farm Insurance Co) Robert Schnieder (Consumer's Union) On: Jerry Dike (TxDOT) Kathleen T. Murphy (DPS) Kevin Raymond (DPS) Vernon Jorgenson (Farmer's Ins. Group) HB 948 For: Deborah Taylor (Herself) Rep. Robert Puente (Author of HB 948) Against: Charles Wirth (State Farm Insurance Co) Robert Schnieder (Consumer's Union) On: Kathleen T. Murphy (DPS) Kevin Raymond (DPS) HB 1881 For: Jimmy Fawcett (TX Police Chiefs Assoc.) On: Wayne Green (TCLEOSE) HCR 94 For: Gertrude Miller (Americ. Jewish Congress) Mark Shilling (Texas Chemical Council) Rep. Harryette Ehrhardt (Author of HCR 94)