The APOS Location Intelligence Solution (APOS LIS) integrates Esri ArcGIS with SAP BusinessObjects to create fully bi-directional, pervasive, enterprise location intelligence for numerous industries.

# Category Archives: Big Data

# Changing the Game

SAP HANA Changes the Game – A Win for its Customers, Maybe Not for Oracle, Microsoft, IBM.

If you’re looking for a more technical breakdown on SAP HANA, the Wikipedia article is very informative.

# What’s the Latency, Kenneth?

Latency numbers every programmer should know. Don’t miss the slider bar at the top, so you can travel back in time.

# Don’t Try to Confuse Me With the Facts

Mistakes can be made by the best of us. Make sure your statistical test considers four assumptions of multiple regression that researchers should consider.

# My City is Smarter Than Your City

# The Internet of Things

# Deep Analytical Talent

The emerging field of Big Data is very dependent on advanced analytical techniques possessed by a very tiny talent pool. Where are these analysts now?

# Data Viz of the Week

# Example Bayesian Inference using R and OpenBUGS

- Open the R console

Go to File/Change dir and set the working directory for R. - Use your favorite editor to save the following code as schools.bug in your R working directory.

model {

for (j in 1:J){

y[j] ~ dnorm (theta[j], tau.y[j])

theta[j] ~ dnorm (mu.theta, tau.theta)

tau.y[j] <- pow(sigma.y[j], -2)

}

mu.theta ~ dnorm (0.0, 1.0E-6)

tau.theta <- pow(sigma.theta, -2)

sigma.theta ~ dunif (0, 1000)

} - Save this data as schools.dat in your R working directory.
school estimate sd A 28 15 B 8 10 C -3 16 D 7 11 E -1 9 F 1 11 G 18 10 H 12 18

- Go back to the R console and type the following commands.

schools <- read.table (“schools.dat”, header=TRUE)

J <- nrow(schools)

y <- schools$estimate

sigma.y <- schools$sd

data <- list (“J”, “y”, “sigma.y”)

inits <- function(){list(theta=rnorm(J,0,100),mu.theta=rnorm(1,0,100),sigma.theta=runif(1,0,100))}

parameters <- c(“theta”, “mu.theta”, “sigma.theta”)

library (“BRugs”)

schools.sim <- bugs (data, inits, parameters, “schools.bug”, n.chains=3, n.iter=1000, program=”openbugs”)

From an example at columbia.edu.