I’ve been learning about Geographical Information System (GIS) data lately. More and more projects and businesses are doing interesting things by associating new kinds of data with specific latitude/longitude pairs; this data might be about air quality, real estate prices, or the make and model of the nearest Uber car.

In Spark Is the New Black in IBM Data Magazine, I recently wrote about how popular the Apache Spark framework is for both Hadoop and non-Hadoop projects these days, and how for many people it goes so far as to replace one of Hadoop’s fundamental components: MapReduce. (I still have trouble writing “Spar” without writing “ql” after it.) While waiting for that piece to be copyedited, I came across 5 Reasons Why Spark Matters to Business by my old XML.com editor Edd…

In part 1 of this series, I discussed the history of R, the programming language and environment for statistical computing and graph generation, and why it’s become so popular lately. The many libraries that people have contributed to it are a key reason for its popularity, and the SPARQL one inspired me to learn some R to try it out. Part 1 showed how to load this library, retrieve a SPARQL result set, and perform some basic statistical analysis of the numbers in the result set. After I…

R is a programming language and environment for statistical computing and graph generation that, despite being over 30 years old, has gotten hot lately because it’s an open-source, cross-platform tool that brings a lot to the world of Data Science, a recently popular field often associated with the analytics aspect of the drive towards Big Data. The large, active community around R has developed many add-on libraries, including one for working with data retrieved from SPARQL endpoints, so…

The combination of microdata and schema.org seems to have hit a sweet spot that has helped both to get a lot of traction. I’ve been learning more about microdata recently, but even before I did, I found that the W3C’s Microdata to RDF Distiller written by Ivan Herman would convert microdata stored in web pages into RDF triples, making it possible to query this data with SPARQL. With major retailers such as Walmart and BestBuy making such data available on—as far as I can tell—every…

While preparing a demo for the upcoming Taxonomy Boot Camp conference, I hit upon a trick for revising SPARQL CONSTRUCT queries so that they don’t need OPTIONAL blocks. As I wrote in the new “Query Efficiency and Debugging” chapter in the second edition of Learning SPARQL, “Academic papers on SPARQL query optimization agree: OPTIONAL is the guiltiest party in slowing down queries, adding the most complexity to the job that the SPARQL processor must do to find the relevant…