Raft of papers #2: Declarative Info Extraction From Text

Relational databases are for structured data, right? And free text lives in the world of keyword search?

Well.  

Another paper we recently finished up was on Declarative Information Extraction in a Probabilistic Database System.  In a nutshell (as my buddy Minos is wont to say), this is about

1. automatically converting free text into structured data,
2. using the state of the art machine learning technique (Conditional Random Fields), which is 
3. coded up in a few lines of SQL that integrates with the rest of your query processing.

This is Daisy Wang‘s baby, and it’s really cool.  She’s achieved a convergence where free text, relational data and statistical models all come together in an elegant and very practical way.  

MapReduce is my chum

One more post on MapReduce and parallel SQL, this time for the folks at O’Reilly Radar.  

Just for the record, I think MapReduce is fine, but not especially interesting technology.  The thing is, the “teachable moment” it presents is really great stuff, because it is bringing people toward data-centric parallel programming.  So it’s good for the data-centric research business in general, and especially for data-centric approaches to parallelism.  

I.e. chum in the water for our research on Lincoln…