Search is increasingly being used to gather intelligence on multi-structured data leveraging distributed platforms such as Hadoop in the background. This session will provide details on how search engines can be abused to use not text, but mathematically derived tokens to build models that implement reflected intelligence. The session will describe how to integrate Apache Solr/Lucene with Hadoop. Then we will show how crowd-sourced search behavior can be looped back into analysis and how constantly self-correcting models can be created and deployed. Finally, we will show how these models can respond with intelligent behavior in realtime.
TED: I think that the agenda needs to go here because it otherwise breaks up some key flow
Search Abuse Can discuss how I started just doing free text, but then a curious thing happened, started to see people using the engine for things like: key/value, denormalized DBs, browsing engines, plagiarism detection, teaching languages, record linkage and much, much moreSearch has added more DB features over the yearsTED: We need to introduce the idea of *REVOLUTION* somewhere in here.
All that revolution is good, but what the heck does this have to do w/ Big Data?
GSI: needs a bit more meat
Service-Oriented ArchitectureStatelessFailover/Fault TolerantLightweight Coordination and MessagingSmart about UpdatesDocument store isDistributedScalableAnalysisBatchNear Real-Time