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Sessions at Hadoop Summit 2012 about Bayesian Classifier

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Wednesday 13th June 2012

  • Bayesian Counters

    by Alex Kozlov

    Processing of large data requires new approaches to data mining: low, close to linear, complexity and stream processing. While in the traditional data mining the practitioner is usually presented with a static dataset, which might have just a timestamp attached to it, to infer a model for predicting future/takeout observations, in stream processing the problem is often posed as extracting as much information as possible on the current data to convert them to an actionable model within a limited time window. In this talk I present an approach based on HBase counters for mining over streams of data, which allows for massively distributed processing and data mining. I will consider overall design goals as well as HBase schema design dilemmas to speed up knowledge extraction process. I will also demo efficient implementations of Naive Bayes, Nearest Neighbor and Bayesian Learning on top of Bayesian Counters.

    At 4:30pm to 5:10pm, Wednesday 13th June