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Distributed, online data mining systems have emerged as a result ofapplications requiring analysis of large amounts of correlated andhigh-dimensional data produced by multiple distributed data sources. We proposea distributed online data classification framework where data is gathered bydistributed data sources and processed by a heterogeneous set of distributedlearners which learn online, at run-time, how to classify the different datastreams either by using their locally available classification functions or byhelping each other by classifying each other's data. Importantly, since thedata is gathered at different locations, sending the data to another learner toprocess incurs additional costs such as delays, and hence this will be onlybeneficial if the benefits obtained from a better classification will exceedthe costs. We model the problem of joint classification by the distributed andheterogeneous learners from multiple data sources as a distributed contextualbandit problem where each data is characterized by a specific context. Wedevelop a distributed online learning algorithm for which we can provesublinear regret. Compared to prior work in distributed online data mining, ourwork is the first to provide analytic regret results characterizing theperformance of the proposed algorithm.

Distributed Online Big Data Classification Using Context Information