Classification spanning correlated data streams
In many applications, classifiers need to be built based on multiple related data streams. For example, stock streams and news streams are related, where the classification patterns may involve features from both streams. Thus instead of mining on a single isolated stream, we need to examine multiple related data streams in order to find such patterns and build an accurate classifier. Other examples of related streams include traffic reports and car accidents, sensor readings of different types or at different locations, etc. In this paper, we consider the classification problem defined over sliding-window join of several input data streams. As the data streams arrive in fast pace and the many-to-many join relationship blows up the data arrival rate even more, it is impractical to compute the join and then build the classifier each time the window slides forward. We present an efficient algorithm to build a Nave Bayesian classifier in such context. Our method does not need to perform the join operations but is still able to build exactly the same classifier as if built on the joined result. It only examines each input tuple twice, independent of the number of tuples it joins in other streams, therefore, is able to keep pace with the fast arriving data streams in the presence of many-to-many join relationships. The experiments confirmed that our classification algorithm is more efficient than conventional methods while maintaining good classification accuracy. Copyright 2006 ACM.
