Statistical Mining in Data Streams

Statistical Mining in Data StreamsAnkur Jain Recent years have seen a steady rise of a new class of data management systemscalled Data Stream Management Systems (DSMS). These systems manage rapid, high-volume data-streams with transient relations instead of static data with persistent rela-tions. Data streams are common to applications such as network traffic and transac-tion monitoring systems, click-stream processors, industrial process control, and sen-sor networks. A DSMS operates on these continuous and time-varying data streams tofacilitate on-the-fly query answering, and to support data acquisition, monitoring andanalysis.In this dissertation, we present statistical stream mining solutions for effective on-line processing of streaming data. We focus research issues related to adaptive streamresource conservation and online mining in a DSMS. We have developed statisticallinear and non-linear filtering techniques based on the Kalman Filter to capture tem-poral correlations in the streaming data. Such correlations help in stream resourceconservation. We also propose techniques that capture spatial correlations between thestreaming sources that further helps improving resource conservation and facilitatesanswering group-queries in an efficient manner.