RSP-DS: Real Time Sequential Pattern Analysis over Data Streams

Abstract

The existing pattern analysis algorithms in data streams environment have only focused on studying performance improvement and effective memory usage. But when new data streams come, existing pattern analysis algorithms have to analyze patterns again and have to regenerate pattern tree. This approach needs many calculations in real time environments having real time pattern analysis needs. This paper proposes a method that continuously analyzes patterns of incoming data streams in real time. The proposed method analyzes patterns first, and then after obtains real time patterns by updating previously analyzed patterns. The patterns form a pattern tree, and freshly created new patterns update the pattern tree. In this way, real time patterns are always maintained in the pattern tree and old patterns in the tree are deleted easily using FIFO method. The advantage of our algorithm is proved by performance comparison with existing methods, MILE, with a condition that pattern is changed continuously.KeywordsData StreamPattern AnalysisSequential PatternHash TablePattern TreeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.