Author(s)

P. A. Michael & D. Stott Parker

Abstract

Data Stream Management Systems (DSMSs) have not yet reached a mature enough stage to effectively run data mining algorithms, as they still face challenges within the streaming environment. Streamonas DSMS, as presented in a recent publication, is the first DSMS to reach the maximum level of difficulty supported by the Linear Road Benchmark which is 10 Expressways. The powerful engine of Streamonas can manage an input stream of 20,368 tuples/second with an average query latency of 0.000026 seconds, 192,307 times faster when compared to the 5 seconds maximum query latency the benchmark allows. The on-line data mining over streams presented in this work, is the first effort to apply spatio-temporal data mining algorithms on the Streamonas DSMS system. Dynamic clustering of spatio-temporal subsequences in real-time has been performed successfully, within the large space, high bandwidth, heavy load linear road benchmark streaming platform. Dynamic clustering queries have been expressed in a novel SQL-like language, which we name Streamonas-SQL. Keywords: real-time, data mining, spatio-temporal, dynamic clustering, pattern matching, streamonas, streamonas-SQL, Linear Road Benchmark, query latency, throughput, semantic space.

Keywords

real-time, data mining, spatio-temporal, dynamic clustering, pattern matching, streamonas, streamonas-SQL, Linear Road Benchmark, query latency, throughput, semantic space.