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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.
...
Pages: 10
Size: 388 kb
Paper DOI: 10.2495/DATA090121
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