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VLDB
2005
ACM

Streaming Pattern Discovery in Multiple Time-Series

10 years 6 months ago
Streaming Pattern Discovery in Multiple Time-Series
In this paper, we introduce SPIRIT (Streaming Pattern dIscoveRy in multIple Timeseries). Given n numerical data streams, all of whose values we observe at each time tick t, SPIRIT can incrementally find correlations and hidden variables, which summarise the key trends in the entire stream collection. It can do this quickly, with no buffering of stream values and without comparing pairs of streams. Moreover, it is any-time, single pass, and it dynamically detects changes. The discovered trends can also be used to immediately spot potential anomalies, to do efficient forecasting and, more generally, to dramatically simplify further data processing. Our experimental evaluation and case studies show that SPIRIT can incrementally capture correlations and discover trends, efficiently and effectively.
Spiros Papadimitriou, Jimeng Sun, Christos Falouts
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where VLDB
Authors Spiros Papadimitriou, Jimeng Sun, Christos Faloutsos
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