Incremental Maintenance of Online Summaries Over Multiple Streams

We propose a novel approach based on predictive quantization (PQ) for online summarization of multiple time-varying data streams. A synopsis over a sliding window of most recent entries is computed in one pass and dynamically updated in constant time. The correlation between consecutive data element...

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Vydáno v:	IEEE transactions on knowledge and data engineering Ročník 20; číslo 2; s. 216 - 229
Hlavní autoři:	Altiparmak, F., Tuncel, E., Ferhatosmanoglu, H.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.02.2008 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Applied sciences Computer science; control theory; systems Computer systems and distributed systems. User interface Data analysis Data mining Data processing. List processing. Character string processing Data security Exact sciences and technology Information systems. Data bases Intrusion detection Memory organisation. Data processing Monitoring multiple streams On-line systems Online online update Prediction Quantization Queries Query processing Real time Sliding Software Stock markets Streams summarization Telecommunication network management Windows (intervals) multiple streams summarization online update Prediction quantization Streaming Correlation Database query data summarization Abstract Updating Maintenance synopsis Real time time-varying data predictive quantization Time varying system Signal quantization Sliding window Linguistics Multiple streams
ISSN:	1041-4347, 1558-2191
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Popis
Shrnutí:	We propose a novel approach based on predictive quantization (PQ) for online summarization of multiple time-varying data streams. A synopsis over a sliding window of most recent entries is computed in one pass and dynamically updated in constant time. The correlation between consecutive data elements is effectively taken into account without the need for preprocessing. We extend PQ to multiple streams and propose structures for real-time summarization and querying of a massive number of streams. Queries on any subsequence of a sliding window over multiple streams are processed in real time. We examine each component of the proposed approach, prediction, and quantization separately and investigate the space-accuracy trade-off for synopsis generation. Complementing the theoretical optimality of PQ-based approaches, we show that the proposed technique, even for very short prediction windows, significantly outperforms the current techniques for a wide variety of query types on both synthetic and real data sets.
Bibliografie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 content type line 23
ISSN:	1041-4347 1558-2191
DOI:	10.1109/TKDE.2007.190693