Multi-granular Time-Based Sliding Windows over Data Streams

We introduce a multi-level window operator that concurrently spans temporal extents of increasing granularity over a streaming dataset. This windowing construct is inherently sliding with time, essentially providing at each granularity a varying, but always finite portion of the most recent stream i...

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Bibliographic Details
Main Authors: Patroumpas, K, Sellis, T
Format: Conference Proceeding
Language:English
Subjects:
Aggregates
Continuous Queries
Data Streams
Maintenance engineering
Monitoring
Multiple Granularities
Semantics
Temperature measurement
Time series analysis
Timestamps
Transient analysis
Windows
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Description
Summary:We introduce a multi-level window operator that concurrently spans temporal extents of increasing granularity over a streaming dataset. This windowing construct is inherently sliding with time, essentially providing at each granularity a varying, but always finite portion of the most recent stream items. After a careful algebraic formulation of its semantics, we investigate interesting properties and suggest a suitable data structure that can efficiently maintain tuples qualifying for each granular level. Moreover, we propose techniques for evaluating advanced continuous requests against multiple time horizons, achieving near real-time response at reduced overhead. Finally, this framework is empirically validated against streaming data, offering concrete evidence of its benefits to online stream processing.
ISSN:1530-1311
2332-6468
DOI:10.1109/TIME.2010.14