Generic windowing support for extensible stream processing systems

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dc.citation.epage1128en_US
dc.citation.issueNumber9en_US
dc.citation.spage1105en_US
dc.citation.volumeNumber44en_US
dc.contributor.authorGedik, B.en_US
dc.date.accessioned2016-02-08T11:00:14Z
dc.date.available2016-02-08T11:00:14Z
dc.date.issued2014en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractStream processing applications process high volume, continuous feeds from live data sources, employ data-in-motion analytics to analyze these feeds, and produce near real-time insights with low latency. One of the fundamental characteristics of such applications is the on-the-fly nature of the computation, which does not require access to disk resident data. Stream processing applications store the most recent history of streams in memory and use it to perform the necessary modeling and analysis tasks. This recent history is often managed using windows. All data stream management systems provide some form of windowing functionality. Windowing makes it possible to implement streaming versions of the traditionally blocking relational operators, such as streaming aggregations, joins, and sorts, as well as any other analytic operator that requires keeping the most recent tuples as state, such as time series analysis operators and signal processing operators. In this paper, we provide a categorization of different window types and policies employed in stream processing applications and give detailed operational semantics for various window configurations. We describe an extensibility mechanism that makes it possible to integrate windowing support into user-defined operators, enabling consistent syntax and semantics across system-provided and third-party toolkits of streaming operators. We describe the design and implementation of a runtime windowing library that significantly simplifies the construction of window-based operators by decoupling the handling of window policies and operator logic from each other. We present our experience using the windowing library to implement a relational operators toolkit and compare the efficacy of the solution to an earlier implementation that did not employ a common windowing library. Copyright © 2013 John Wiley & Sons, Ltd.en_US
dc.identifier.doi10.1002/spe.2194en_US
dc.identifier.issn0038-0644
dc.identifier.urihttp://hdl.handle.net/11693/26468
dc.language.isoEnglishen_US
dc.publisherJohn Wiley & Sons Ltd.en_US
dc.relation.isversionofhttp://dx.doi.org/10.1002/spe.2194en_US
dc.source.titleSoftware: Practice and Experienceen_US
dc.subjectData stream processingen_US
dc.subjectWindowing libraryen_US
dc.subjectWindowing semanticsen_US
dc.subjectSemanticsen_US
dc.subjectSignal processingen_US
dc.subjectTime series analysisen_US
dc.subjectData stream management systemsen_US
dc.subjectDesign and implementationsen_US
dc.subjectFundamental characteristicsen_US
dc.subjectModel and analysisen_US
dc.subjectOperational semanticsen_US
dc.subjectRelational operatoren_US
dc.subjectStream processing systemsen_US
dc.subjectData communication systemsen_US
dc.titleGeneric windowing support for extensible stream processing systemsen_US
dc.typeArticleen_US

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