Scheduling for shared window joins over data stream

Moustafa A. Hammad, Michael J. Franklin, Walid G. Aref, Ahmed Elmagarmid

Research output: Chapter in Book/Report/Conference proceedingConference contribution

94 Citations (Scopus)

Abstract

Continuous Query (CQ) systems typically exploit commonality among query expressions to achieve improved efficiency through shared processing. Re cently proposed CQ systems have introduced window specifications in order to support unbounded data streams. There has been, however, little investigation of sharing for windowed query operators. In this paper, we address the shared execution of windowed joins, a core operator for CQ sys tems. We show that the strategy used in systems to date has a previously unreported performance flaw that can negatively impact queries with relatively small windows. We then propose two new execution strategies for shared joins. We evaluate the alternatives using both analytical model and implementation in a DBMS. The results show that one strategy, called MQT, provides the best performance over a range of workload settings.

Original languageEnglish
Title of host publicationProceedings - 29th International Conference on Very Large Data Bases, VLDB 2003
PublisherMorgan Kaufmann
Pages297-308
Number of pages12
ISBN (Electronic)0127224424, 9780127224428
Publication statusPublished - 1 Jan 2003
Externally publishedYes
Event29th International Conference on Very Large Data Bases, VLDB 2003 - Berlin, Germany
Duration: 9 Sep 200312 Sep 2003

Other

Other29th International Conference on Very Large Data Bases, VLDB 2003
CountryGermany
CityBerlin
Period9/9/0312/9/03

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ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Information Systems and Management
  • Computer Science Applications
  • Computer Networks and Communications

Cite this

Hammad, M. A., Franklin, M. J., Aref, W. G., & Elmagarmid, A. (2003). Scheduling for shared window joins over data stream. In Proceedings - 29th International Conference on Very Large Data Bases, VLDB 2003 (pp. 297-308). Morgan Kaufmann.