Input-sensitive scalable continuous join query processing

Published

Journal Article

This article considers the problem of scalably processing a large number of continuous queries. Our approach, consisting of novel data structures and algorithms and a flexible processing framework, advances the state-of-the-art in several ways. First, our approach is query sensitive in the sense that it exploits potential overlaps in query predicates for efficient group processing. We partition the collection of continuous queries into groups based on the clustering patterns of the query predicates, and apply specialized processing strategies to heavily clustered groups (or hotspots). We show how to maintain the hotspots efficiently, and use them to scalably process continuous select-join, band-join, and window-join queries. Second, our approach is also data sensitive, in the sense that it makes cost-based decisions on how to process each incoming tuple based on its characteristics. Experiments demonstrate that our approach can improve the processing throughput by orders of magnitude. © 2009 ACM.

Full Text

Duke Authors

Cited Authors

  • Agarwal, PK; Xie, J; Yang, J; Yu, H

Published Date

  • August 1, 2009

Published In

Volume / Issue

  • 34 / 3

Electronic International Standard Serial Number (EISSN)

  • 1557-4644

International Standard Serial Number (ISSN)

  • 0362-5915

Digital Object Identifier (DOI)

  • 10.1145/1567274.1567275

Citation Source

  • Scopus