Input-sensitive scalable continuous join query processing
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 01, 2009
- Source ID
- 10.1145/1567274.1567275
Entities
People
- Hai Yu
- Jun Yang
- Junyi Xie
- Pankaj Agarwal
Organizations
- Army Research Office
- Division of Information and Intelligent Systems
- Duke University
- National Science Foundation
- Oracle