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Article
ARGUS: Efficient Scalable Continuous Query Optimization for Large-Volume Data Streams
Proceedings of the Tenth International Database Engineering & Applications Symposium (IDEAS-06)
  • Chun Jin, Carnegie Mellon University
  • Jaime G. Carbonell, Carnegie Mellon University
Date of Original Version
12-1-2006
Type
Conference Proceeding
Abstract or Description

We present the architecture of ARGUS, a stream processing system implemented atop commercial DBMSs to support large-scale complex continuous queries over data streams. ARGUS supports incremental operator evaluation and incremental multi-query plan optimization as new queries arrive. The latter is done to a degree well beyond the previous state-of-the-art via a suite of techniques such as query-algebra canonicalization, indexing, and searching, and topological query network optimization with join order optimization, conditional materialization, minimal column projection, and transitivity inference. Building on top of a DBMS, the system provides a value-adding package to the existing database applications where the needs of stream processing become increasingly demanding. Compared to directly running the continuous queries on the DBMS, ARGUS achieves well over a 100-fold improvement in performance

DOI
10.1109/IDEAS.2006.11
Citation Information
Chun Jin and Jaime G. Carbonell. "ARGUS: Efficient Scalable Continuous Query Optimization for Large-Volume Data Streams" Proceedings of the Tenth International Database Engineering & Applications Symposium (IDEAS-06) (2006)
Available at: http://works.bepress.com/jaime_carbonell/9/