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ARGUS: Efficient Scalable Continuous Query Optimization for Large-Volume Data Streams

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2 Author(s)
Jin, C. ; Language Technol. Inst., Carnegie Mellon Univ., Pittsburgh, PA ; Carbonell, J.

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

Published in:

Database Engineering and Applications Symposium, 2006. IDEAS '06. 10th International

Date of Conference:

Dec. 2006