file.pdf (371.95 kB)
Download file

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

Download (371.95 kB)
journal contribution
posted on 01.01.2006, 00:00 authored by Chun Jin, Jaime G. Carbonell

We present the methods and 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 evaluations 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. 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.


Publisher Statement

All Rights Reserved



Usage metrics