10.1184/R1/6621158.v1 Chun Jin Chun Jin Jaime G. Carbonell Jaime G. Carbonell ARGUS: Efficient Scalable Continuous Query Optimization for Large-Volume Data Streams Carnegie Mellon University 2006 Software Research 2006-01-01 00:00:00 Journal contribution https://kilthub.cmu.edu/articles/journal_contribution/ARGUS_Efficient_Scalable_Continuous_Query_Optimization_for_Large-Volume_Data_Streams/6621158 <p> </p><p>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.</p> <p></p>