Optimizations Enabled by Relational Data Model View to Querying Data Streams
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Dynamic Querying of Streaming Data with the dQUOB System
IEEE Transactions on Parallel and Distributed Systems
Tools and techniques for performance measurement of large distributed multiagent systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
IEEE Transactions on Parallel and Distributed Systems
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There is growing interest in run-time detection as parallel and distributed systems grow larger and more complex. This work targets run-time analysis of complex, interactive scientific applications for purposes of attaining scalability improvements with respect to the amount and complexity of the data transmitted, transformed, and shared among different application components. Such improvements are derived from using database techniques to manipulate data streams. Namely, by imposing a relational model on the data streams, constraints on the stream may be expressed as database queries evaluated against the data events comprising the stream. The application in this paper is to a safety-critical system.This paper also presents a tool, dQUOB, which (1) offers the means for dynamic creation of queries and for their application to large data streams, (2) permits implementation and runtime use of multiple `query optimization' techniques, and (3) supports dynamic reoptimization of queries based on streams' dynamic behavior.