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• W86021630 abstract "In the last decade and half, scientific and commercial databases have grown at a fast pace as increasingly many enterprises have begun offering and using database-centered services. Concomitantly, new application requirements have placed unusual processing demands on existing databases. Many researchers believe that next generation database systems will be required to provide efficient and scalable facilities for high level inference and complex query processing.In this thesis, we address the efficiency and scalability problem in the context of Database Rule Processing Systems (DRPS). We propose a scalable strategy for the efficient parallel processing of rule programs applied to massive databases. We introduce predictive load balancing techniques in a synchronous model of rule execution, where the variance in runtime of the distributed sites is minimized per cycle of rule processing, thus increasing utilization and speedup. We demonstrate that static load balancing techniques are insufficient, and thus low overhead dynamic load balancing is the key to successful scaling. We present a form of dynamic load balancing that is based upon predicting future system loads, rather than conventional demand-driven or adaptive approaches that monitor current system state. We utilize a combination of meta-data on the database and observed system performance to predictively formulate load balancing decisions over a parallel ensemble. We analyze a number of possible predictive dynamic load balancing protocols by isoefficiency analysis to guide the design of a distributed rule processing system. Our results are outlined as follows. We develop a set-oriented rule language with parallel execution semantics to overcome the constraints imposed by tuple-oriented rule languages. We initially assume a replicated database configuration for simplicity, and we explore automatic work partitioning techniques, including heuristic determination of partitioning attributes, predictive dynamic load balancing protocols to apply them, a self-monitoring principle for system modelling that predicts system performance in the near term based upon recent performance history, and extensive use of statistics on the underlying database (meta data) to achieve load balancing and hence scaling. We then extend our basic results and develop comprehensive algorithms for predictively balanced inference support and complex query processing over distributed database configurations. Our approach enables large-scale expert databases. It is expected that the resulting quantitative improvements will lead to qualitative improvements in the applications built within this framework." @default.
• W86021630 created "2016-06-24" @default.
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• W86021630 date "1994-12-15" @default.
• W86021630 modified "2023-09-28" @default.
• W86021630 title "Runtime reorganization of parallel and distributed expert database systems" @default.
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