A rule-based view of query optimization
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
Computer Vision, Graphics, and Image Processing
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SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
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SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Open issues in parallel query optimization
ACM SIGMOD Record
Volcano An Extensible and Parallel Query Evaluation System
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ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
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VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Temporal Query Processing and Optimization in Multiprocessor Database Machines
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
On the Effectiveness of Optimization Search Strategies for Parallel Execution Spaces
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
Analysis of Dynamic Load Balancing Strategies for Parallel Shared Nothing Database Systems
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
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VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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RIDE '96 Proceedings of the 6th International Workshop on Research Issues in Data Engineering (RIDE '96) Interoperability of Nontraditional Database Systems
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RIDE '96 Proceedings of the 6th International Workshop on Research Issues in Data Engineering (RIDE '96) Interoperability of Nontraditional Database Systems
A framework for moving sensor data query and retrieval of dynamic atmospheric events
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
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Exploratory data mining and analysis requires a computing environment which provides facilities for the user-friendly expression and rapid execution of “scientific queries.” In this paper, we address research issues in the parallelization of scientific queries containing complex user-defined operations. In a parallel query execution environment, parallelizing a query execution plan involves determining how input data streams to evaluators implementing logical operations can be divided to be processed by clones of the same evaluator in parallel. We introduced the concept of “relevance window” that characterizes data lineage and data partitioning opportunities available for an user-defined evaluator. In addition, we developed a query parallelization framework by extending relational parallel query optimization algorithms to allow the parallelization characteristics of user-defined evaluators to guide the process of query parallelization in an extensible query processing environment. We demonstrated the utility of our system by performing experiments mining cyclonic activity, blocking events, and the upward wave-energy propagation features from several observational and model simulation datasets.