ACM Transactions on Database Systems (TODS)
Estimating local cost parameters for global query optimization in a multidatabase system
Estimating local cost parameters for global query optimization in a multidatabase system
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Efficient evaluation of XML middle-ware queries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Orthogonal optimization of subqueries and aggregation
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Optimizing object queries using an effective calculus
ACM Transactions on Database Systems (TODS)
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Optimization of Run-time Management of Data Intensive Web-sites
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Context-Based Prefetch for Implementing Objects on Relations
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Efficiently publishing relational data as XML documents
The VLDB Journal — The International Journal on Very Large Data Bases
Evolutionary techniques for updating query cost models in a dynamic multidatabase environment
The VLDB Journal — The International Journal on Very Large Data Bases
Scalpel: optimizing query streams using semantic prefetching
Scalpel: optimizing query streams using semantic prefetching
Context-aware prefetching at the storage server
ATC'08 USENIX 2008 Annual Technical Conference on Annual Technical Conference
Multiple-Objective Compression of Data Cubes in Cooperative OLAP Environments
ADBIS '08 Proceedings of the 12th East European conference on Advances in Databases and Information Systems
Journal of Intelligent Information Systems
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
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Streams of relational queries submitted by client applications to database servers contain patterns that can be used to predict future requests. We present the Scalpel system, which detects these patterns and optimizes request streams using context-based predictions of future requests. Scalpel uses its predictions to provide a form of semantic prefetching, which involves combining a predicted series of requests into a single request that can be issued immediately. Scalpel's semantic prefetching reduces not only the latency experienced by the application but also the total cost of query evaluation. We describe how Scalpel learns to predict optimizable request patterns by observing the application's request stream during a training phase. We also describe the types of query pattern rewrites that Scalpels cost-based optimizer considers. Finally, we present empirical results that show the costs and benefits of Scalpel's optimizations.