ACM Transactions on Database Systems (TODS)
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
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
Measuring evolving data streams' behavior through their intrinsic dimension
New Generation Computing
Speeding-up data-driven applications with program summaries
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Information Sciences: an International Journal
Type-Level access pattern view: a technique for enhancing prefetching performance
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
A distance-based algorithm for clustering database user sessions
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Finding and analyzing database user sessions
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Hi-index | 0.00 |
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 Scalpel's cost-based optimizer considers. Finally, we present empirical results that show the costs and benefits of Scalpel's optimizations.