ACM Transactions on Database Systems (TODS)
The SEQUOIA 2000 storage benchmark
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Improvements on a heuristic algorithm for multiple-query optimization
Data & Knowledge Engineering
Optimization of dynamic query evaluation plans
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Efficient and extensible algorithms for multi query optimization
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Clustering Algorithms
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
The Query Clustering Problem: A Set Partitioning Approach
IEEE Transactions on Knowledge and Data Engineering
Using Common Subexpressions to Optimize Multiple Queries
Proceedings of the Fourth International Conference on Data Engineering
Design and Analysis of Parametric Query Optimization Algorithms
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
PLASTIC: reducing query optimization overheads through plan recycling
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
The workload you have, the workload you would like
DOLAP '03 Proceedings of the 6th ACM international workshop on Data warehousing and OLAP
Developing a characterization of business intelligence workloads for sizing new database systems
Proceedings of the 7th ACM international workshop on Data warehousing and OLAP
Analyzing plan diagrams of database query optimizers
VLDB '05 Proceedings of the 31st international conference on Very large data bases
AniPQO: almost non-intrusive parametric query optimization for nonlinear cost functions
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Green query optimization: taming query optimization overheads through plan recycling
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient use of the query optimizer for automated physical design
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Ontology-Based Data Sharing in P2P Databases
Semantic Web, Ontologies and Databases
Efficiently approximating query optimizer plan diagrams
Proceedings of the VLDB Endowment
Reusing classical query rewriting in P2P databases
DBISP2P'05/06 Proceedings of the 2005/2006 international conference on Databases, information systems, and peer-to-peer computing
Variance aware optimization of parameterized queries
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Predicting cost amortization for query services
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
The QOL approach for optimizing distributed queries without complete knowledge
Proceedings of the 16th International Database Engineering & Applications Sysmposium
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Query optimization is a computationally intensive process, especially for complex queries. We present here a tool, called PLASTIC, that can be used by query optimizers to amortize the optimization cost. Our scheme groups similar queries into clusters and uses the optimizer-generated plan for the cluster representative to execute all future queries assigned to the cluster. Query similarity is evaluated based on a comparison of query structures and the associated table schemas and statistics, and a classifier is employed for efficient cluster assignments. Experiments with a variety of queries on a commercial optimizer show that PLASTIC predicts the correct plan choice in most cases, thereby providing significantly improved query optimization times. Further, when errors are made, the additional execution cost incurred due to the sub-optimal plan choices is marginal.