Multiple query optimization with Depth-First Branch-and-Bound and dynamic query ordering
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Advanced query optimization techniques for relational database systems
Advanced query optimization techniques for relational database systems
Efficient and extensible algorithms for multi query optimization
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
On the Multiple-Query Optimization Problem
IEEE Transactions on Knowledge and Data Engineering
Using Common Subexpressions to Optimize Multiple Queries
Proceedings of the Fourth International Conference on Data Engineering
Hi-index | 0.00 |
The ever-increasing load on databases dictates that queries do not need to be processed one by one. Multi-query optimization seeks to optimize queries grouped in batches instead of one by one. Multi-query optimizers aim at identifying inter and intra query similarities to bring up sharing of common sub-expressions and hence saving computer resources like time, processor cycles and memory. Of course, the searching takes some resources but so long as the saved resources are more than those used, there is a global benefit. Since queries are random and from different sources, similarities are not guaranteed but since they are addressed to the same schema, it is likely. The search strategy need to be intelligent such that it continues only when there is a high probability of a sharing (hence resource saving) opportunity. We present a search strategy that assembles the queries in an order such that the benefits are high, that detects null sharing cases and therefore terminates the similar sub-expressions' search as well as removing sub-expressions which already exist else where so as to reduce subsequent searching procedures for a global advantage. AMS Subject Classification: 68M20, 68P20, 68Q85