A rule-based view of query optimization
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Logic-based approach to semantic query optimization
ACM Transactions on Database Systems (TODS)
PODS '91 Proceedings of the tenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Extensible/rule based query rewrite optimization in Starburst
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
Understanding the new SQL: a complete guide
Understanding the new SQL: a complete guide
Answering queries using views (extended abstract)
PODS '95 Proceedings of the fourteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Rule languages and internal algebras for rule-based optimizers
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
On the Multiple-Query Optimization Problem
IEEE Transactions on Knowledge and Data Engineering
Security Constraint Processing in a Multilevel Secure Distributed Database Management System
IEEE Transactions on Knowledge and Data Engineering
Fusion Queries over Internet Databases
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Semantic Query Reformulation in Deductive Databases
Proceedings of the Seventh International Conference on Data Engineering
Praire: A Rule Specification Framework for Query Optimizers
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Optimizing Queries with Materialized Views
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Implementation of Two Semantic Query Optimization Techniques in DB2 Universal Database
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Semantic Data Caching and Replacement
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Semantic Query Optimization for Bottom-Up Evaluation
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
View disassembly: A rewrite that extracts portions of views
Journal of Computer and System Sciences
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Many database applications and environments, such as mediation over heterogeneous database sources and data warehousing for decision support, lead to complex queries. Queries are often nested, defined over previously defined views, and may involve unions. There are good reasons why one might want to “remove” pieces (sub-queries or sub-views) from such queries: some sub-views of a query may be effectively cached from previous queries, or may be materialized views; some may be known to evaluate empty, by reasoning over the integrity constraints; and some may match protected queries, which for security cannot be evaluated for all users.In this paper, we present a new evaluation strategy with respect to queries defined over views, which we call tuple-tagging, that allows for an efficient “removal” of sub-views from the query. Other approaches to this are to rewrite the query so the sub-views to be removed are effectively gone, then to evaluate the rewritten query. With the tuple tagging evaluation, no rewrite of the original query is necessary.We describe formally a discounted query (a query with sub-views marked that are to be considered as removed), present the tuple tagging algorithm for evaluating discounted queries, provide an analysis of the algorithm's performance, and present some experimental results. These results strongly support the tuple-tagging algorithm both as an efficient means to effectively remove sub-views from a view query during evaluation, and as a viable optimization strategy for certain applications. The experiments also suggest that rewrite techniques for this may perform worse than the evaluation of the original query, and much worse than the tuple tagging approach.