ACM Transactions on Database Systems (TODS)
Answering complex SQL queries using automatic summary tables
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Maintenance of cube automatic summary tables
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A general framework for the view selection problem for data warehouse design and evolution
Proceedings of the 3rd ACM international workshop on Data warehousing and OLAP
fAST Refresh using Mass Query Optimization
Proceedings of the 17th International Conference on Data Engineering
Answering queries using views: A survey
The VLDB Journal — The International Journal on Very Large Data Bases
Constructing search spaces for materialized view selection
Proceedings of the 7th ACM international workshop on Data warehousing and OLAP
Recommending Materialized Views and Indexes with IBM DB2 Design Advisor
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Optimizing refresh of a set of materialized views
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Computing closest common subexpressions for view selection problems
DOLAP '06 Proceedings of the 9th ACM international workshop on Data warehousing and OLAP
Load balancing and data placement for multi-tiered database systems
Data & Knowledge Engineering
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Materialized views (MVs) are used in databases and data warehouses to greatly improve query performance. In this context, a great challenge is to exploit commonalities among the views and to employ multi-query optimization techniques in order to derive an efficient global evaluation plan for refreshing the MVs concurrently. IBMDB2$^{\rm {\textregistered}}Universal Database^{\texttrademark}$ (DB2 UDB) provides two query matching techniques, query stacking and query sharing, to exploit commonalities among the MVs, and to construct an efficient global evaluation plan. When the number of MVs is large, memory and time restrictions prevent us from using both query matching techniques in constructing efficient global plans. We suggest an approach that applies the query stacking and query sharing techniques in different steps. The query stacking technique is applied first, and the outcome is exploited to define groups of MVs. The number of MVs in each group is restricted. This allows the query sharing technique to be applied only within groups in a second step. Finally, the query stacking technique is used again to determine an efficient global evaluation plan. An experimental evaluation shows that the execution time of the plan generated by our approach is very close to that of the plan generated using both query matching techniques without restriction. This result is valid no matter how big the database is.