ACM Transactions on Database Systems (TODS)
Improvements on a heuristic algorithm for multiple-query optimization
Data & Knowledge Engineering
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Improved query performance with variant indexes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Simultaneous optimization and evaluation of multiple dimensional queries
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
DynaMat: a dynamic view management system for data warehouses
SIGMOD '99 Proceedings of the 1999 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
Data warehousing features in Informix OnLine XPS
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Using Common Subexpressions to Optimize Multiple Queries
Proceedings of the Fourth International Conference on Data Engineering
Selection of Views to Materialize in a Data Warehouse
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Selection of Views to Materialize Under a Maintenance Cost Constraint
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Materialized View Selection for Multidimensional Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Optimizing multiple dimensional queries simultaneously in multidimensional databases
The VLDB Journal — The International Journal on Very Large Data Bases
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Multi-Dimensional Expressions (MDX) provide an interface for asking several related OLAP queries simultaneously. An interesting problem is how to optimize the execution of an MDX query, given that most data warehouses maintain a set of redundant materialized views to accelerate OLAP operations. A number of greedy and approximation algorithms have been proposed for different versions of the problem. In this paper we evaluate experimentally their performance using the APB and TPC-H benchmarks, concluding that they do not scale well for realistic workloads. Motivated by this fact, we developed two novel greedy algorithms. Our algorithms construct the execution plan in a top-down manner by identifying in each step the most beneficial view, instead of finding the most promising query. We show by extensive experimentation that our methods outperform the existing ones in most cases.