Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
On the complexity of the view-selection problem
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
View selection using randomized search
Data & Knowledge Engineering
Exact and Approximate Algorithms for the Index Selection Problem in Physical Database Design
IEEE Transactions on Knowledge and Data Engineering
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Materialized Views Selection in a Multidimensional Database
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Materialized View Selection for Multidimensional Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Automated Selection of Materialized Views and Indexes in SQL Databases
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Algorithms for Materialized View Design in Data Warehousing Environment
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
A uniform approach for selecting views and indexes in a data warehouse
IDEAS '97 Proceedings of the 1997 International Symposium on Database Engineering & Applications
Index Selection for Databases: A Hardness Study and a Principled Heuristic Solution
IEEE Transactions on Knowledge and Data Engineering
Selection of Views to Materialize in a Data Warehouse
IEEE Transactions on Knowledge and Data Engineering
Selecting and using views to compute aggregate queries
ICDT'05 Proceedings of the 10th international conference on Database Theory
A formal model for the problem of view selection for aggregate queries
ADBIS'05 Proceedings of the 9th East European conference on Advances in Databases and Information Systems
A view selection algorithm with performance guarantee
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Systematic Exploration of Efficient Query Plans for Automated Database Restructuring
ADBIS '09 Proceedings of the 13th East European Conference on Advances in Databases and Information Systems
SimulPh.D.: A Physical Design Simulator Tool
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
CoPhy: a scalable, portable, and interactive index advisor for large workloads
Proceedings of the VLDB Endowment
Revisiting the partial data cube materialization
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
CASCON '12 Proceedings of the 2012 Conference of the Center for Advanced Studies on Collaborative Research
Deterministic view selection for data-analysis queries: properties and algorithms
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
An integer programming approach for the view and index selection problem
Data & Knowledge Engineering
Dynamic View Management System for Query Prediction to View Materialization
International Journal of Data Warehousing and Mining
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In on-line analytical processing (OLAP), precomputing (materializing as views) and indexing auxiliary data aggregations is a common way of reducing query-evaluation time costs for important data-analysis queries. We consider an OLAP view- and index-selection problem stated as an optimization problem, where (i) the inputs include the data-warehouse schema, a set of data-analysis queries of interest, and a storage-limit constraint, and (ii) the output is a set of views and indexes that minimizes the costs of the input queries, subject to the storage limit. While greedy and other heuristic strategies for choosing views or indexes might help to some extent in improving the costs, it is highly nontrivial to arrive at a globally optimum solution, one that reduces the processing costs of typical OLAP queries as much as is theoretically possible. In fact, as observed in [17] and to the best of our knowledge, there is no known approximation algorithm for OLAP view or index selection with nontrivial performance guarantees. In this paper we propose a systematic study of the OLAP view- and index-selection problem. Our specific contributions are as follows: (1) We develop an algorithm that effectively and efficiently prunes the space of potentially beneficial views and indexes when given realistic-size instances of the problem. (2) We provide formal proofs that our pruning algorithm keeps at least one globally optimum solution in the search space, thus the resulting integer-programming model is guaranteed to find an optimal solution. (3) We develop a family of algorithms to further reduce the size of the search space, so that we are able to solve larger problem instances, although we no longer guarantee the global optimality of the resulting solution. (4) Finally, we present an experimental comparison of our proposed approaches with the state-of-the-art approaches of [2, 12]. Our experiments show that our approaches to view and index selection result in high-quality solutions --- in fact, in globally optimum solutions for many realistic-size problem instances. Thus, they compare favorably with the well-known OLAP-centered approach of [12] and provide for a winning combination with the end-to-end framework of [2] for generic view and index selection.