Vertical partitioning algorithms for database design
ACM Transactions on Database Systems (TODS)
Principles of distributed database systems
Principles of distributed database systems
Distributed and Parallel Databases
Research problems in data warehousing
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Distributed Object Based Design: Vertical Fragmentation of Classes
Distributed and Parallel Databases
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
An integer programming approach for the view and index selection problem
Data & Knowledge Engineering
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Careful selection of aggregate views and some of their most used indexes to materialize in a data warehouse reduces the warehouse query response time as well as warehouse maintenance cost under some storage space constraint. Data Warehouses collect and store large amounts of integrated enterprise data from a number of independent data sources over a long period of time. Warehouse data are used for online analytical processing to assist management in making quick and competitive business decisions. Precomputing and storing summary tables (materialized views) reduces the amount of time needed to recompute these views across several source tables in order to answer complex warehouse queries. A data cube is an elegant way for representing aggregate information in a Warehouse and is an n-dimensional view with 2n subviews. This paper presents a uniform technique for selecting the subviews of the data cube and their indexes to materialize in order to produce the best resultant benefit to the system in terms of query response time and maintenance cost while satisfying some storage space constraint.