A horizontal fragmentation algorithm for the fact relation in a distributed data warehouse
Proceedings of the eighth international conference on Information and knowledge management
Integrating vertical and horizontal partitioning into automated physical database design
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
SAGA: a combination of genetic and simulated annealing algorithms for physical data warehouse design
BNCOD'06 Proceedings of the 23rd British National Conference on Databases, conference on Flexible and Efficient Information Handling
Materialized view selection as constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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On-line analytical processing (OLAP) queries are strongly affected by the amount data needed to be accessed from the disk. Therefore, there is a need to employ techniques that can facilitate efficient execution of these queries. Data partitioning concept that has been studied in the context of relational databases aims to reduce query execution time and facilitate the parallel execution of queries. In this paper, we develop a new framework based on genetic algorithm for applying the partitioning technique on relational DW schema (star schema) to reduce the total query execution cost. We develop an analytical cost model for executing a set of OLAP queries on a partitioned star schema. We conduct experiments to evaluate the utility of partitioning in efficiently executing OLAP queries. Finally, we show how partitioning can be used to facilitate parallel execution of OLAP queries.