Principles of distributed database systems
Principles of distributed database systems
Research problems in data warehousing
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
A horizontal fragmentation algorithm for the fact relation in a distributed data warehouse
Proceedings of the eighth international conference on Information and knowledge management
A formal approach to the vertical partitioning problem in distributed database design
PDIS '93 Proceedings of the second international conference on Parallel and distributed information systems
Horizontal data partitioning in database design
SIGMOD '82 Proceedings of the 1982 ACM SIGMOD international conference on Management of data
Aggregate-Query Processing in Data Warehousing Environments
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A Case for Parallelism in Data Warehousing and OLAP
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
AutoPart: Automating Schema Design for Large Scientific Databases Using Data Partitioning
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database 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. There has been a lot of work to optimize the performance of relational data warehouses. Among the two fragmentation techniques, vertical fragmentation is often considered more complicated than horizontal, it nearly impossible to obtain an optimal solution. 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 minimize 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.