ACM Transactions on Database Systems (TODS)
Improved query performance with variant indexes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The difficulty of optimum index selection
ACM Transactions on Database Systems (TODS)
Proceedings of the ninth international conference on Information and knowledge management
Efficient Join-Index-Based Spatial-Join Processing: A Clustering Approach
IEEE Transactions on Knowledge and Data Engineering
Adaptive and Automated Index Selection in RDBMS
EDBT '92 Proceedings of the 3rd International Conference on Extending Database Technology: Advances in Database Technology
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Multi-Dimensional Database Allocation for Parallel Data Warehouses
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Index Selection for Databases: A Hardness Study and a Principled Heuristic Solution
IEEE Transactions on Knowledge and Data Engineering
Optimizing bitmap indices with efficient compression
ACM Transactions on Database Systems (TODS)
Secondary bitmap indexes with vertical and horizontal partitioning
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
A genetic algorithm for the index selection problem
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Yet another algorithms for selecting bitmap join indexes
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
On simplifying integrated physical database design
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
Automatic selection of bitmap join indexes in data warehouses
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Time-HOBI: Index for optimizing star queries
Information Systems
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Multi-table indexes boost the performance of extremely large databases by reducing the cost of joins involving several tables. The bitmap join indexes ($\mathcal{B}\mathcal{J}\mathcal{I}$) are one of the most popular examples of this category of indexes. They are well adapted for point and range queries. Note that the selection of multi-table indexes is more difficult than the mono-table indexes, considered as the pioneer of database optimisation problems. The few studies dealing with the $\mathcal{B}\mathcal{J}\mathcal{I}$ selection problem in the context of relational data warehouses have three main limitations: (i) they consider $\mathcal{B}\mathcal{J}\mathcal{I}$ defined on only two tables (a fact table and a dimension table) by the use of one or several attributes of that dimension table, (ii) they use simple greedy algorithms to pick the right indexes and (iii) their algorithms are static. In this paper, we propose genetic algorithms for selecting $\mathcal{B}\mathcal{J}\mathcal{I}$ using a large number of attributes belonging to n (≥2) dimension tables in the static and incremental ways. Intensive experiments are conducted to show the efficiency of our proposal.