Optimizing Encoded Bitmap Index Using Frequent Itemsets Mining

Indexing techniques based on bitmap representations are well suited to a warehouse system. They significantly improve query processing time by utilizing low-cost Boolean operations and multiple index scans, executing queries by performing simple predicate conditions on the index level before going to the primary data source. To optimize existing Encoded Bitmap Index, in this paper, we apply a data mining technique called frequent itemsets mining to find a well-defined encoding scheme, leading to improve query processing time. Our comparative study show that in the best case the performance of optimizing Encoded Bitmap Index using frequent itemsets mining is better than those found by existing techniques for membership queries from the point of view of space-time trade-off.