Querying data warehouses efficiently using the Bitmap Join Index OLAP Tool

Data warehouse and OLAP are core aspects of business intelligence environments, since the former store integrated and time-variant data, while the latter enables multidimensional queries, visualization and analysis. The bitmap join index has been recognized as an efficient mechanism to speed up queries over data warehouses. However, existing OLAP tools does not use strictly this index to improve the performance of query processing. In this paper, we introduce the BJIn OLAP Tool to efficiently perform OLAP queries over data warehouses, such as roll-up, drill-down, slice-and-dice and pivoting, by employing the bitmap join index. The BJIn OLAP Tool was implemented and tested through a performance evaluation to assess its efficiency and to corroborate the feasibility of adopting the bitmap join index to execute OLAP queries. The performance results reported that our BJIn OLAP Tool provided a performance gain that ranged from 31% up to 97% if compared to existing solutions regarding the query processing. Our tool has proven not only to efficiently process queries, but also to process OLAP operations on the server and client sides, for different volumes of data and taking into account different operating systems. Besides, it provides a reasonable use of the main memory and enables new rows to be appended to bitmap join indices.