Regional bitmap index: A secondary index for data management in Could computing environment

Abstract

The fast development of Cloud Computing technologies has brought new dawns to the storage and management of massive data. Nevertheless, due to the essential changes in the storage model, the matured indexing techniques used in traditional relational data management systems can neither be directly applied to massive data, nor be migrated to Cloud environment in an easy way. Based on comparisons between two basic approaches to secondary indexing, i. e. centralized and distributed approaches, the Regional Bitmap Index (RBI) is proposed to combine the advantages of both approaches and provide efficient supports to various queries against massive data in the Cloud. By means of fully utilizing the parallel computing resources provided by the Cloud, the query efficiency is dramatically improved. Meanwhile, based on global distribution information, RBI can avoid the unnecessary computing expenses on local nodes; therefore query throughputs can keep steady even if concurrency of the incoming queries increases. Experiments on real dataset show that the Regional Bitmap Index can significantly outperform other methods.