Optimizing OLAP heterogeneous computing based on Rabin-Karp algorithm

The enormous amount of data has been boundlessly growing over the last few decades and expected to exponentially do so in the future. However, a substantial size of this accumulated amount is discarded anyhow. The processing capabilities have been considered as one of the major barriers in the way of exploiting this priceless mine. Therefore, the issue has absorbed considerable part of researchers’ concentration. OLAP has been considered as a powerful method for analysing excessive size of data that works closely to intelligent business, medical fields. Yet, such a method will always need increasing processing resources. Numerous enhancements have been suggested in order to improve OLAP performance, part of them has gone to the processing capabilities whereby parallel processing has occupied a sizeable space. A heterogeneous computing is a relatively recent approach that is being under examination. In this paper, through experimental results and based on Rabin-Karp Algorithm; we propose an optimized heterogeneous solution that takes into account the benefits and the boundaries in order to achieve a better OLAP performance in terms of response time with three times gain. In the light of our results; we present the achieved gain and possible future trends. Keywords— OLAP, heterogeneous computing, Rabin-Karp, data mining, pattern recognition