Parallel Algorithms for Mining Sequential Associations: Issues and Challenges

Abstract

Discovery of predictive sequential associations among events is becoming increasingly useful and essential in many scientific and commercial domains. Enormous sizes of available datasets and possibly large number of mined associations demand efficient and scalable parallel algorithms. In this paper, we first present a concept of universal sequential associations. Developing parallel algorithms for discovering such associations becomes quite challenging depending on the nature of the input data and the timing constraints imposed on the desired associations. We discuss possible challenging scenarios, and propose four different parallel algorithms that cater to various situations. This paper is written to serve as a comprehensive account of the design issues and challenges involved in parallelizing sequential association discovery algorithms.