Frequent Pattern Mining in Massive Spatio-Temporal Data on Distributed Storage and Computing

Abstract

As the location-acquisition technologies become increasingly pervasive, tracking the movement of objects from trajectory datasets are more and more available. As a result, discovering frequent movement patterns from such a dataset has recently gained great interest. However, trajectory dataset is usually large in volume and exceeds the computation capacity of traditional centralized technologies. We propose a new approach to discovering patterns over a massive data set based on distributed storage and computing. We apply the proposed approach to different real-world datasets in different conditions. We also discuss the results and possible future research directions.