March 9, 2017 03:46:21


Filter and Refine Approach

Spatiotemporal co-occurrence patterns (STCOPs) represent the subsets of event types that occur together in both space and time. However, the discovery of STCOPs in data sets with extended spatial representations that evolve over time is computationally expensive because of the necessity to calculate interest measures to assess the co-occurrence strength, and the number of candidates for STCOPs growing exponentially with the number of spatiotemporal event types. In this work, we introduce a novel and effective filter and-refine algorithm to efficiently find prevalent STCOPs in massive spatiotemporal data repositories with polygon shapes that move and evolve over time. We provide theoretical analysis of our approach, and follow this investigation with a practical evaluation of our algorithm effectiveness on three real-life data sets and one artificial data set.

Pattern Growth-Based Approach

Spatiotemporal co-occurrence pattern (STCOP) mining refers to discovering the subsets of event types whose instances frequently co-locate in a spatial context and coincide in a temporal context. STCOP mining is the spatiotemporal extension to Frequent Itemset Mining (FIM). Unlike the classical FIM approaches, which are applied on transactional databases, STCOP mining is applied on the spatiotemporal datasets comprised of event instances which are represented by evolving region trajectories. Previous STCOP mining algorithms are Apriori-based, where the number of candidate patterns can grow exponentially with the number of event types. In this work, we present a pattern growth-based approach for mining STCOPs, which allows us to discover STCOPs without computationally expensive candidate generation processes. We experimented our algorithm with four real-life solar event datasets and compared its performance with the earlier Apriori-based approach

Time-Efficient STCOP

Mining spatiotemporal co-occurrence patterns requires assessing the strength of co-occurrences among the instances of different feature types. Currently, a spatiotemporal version of the Jaccard measure is used for measuring the strength of spatiotemporal co-occurrences. We present an extended spatiotemporal version of the Jaccard measure (J*) that is more relevant and efficient for the task of STCOP mining. We also demonstrate the space and time efficiency of the J* with experimental evaluation.
 STCOP in Non-relational Databases