Mining spatiotemporal co-occurrence patterns from massive data sets with evolving regions

Abstract

Due to the current rates of data acquisition, the growth of data volumes in nearly all domains of our lives is reaching historic proportions [5], [6], [7]. Spatiotemporal data mining has emerged in recent decades with the main goal focused on developing data-driven mechanisms for the understanding of the spatiotemporal characteristics and patterns occurring in the massive repositories of data. This work focuses on discovering spatiotemporal co-occurrence patterns (STCOPs) from large data sets with evolving regions. Spatiotemporal co-occurrence patterns represent the subset of event types that occur together in both space and time. Major limitations of existing spatiotemporal data mining models and techniques include the following. First, they do not take into account continuously evolving spatiotemporal events that have polygon-like representations. Second, they do not investigate and provide sufficient interest measures for the STCOPs discovery purposes. Third, computationally and storage efficient algorithms to discover STCOPs are missing. These limitations of existing approaches represent important hurdles while analyzing massive spatiotemporal data sets in several application domains that generate big data, including solar physics, which is an application of our interdisciplinary research. In this work, we address these limitations by i) introducing the problem of mining STCOPs from data sets with extended (region-based) spatial representations that evolve over time, ii) developing a set of novel interest measures, and iii) providing a novel framework to model STCOPs. We also present and investigate three novel approaches to STCOPs mining. We follow this investigation by applying our algorithm to perform a novel data-driven discovery of STCOPs from solar physics data.