Spatiotemporal Data Mining: A Survey

TL;DR

This survey provides an updated overview of spatiotemporal data mining techniques, emphasizing recent advances and parallel computing methods to handle large-scale data across various scientific domains.

Contribution

It offers a comprehensive, current review of spatiotemporal data mining methods, including detailed coverage of parallel processing techniques not addressed in previous surveys.

Findings

Updated survey of recent spatiotemporal data mining methods

Detailed analysis of parallel processing techniques

Highlights challenges and future directions in the field

Abstract

Spatiotemporal data mining aims to discover interesting, useful but non-trivial patterns in big spatial and spatiotemporal data. They are used in various application domains such as public safety, ecology, epidemiology, earth science, etc. This problem is challenging because of the high societal cost of spurious patterns and exorbitant computational cost. Recent surveys of spatiotemporal data mining need update due to rapid growth. In addition, they did not adequately survey parallel techniques for spatiotemporal data mining. This paper provides a more up-to-date survey of spatiotemporal data mining methods. Furthermore, it has a detailed survey of parallel formulations of spatiotemporal data mining.