Spatiotemporal Data Mining: A Survey
Arun Sharma, Zhe Jiang, Shashi Shekhar

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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsData Mining Algorithms and Applications · Advanced Clustering Algorithms Research · Data Management and Algorithms
