Spatio-Temporal Data Mining: A Survey of Problems and Methods

Authors:
Gowtham Atluri

University of Cincinnati, OH

University of Cincinnati, OH
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,
Anuj Karpatne

University of Minnesota, MN

University of Minnesota, MN

0000-0003-1647-3534
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,
Vipin Kumar

University of Minnesota, MN

University of Minnesota, MN
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Authors Info & Claims

ACM Computing Surveys Volume 51 Issue 4Article No.: 83pp 1–41https://doi.org/10.1145/3161602

Published:22 August 2018Publication History

ACM Computing Surveys

Abstract

Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains, including climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences. Spatio-temporal data differ from relational data for which computational approaches are developed in the data-mining community for multiple decades in that both spatial and temporal attributes are available in addition to the actual measurements/attributes. The presence of these attributes introduces additional challenges that needs to be dealt with. Approaches for mining spatio-temporal data have been studied for over a decade in the data-mining community. In this article, we present a broad survey of this relatively young field of spatio-temporal data mining. We discuss different types of spatio-temporal data and the relevant data-mining questions that arise in the context of analyzing each of these datasets. Based on the nature of the data-mining problem studied, we classify literature on spatio-temporal data mining into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining. We discuss the various forms of spatio-temporal data-mining problems in each of these categories.

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Index Terms

Spatio-Temporal Data Mining: A Survey of Problems and Methods
1. Applied computing
  1. Physical sciences and engineering
2. Computing methodologies
  1. Artificial intelligence
  2. Machine learning

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Published in
ACM Computing Surveys Volume 51, Issue 4
July 2019
765 pages
ISSN:0360-0300
EISSN:1557-7341
DOI:10.1145/3236632
Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL 32611
Issue’s Table of Contents
Copyright © 2018 ACM
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
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Publication History
- Published: 22 August 2018
- Revised: 1 November 2017
- Accepted: 1 November 2017
- Received: 1 September 2017
Published in csur Volume 51, Issue 4

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Data mining
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Spatio-Temporal Data Mining: A Survey of Problems and Methods

ACM Computing Surveys

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