Spatio-Temporal Data Mining

By Gowtham Atluri, Anuj Karpatne, Vipin Kumar. ACM Computing Surveys, 2017

301 citations recorded by the source corpus.

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.

Subfields: Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e0b16bb5d747d61a0f689a73354a5736a909378d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Philip S. Yu
2. Jiannong Cao
3. Senzhang Wang
4. Yongfeng Zhang
5. Jiawei Han
6. B. Liu
7. Bing Liu
8. Leman Akoglu

In Signal Processing

1. Philip S. Yu
2. Jiannong Cao
3. Senzhang Wang
4. Charu C. Aggarwal
5. Jiawei Han
6. Pavel Berkhin

Citations in the corpus

• Deep Learning for Spatio-Temporal Data Mining: A Survey (2019, methodology): “We follow and extend the categorization in [4], and classify the ST data into the following types: event data, trajectory data, point reference data, raster data, and videos.”