Advances in Spatiotemporal Data Mining
Liu Dayou, Chen Huiling, Qi Hong, and Yang Bo
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In recent years, the widespread use of the advanced technologies such as global positioning systems, sensor network and mobile devices, results in accumulation of a great amount of non-spatiotemporal data and spatiotemporal data. In addition, the processing of spatiotemporal data is more complex, which makes the increasing onerous situation of data processing tasks worse. To address these challenges, spatiotemporal data mining has emerged as an active research field, focusing on the development of theory, methodology, and practice for the extraction of useful information and knowledge from massive and complex spatiotemporal databases. Therefore, looking for effective spatiotemporal data mining methods is of great significance. This paper attempts to review the recent theoretical and applied research progress in spatiotemporal data mining and knowledge discovery. We mainly focus on spatiotemporal pattern discovery, spatiotemporal clustering, spatiotemporal anomaly detection, spatiotemporal prediction, spatiotemporal classification, and the combination of spatiotemporal data mining with reasoning. We have introduced the state-of-the-art research on spatiotemporal data mining in detail, and discussed the current major problems we are facing and its possible solutions.