Mining semantic trajectory patterns from geo-tagged data

Cai, Guochen, Lee, Kyungmi, and Lee, Ickjai (2018) Mining semantic trajectory patterns from geo-tagged data. Journal of computer science and technology, 33 (4). pp. 849-862.

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Abstract

User-generated social media data tagged with geographic information present messages of dynamic spatio-temporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding of human mobility behaviors. Several trajectory data mining approaches have been proposed to benefit from these rich datasets, but fail to incorporate aspatial semantics in mining. This study investigates mining frequent moving sequences of geographic entities with transit time from geo-tagged data. Different from previous analysis of geographic feature only trajectories, this work focuses on extracting patterns with rich context semantics. We extend raw geographic trajectories generated from geo-tagged data with rich context semantic annotations, use regions-of-interest as stops to represent interesting places, enrich them with multiple aspatial semantic annotations, and propose a semantic trajectory pattern mining algorithm that returns basic and multidimensional semantic trajectory patterns. Experimental results demonstrate that semantic trajectory patterns from our method present semantically meaningful patterns and display richer semantic knowledge.

Item ID: 55003
Item Type: Article (Refereed Research - C1)
Keywords: semantic trajectory, spatio-temporal, geo-tagged data, trajectory pattern mining
ISSN: 1860-4749
Date Deposited: 13 Aug 2018 22:58
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890299 Computer Software and Services not elsewhere classified @ 100%
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