? 从地理标记数据中挖掘语义轨迹模式
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Journal of Computer Science and Technology 2018, Vol. 33 Issue (4) :849-862    DOI: 10.1007/s11390-018-1860-1
Special Issue on Software Engineering for High-Confidence Systems << Previous Articles | Next Articles >>
从地理标记数据中挖掘语义轨迹模式
Guochen Cai, Kyungmi Lee, Ickjai Lee*, Member, ACM
Information Technology Academy, College of Business, Law and Governance, James Cook University Queensland, QLD 4870, Australia
Mining Semantic Trajectory Patterns from Geo-Tagged Data
Guochen Cai, Kyungmi Lee, Ickjai Lee*, Member, ACM
Information Technology Academy, College of Business, Law and Governance, James Cook University Queensland, QLD 4870, Australia

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摘要 用户生成的标记了地理信息的社交媒体数据展现了动态时空轨迹信息。这些日益增长的流动性数据为提升对人们迁移行为的理解提供了潜在的机遇。现有好几个轨迹数据挖掘方法利用这些丰富的数据集,但它们不能在挖掘中合并非空间语义。本文调研了从地理标记数据中挖掘具有迁移时间的地理实体的频繁移动序列。与之前只有轨迹的地理特征的分析不同,本文主要提取具有丰富语境语义的模式。我们扩展了由地理标记数据生成的原始的地理轨迹,该数据具有丰富语境语义标注,使用地理兴趣表征名胜点,使用非空间语义标注丰富它们,并提出了一个语义轨迹模式挖掘算法,该算法返回基本的和多维的语义轨迹模式。实验结果表明我们方法所得到的语义轨迹模式在语义上呈现了语义上有意义的模式,并且展现了更加丰富的语义知识。
关键词语义轨迹   时空的   地理标记数据   轨迹模型挖掘     
Abstract: User-generated social media data tagged with geographic information present messages of dynamic spatiotemporal 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.
Keywordssemantic trajectory   spatio-temporal   geo-tagged data   trajectory pattern mining     
Received 2017-03-31;
通讯作者: Ickjai Lee,E-mail:Ickjai.Lee@jcu.edu.au     Email: Ickjai.Lee@jcu.edu.au
About author: Guochen Cai received his Ph.D. degree, in 2017, in information technology from James Cook University, Queensland. His current research areas include trajectory data mining, semantic trajectories, recommender systems, and spatio-temporal data mining.
引用本文:   
Guochen Cai, Kyungmi Lee, Ickjai Lee.从地理标记数据中挖掘语义轨迹模式[J]  Journal of Computer Science and Technology , 2018,V33(4): 849-862
Guochen Cai, Kyungmi Lee, Ickjai Lee.Mining Semantic Trajectory Patterns from Geo-Tagged Data[J]  Journal of Computer Science and Technology, 2018,V33(4): 849-862
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