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Mining semantic trajectory Patterns from Geo-Tagged Data

Journal of Computer Science & Technology 2018年第4期33卷 849-862页

作者： Guochen Cai Kyungmi Lee Ickjai Lee Information Technology Academy College of Business Law and Governance James Cook University Queensland QLD 4870 Australia

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.

关键词： semantic trajectory spatio-temporal geo-tagged data trajectory pattern mining

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SUDM-SP:A method for discovering trajectory similar users based on semantic privacy

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高置信计算(英文) 2023年第3期3卷 68-80页

作者： Weiqi Zhang Guisheng Yin Bingyi Xie College of Computer Science and Technology Harbin Engineering UniversityHarbin 150009China Department of Computer Science Georgia State UniversityGA 30303USA

With intelligent terminal devices’widespread adoption and global positioning systems’advancement,Location-based Social Networking Services(LbSNs)have gained considerable *** recommendation mechanism,which revolves around identifying similar users,holds significant importance in *** order to enhance user experience,LbSNs heavily rely on accurate *** mining and analyzing users who exhibit similar behavioral patterns to the target user,LbSNs can offer personalized services that cater to individual ***,trajectory data,a form encompassing various sensitive attributes,pose privacy *** disclosure of users’precise trajectory information can have severe consequences,potentially impacting their daily ***,this paper proposes the Similar User Discovery Method based on semantic Privacy(SUDM-SP)for trajectory *** approach involves employing a model that generates noise trajectories,maximizing expected noise to preserve the privacy of the original *** users are then identified based on the published noise trajectory ***-SP consists of two key ***,a puppet noise location,exhibiting the highest semantic expectation with the original location,is generated to derive noisesuppressed trajectory ***,a mechanism based on semantic and geographical distance is employed to cluster highly similar users into communities,facilitating the discovery of noise trajectory similarity among *** trials conducted using real datasets,the effectiveness of SUDM-SP,as a recommendation service ensuring user privacy protection is substantiated.

关键词： Differential privacy LbSNs semantic trajectory Recommendation

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Wuhan University Journal of Natural Sciences 2018年第4期23卷 347-354页

作者： WU Xia ZHU Yuanyuan PENG Yuwei PENG Zhiyong School of Computer Wuhan University

In recent years, a few researches focus on the similarity measure of semantic trajectories in road networks, since semantic trajectories in road networks have smaller volumes, higher qualities and can better reflect user behaviors. However, these works do not further discuss how to efficiently search similar trajectories. Thus, to implement an efficient similarity search, we design an index called SIET based on the structures of road networks. Then, we propose a novel algorithm called SSN-BF to search similar trajectories efficiently by using best-first strategy. At last, we take the experimental evaluations on real dataset and prove the efficiency of our algorithm.

关键词： semantic trajectory road network trajectory search similarity search

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A trajectory summarisation gener

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IET Cyber-Systems and Robotics 2023年第1期5卷 154-164页

作者： Weifeng Liu Liwen Ma Shaoyong Qu Zhangming Peng School of Electrical and Control Engineering Shaanxi University of Science and TechnologyXi'anChina School of Artificial Intelligence Hangzhou Dianzi UniversityHangzhouChina School of Mechanical Engineering Hangzhou Dianzi UniversityHangzhouChina

The semantic representation of the trajectory is conducive to enrich the content oftrajectory data mining. A trajectory summarisation generation method based on themobile robot behaviour analysis was proposed to realize the abstract expression andsemantic representation of the spatio-temporal motion features of the robot and itsenvironmental interaction state. First, the behavioural semantic modelling and representationof the mobile robot are completed by modelling the sub-trajectory andcalculating the topological behaviour (TOP). Second, Chinese word segmentation andsemantic slot filling methods are used to combine with hierarchical clustering to performbasic word extraction and classification for describing trajectory sentences. Then, thedescription language frame is extracted based on the TOP, and the final trajectorysummarisation is generated. The result shows that the proposed method can semanticallyrepresent robot behaviours with different motion features and topological features,extract two verb-frameworks for describing the sentences according to their topologicalfeatures, and dynamically adjust the syntactic structure for the different topological behavioursbetween the target and the environment. The proposed method can generatesemantic information of relatively high quality for spatio-temporal data and help tounderstand the higher-order semantics of moving robot behaviour.

关键词： behaviour analysis behaviour semantics natural language processing semantic trajectory trajectory summarisation

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