Mining Semantic Trajectory Patterns from Geo-Tagged Data

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.

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

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

Efficient Similarity Search Based on Semantic Trajectories in Road Networks

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.

A trajectory summarisation gener

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.