Towards Adaptive Continuous Trajectory Clustering Over a Distributed Web Data Stream

Authors

  • Yang Wu School of Computer Science and Technology, Soochow University, China
  • Junhua Fang School of Computer Science and Technology, Soochow University, China
  • Pingfu Chao School of Computer Science and Technology, Soochow University, China
  • Zhicheng Pan School of Data Science and Engineering, East China Normal University, Shanghai, China
  • Wei Chen School of Computer Science and Technology, Soochow University, China
  • Lei Zhao School of Computer Science and Technology, Soochow University, China

DOI:

https://doi.org/10.13052/jwe1540-9589.2216

Keywords:

Spatio-temporal data, continuous trajectory clustering, distributed stream processing, trajectory analysis

Abstract

With the popularity of modern mobile devices and GPS technology, big web stream data with location are continuously generated and collected. The sequential positions form a trajectory, and the clustering analysis on trajectories is beneficial to a wide range of applications, e.g., route recommendation. In the past decades, extensive efforts have been made to improve the efficiency of static trajectory clustering. However, trajectory stream data is received incrementally, and the continuous trajectory clustering inevitably faces the following two problems: (1) physical structure design for trajectory representation leads to severe space overhead, and (2) dynamic maintenance of trajectory semantics and its retrieval structure brings intensive computation. To overcome the above problems, an adaptive continuous trajectory clustering framework (ACTOR) is proposed in this paper. Overall, it covers three key components: (1) Simplifier represents trajectory with a well-designed PT structure. (2) Partitioner utilizes a hexagonal-based indexing strategy to enhance the local computational efficiency. (3) Executor accommodates an adaptive selection of P-clustering and R-clustering approaches according to the ROC (rate of change) matrix. Empirical studies on real-world data validate the usefulness of our proposal and prove the huge advantage of our approach over available solutions in the literature.

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Author Biographies

Yang Wu, School of Computer Science and Technology, Soochow University, China

Yang Wu is a postgraduate student at Soochow University. His research interests mainly include trajectory data analysis and trajectory data stream clustering.

Junhua Fang, School of Computer Science and Technology, Soochow University, China

Junhua Fang is an associate professor of Soochow University. His research interests mainly include spatio-temporal database, cloud computing, and distributed stream processing.

Pingfu Chao, School of Computer Science and Technology, Soochow University, China

Pingfu Chao is an associate professor of Soochow University. His research interests mainly include spatial-temporal data management, trajectory data mining, distributed database system and smart grid data analysis.

Zhicheng Pan, School of Data Science and Engineering, East China Normal University, Shanghai, China

Zhicheng Pan is a Ph.D. student at East China Normal University. His research interests mainly include spatio-temporal databases and machine learning for database.

Wei Chen, School of Computer Science and Technology, Soochow University, China

Wei Chen is an associate professor of Soochow University. His research interests mainly include spatio-temporal data analysis, knowledge graph, heterogeneous information network and data fusion.

Lei Zhao, School of Computer Science and Technology, Soochow University, China

Lei Zhao is a professor of Soochow University. His research focuses on graph databases, social media analysis, query outsourcing, parallel and distributed computing.

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Published

2023-04-20

How to Cite

Wu, Y. ., Fang, J. ., Chao, P. ., Pan, Z. ., Chen, W. ., & Zhao, L. . (2023). Towards Adaptive Continuous Trajectory Clustering Over a Distributed Web Data Stream. Journal of Web Engineering, 22(01), 105–130. https://doi.org/10.13052/jwe1540-9589.2216

Issue

Section

ICWE2022