Department of Computer Science

“Evolutionary Clustering of Moving Objects” Selected for ICDE Best Paper Award

“Evolutionary Clustering of Moving Objects” Selected for ICDE Best Paper Award

Tianyi Li, Christian S. Jensen, Torben Bach Pedersen, and Jilin Hu, Department of Computer Science, Aalborg University, together with Lu Chen and Yunjun Gao, College of Computer Science, Zhejiang University, Hangzhou, China, have received one of this year’s ICDE Best Paper Awards for their “Evolutionary Clustering of Moving Objects”.

The IEEE International Conference on Data Engineering (ICDE) is the flagship conference for the IEEE Technical Committee on Data Engineering. At this year’s conference in Kuala Lumpur, Malaysia, 780 research papers were submitted, 211 were accepted and out of those, the paper “Evolutionary Clustering of Moving Objects” was selected as one of the three winners of the ICDE Best Paper Award.

Improving the quality of clustering

With the widespread deployment of smartphones, networked in-vehicle devices with geo-positioning capabilities, and vessel tracking technologies, it has become feasible to collect and cluster the evolving geo-locations from land- and sea-based moving objects enabling a variety of real-time services, such as road traffic management and vessel collision risk assessment.

However, according to the authors, little attention has so far been given to the quality of the evolving geo-location clusters generated by land- and sea-based moving objects, and they propose the notion of evolutionary clustering of moving objects, abbreviated ECM, that enhances the quality of moving object clustering by means of temporal smoothing that prevents abrupt changes in clusters across successive timestamps.

A novel method

According to the Research Program Committee Chair Professor Gao Cong, Nanyang Technological University, Singapore, who presented the ICDE Best Paper Awards during an online meeting on Wednesday, May 11, “Evolutionary Clustering of Moving Objects” presents a novel method for the task of evolutionary clustering of moving objects:

- The new method, called the Evolutionary Clustering of moving Objects (ECO), considers the temporal smoothness of moving objects for improving the clustering quality. ECO is formulated as an optimization problem and optimization techniques are proposed to achieve state-of-the-art clustering quality and processing time for the task of evolutionary clustering of moving objects. The proposed solution clearly advances the state-of-the-art evolutionary clustering methods for moving objects.

Tianyi Li, Research Assistant in the Data Engineering, Science and Systems research group at the Department of Computer Science. Aalborg University, and the paper’s main author, expressed her gratitude for receiving the ICDE Best Paper Award:

- I am extremely honoured, thrilled and humbled to receive such an important award from ICDE, a top venue in the field of databases. I am earnestly grateful for the recognition of our paper.

The IEEE Computer Society

The IEEE Computer Society is the world’s top member organization dedicated to computer science and technology aiming at advancing the theory, design, practice, and application of computer and information-processing science and technology, as well as the professional standing of its members.

The resources of the IEEE Computer Society include international conferences, peer-reviewed publications, a robust digital library, globally recognized standards, and continuous learning opportunities.

FURTHER INFORMATION

 

CONTACT

Tianyi Li
Research Assistant
Department of Computer Science,
Aalborg University

Mail: tianyi@cs.aau.dk
Phone: +45 9940 8887

Stig Andersen
Communications Officer
Department of Computer Science,
Aalborg University

Mail: stan@cs.aau.dk
Phone: +45 4019 7682

 

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