Key concepts of group pattern discovery algorithms from spatio-temporal trajectories

Dluzniak, Karolina, Jie, Wei ORCID: https://orcid.org/0000-0002-5392-0009, Wang, Hai and Xing, Wei (2020) Key concepts of group pattern discovery algorithms from spatio-temporal trajectories. In: The 15th International Conference on Semantics, Knowledge and Grids On Big Data, AI and Future Interconnection Environment, 17-18 September 2019, Guangzhou, China.

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Abstract

Over the years, the increasing development of location acquisition devices have generated a significant amount of spatio-temporal data. This data can be further analysed in search for some interesting patterns, new information, or to construct predictive models such as next location prediction. The goal of this paper is to contribute to the future research and development of group pattern discovery algorithms from spatio-temporal data by providing an insight into algorithms design in this research area which is based on a comprehensive classification of state-of-the-art models. This work includes static, big data as well as data stream processing models which to the best of authors’knowledge is the first attempt of presenting them in this context.Furthermore, the currently available surveys and taxonomies in this research area do not focus on group pattern mining algorithms nor include the state-of-the-art models. The authors conclude with the proposal of a conceptual model of Universal,Streaming, Distributed and Parameter-light (UDSP) algorithm that addresses current challenges in this research area.

Item Type: Conference or Workshop Item (Paper)
ISSN: 2325-0623
ISBN: 9781728158235
Identifier: 10.1109/skg49510.2019.00039
Page Range: pp. 190-197
Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: spatio-temporal data mining, distributed trajectory mining, clustering
Subjects: Computing
Related URLs:
Depositing User: WEI JIE
Date Deposited: 20 Sep 2019 17:51
Last Modified: 23 Jun 2020 14:02
URI: http://repository.uwl.ac.uk/id/eprint/6400

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