Parallel Discovery of Trajectory Companion Pattern and System Evaluation

• Item type: Journal(1)
• 公開日: 2020-09-15
• タイトル: Parallel Discovery of Trajectory Companion Pattern and System Evaluation
• タイトル / 言語: en
• タイトル: Parallel Discovery of Trajectory Companion Pattern and System Evaluation
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集：“Applications and the Internet” in Conjunction with Main Topics of COMPSAC2019（招待論文）] data stream processing, parallel computing, distributed computing, big data
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Gina Cody School of Engineering and Computer Science, Concordia University
• 著者所属: Gina Cody School of Engineering and Computer Science, Concordia University
• 著者所属: Gina Cody School of Engineering and Computer Science, Concordia University
• 著者所属: Microsoft Research
• 著者所属: Gina Cody School of Engineering and Computer Science, Concordia University
• 著者所属(英): en
• 著者所属(英): Gina Cody School of Engineering and Computer Science, Concordia University
• 著者所属(英): en
• 著者所属(英): Gina Cody School of Engineering and Computer Science, Concordia University
• 著者所属(英): en
• 著者所属(英): Gina Cody School of Engineering and Computer Science, Concordia University
• 著者所属(英): en
• 著者所属(英): Microsoft Research
• 著者所属(英): en
• 著者所属(英): Gina Cody School of Engineering and Computer Science, Concordia University
• 著者名: Yongyi, Xian; Yan, Liu; Chuanfei, Xu; Sameh, Elnikety; Elie, Neghawi
• 著者名(英): Yongyi, Xian; Yan, Liu; Chuanfei, Xu; Sameh, Elnikety; Elie, Neghawi
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Trajectories consist of spatial information of moving objects. Over contious time spans, trajectory data form data streams constantly generated from diverse and geographically distributed sources. Discovery of traveling patterns on trajectory streams such as gathering and companies enables value domain applications. Such a discovery needs to process arrival records in various sources and correlate across records near real-time. Thus techniques for handling trajectory streams should scale on distributed cluster computing. The challenge is at three aspects, namely a data model to represent the continuous trajectory data, the parallelism of the discovery algorithm, and an end-to-end parallel framework. In this paper, we propose a parallel discovery method that consists of 1) a model of partitioning trajectory samples on various time intervals; 2) definition on distance measurements of trajectories; and 3) a parallel discovery algorithm. We build a stream processing workflow and investigate experiments on a public dataset to evaluate the system's performance, scalability, stability, and data intensity. Our method discovers trajectory gathering patterns with low latency and scales as the size of trajectory data grows.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.28(2020) (online); DOI http://dx.doi.org/10.2197/ipsjjip.28.538; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Trajectories consist of spatial information of moving objects. Over contious time spans, trajectory data form data streams constantly generated from diverse and geographically distributed sources. Discovery of traveling patterns on trajectory streams such as gathering and companies enables value domain applications. Such a discovery needs to process arrival records in various sources and correlate across records near real-time. Thus techniques for handling trajectory streams should scale on distributed cluster computing. The challenge is at three aspects, namely a data model to represent the continuous trajectory data, the parallelism of the discovery algorithm, and an end-to-end parallel framework. In this paper, we propose a parallel discovery method that consists of 1) a model of partitioning trajectory samples on various time intervals; 2) definition on distance measurements of trajectories; and 3) a parallel discovery algorithm. We build a stream processing workflow and investigate experiments on a public dataset to evaluate the system's performance, scalability, stability, and data intensity. Our method discovers trajectory gathering patterns with low latency and scales as the size of trajectory data grows.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.28(2020) (online); DOI http://dx.doi.org/10.2197/ipsjjip.28.538; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 61, 号 9, 発行日 2020-09-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764