VIPTRA: Visualization and Interactive Processing on Big Trajectory Data

Xin Ding, Rui Chen, Lu Chen, Yunjun Gao, Christian Søndergaard Jensen

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4 Citationer (Scopus)

Abstrakt

Massive trajectory data is being collected and used widely in many applications such as transportation, location-based services, and urban computing. As a result, abundant methods and systems have been proposed for managing and processing trajectory data. However, it remains difficult for users to interact well with data management and processing, due to the lack of efficient data processing methods and effective visualization techniques for big trajectory data. In this demonstration, we present a new framework, VIPTRA, to process big trajectory data visually and interactively. VIPTRA builds upon UlTraMan, a distributed in-memory system for big trajectory data, and thus, it takes advantage of its capability of high performance. The demonstration shows the efficiency of data processing and user-friendly visualization and interaction techniques provided in VIPTRA, via several scenarios of visual analysis and trajectory editing tasks.
OriginalsprogEngelsk
TitelProceedings - 2018 IEEE 19th International Conference on Mobile Data Management, MDM 2018
Antal sider2
ForlagIEEE
Publikationsdato13 jul. 2018
Sider290-291
ISBN (Elektronisk)978-1-5386-4133-0
DOI
StatusUdgivet - 13 jul. 2018
Begivenhed19th IEEE International Conference on Mobile Data Management, MDM 2018 - Aalborg, Danmark
Varighed: 25 jun. 201828 jun. 2018

Konference

Konference19th IEEE International Conference on Mobile Data Management, MDM 2018
LandDanmark
ByAalborg
Periode25/06/201828/06/2018
SponsorAalborg University, Center for Data-Intensive Systems (DAISY), Aalborg University, IEEE, IEEE Technical Committee on Data Engineering (TCDE), Otto Monsted Foundation

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  • Citationsformater

    Ding, X., Chen, R., Chen, L., Gao, Y., & Jensen, C. S. (2018). VIPTRA: Visualization and Interactive Processing on Big Trajectory Data. I Proceedings - 2018 IEEE 19th International Conference on Mobile Data Management, MDM 2018 (s. 290-291). IEEE. https://doi.org/10.1109/MDM.2018.00055