UlTraMan

A Unified Platform for Big Trajectory Data Management and Analytics

Xin Ding, Lu Chen, Yunjun Gao, Christian Søndergaard Jensen, Hujun Bao

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

12 Citationer (Scopus)

Resumé

Massive trajectory data is being generated by GPS-equipped devices, such as cars and mobile phones, which is used increasingly in transportation, location-based services, and urban computing. As a result, a variety of methods have been proposed for trajectory data management and analytics. However, traditional systems and methods are usually designed for very specific data management or analytics needs, which forces users to stitch together heterogeneous systems to analyze trajectory data in an inefficient manner. Targeting the overall data pipeline of big trajectory data management and analytics, we present a unified platform, termed as UlTraMan. In order to achieve scalability, efficiency, persistence, and flexibility, (i) we extend Apache Spark with respect to both data storage and computing by seamlessly integrating a key-value store, and (ii) we enhance the MapReduce paradigm to allow flexible optimizations based on random data access. We study the resulting system's flexibility using case studies on data retrieval, aggregation analyses, and pattern mining. Extensive experiments on real and synthetic trajectory data are reported to offer insight into the scalability and performance of UlTraMan.
OriginalsprogEngelsk
TidsskriftProceedings of the VLDB Endowment
Vol/bind11
Udgave nummer7
Sider (fra-til)787-799
ISSN2150-8097
DOI
StatusUdgivet - 2018

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Information management
Trajectories
Scalability
Location based services
Electric sparks
Mobile phones
Global positioning system
Railroad cars
Agglomeration
Pipelines
Data storage equipment
Experiments

Citer dette

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UlTraMan : A Unified Platform for Big Trajectory Data Management and Analytics. / Ding, Xin; Chen, Lu; Gao, Yunjun; Jensen, Christian Søndergaard; Bao, Hujun .

I: Proceedings of the VLDB Endowment, Bind 11, Nr. 7, 2018, s. 787-799.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

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