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Dynamic Group Trip Planning Queries in Spatial Databases

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Published:27 June 2017Publication History

ABSTRACT

In this paper, we introduce the concept of "dynamic groups" for Group Trip Planning (GTP) queries and propose a novel query type Dynamic Group Trip Planning (DGTP) queries. The traditional GTP query assumes that the group members remain static or fixed during the trip, whereas in the proposed DGTP queries, the group changes dynamically over the duration of a trip where members can leave or join the group at any point of interest (POI) such as a shopping center, a restaurant or a movie theater. The changes of members in a group can be either predetermined (i.e., group changes are known before the trip is planned) or in real-time (changes happen during the trip). In this paper, we provide efficient solutions for processing DGTP queries in the Euclidean space. A comprehensive experimental study using real and synthetic datasets shows that our efficient approach can compute DGTP query solutions within few seconds and significantly outperforms a naive approach in terms of query processing time and I/O access.

References

  1. Elham Ahmadi and Mario A. Nascimento. 2015. A Mixed Breadth-Depth First Search Strategy for Sequenced Group Trip Planning Queries. In MDM. 24--33. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Tanzima Hashem, Sukarna Barua, Mohammed Eunus Ali, Lars Kulik, and Egemen Tanin. 2015. Efficient Computation of Trips with Friends and Families. In CIKM. 931--940. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Tanzima Hashem, Tahrima Hashem, Mohammed Eunus Ali, and Lars Kulik. 2013. Group Trip Planning Queries in Spatial Databases. In SSTD. 259--276.Google ScholarGoogle Scholar
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  1. Dynamic Group Trip Planning Queries in Spatial Databases

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    • Published in

      cover image ACM Other conferences
      SSDBM '17: Proceedings of the 29th International Conference on Scientific and Statistical Database Management
      June 2017
      373 pages
      ISBN:9781450352826
      DOI:10.1145/3085504

      Copyright © 2017 ACM

      © 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 27 June 2017

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      • research-article
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      • Refereed limited

      Acceptance Rates

      Overall Acceptance Rate56of146submissions,38%

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