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Scout: A GPU-Aware System for Interactive Spatio-temporal Data Visualization

Authors:

Harshada Chavan,

Mohamed F. MokbelAuthors Info & Claims

SIGMOD '17: Proceedings of the 2017 ACM International Conference on Management of Data

Pages 1691 - 1694

https://doi.org/10.1145/3035918.3056444

Published: 09 May 2017 Publication History

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Abstract

This demo presents Scout; a full-fledged interactive data visualization system with native support for spatio-temporal data. Scout utilizes computing power of GPUs to achieve real-time query performance. The key idea behind Scout is a GPU-aware multi-version spatio-temporal index. The indexing and query processing modules of Scout are designed to complement the GPU hardware characteristics. Front end of Scout provides a user interface to submit queries and view results. Scout supports a variety of spatio-temporal queriesrange, k-NN, and join. We use real data sets to demonstrate scalability and important features of Scout.

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Cited By

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Lewis ZTu Y(2022)G-PICS: A Framework for GPU-Based Spatial Indexing and Query ProcessingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299244034:3(1243-1257)Online publication date: 1-Mar-2022
https://doi.org/10.1109/TKDE.2020.2992440
Magdy A(2020)MicroblogsSIGSPATIAL Special10.1145/3404820.340482712:1(41-52)Online publication date: 8-Jul-2020
https://dl.acm.org/doi/10.1145/3404820.3404827
Magdy AAbdelhafeez LKang YOng EMokbel M(2019)Microblogs data management: a surveyThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-019-00569-629:1(177-216)Online publication date: 18-Sep-2019
https://dl.acm.org/doi/10.1007/s00778-019-00569-6
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Index Terms

Scout: A GPU-Aware System for Interactive Spatio-temporal Data Visualization
1. Information systems
  1. Data management systems
    1. Information integration
      1. Extraction, transformation and loading

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Information & Contributors

Information

Published In

SIGMOD '17: Proceedings of the 2017 ACM International Conference on Management of Data

May 2017

1810 pages

ISBN:9781450341974

DOI:10.1145/3035918

General Chairs:
Rada Chirkova
North Carolina State University, USA
,
Jun Yang
Duke University, USA
,
Program Chair:
Dan Suciu
University of Washington, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 May 2017

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SIGMOD/PODS'17

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SIGMOD

SIGMOD/PODS'17: International Conference on Management of Data

May 14 - 19, 2017

Illinois, Chicago, USA

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

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Lewis ZTu Y(2022)G-PICS: A Framework for GPU-Based Spatial Indexing and Query ProcessingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299244034:3(1243-1257)Online publication date: 1-Mar-2022
https://doi.org/10.1109/TKDE.2020.2992440
Magdy A(2020)MicroblogsSIGSPATIAL Special10.1145/3404820.340482712:1(41-52)Online publication date: 8-Jul-2020
https://dl.acm.org/doi/10.1145/3404820.3404827
Magdy AAbdelhafeez LKang YOng EMokbel M(2019)Microblogs data management: a surveyThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-019-00569-629:1(177-216)Online publication date: 18-Sep-2019
https://dl.acm.org/doi/10.1007/s00778-019-00569-6
Nouri ZTu YSacharidis DGamper JBöhlen M(2018)GPU-based parallel indexing for concurrent spatial query processingProceedings of the 30th International Conference on Scientific and Statistical Database Management10.1145/3221269.3221296(1-12)Online publication date: 9-Jul-2018
https://dl.acm.org/doi/10.1145/3221269.3221296

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