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Querying and Exploring Polygamous Relationships in Urban Spatio-Temporal Data Sets

Authors:

Juliana FreireAuthors Info & Claims

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

Pages 1643 - 1646

https://doi.org/10.1145/3035918.3058741

Published: 09 May 2017 Publication History

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Abstract

The Data Polygamy framework allows users to uncover interesting patterns and interactions in the data exhaust from different components of an urban environment. But analyzing the plethora of relationships derived by the framework is challenging. In this demo, we show how visualization can help in the discovery of relationships that are potentially interesting by allowing users to query and explore the relationship set in an intuitive way. We will demonstrate the effectiveness of the visual interface through case studies, and demo visitors will also interact with the polygamous relationships.

References

[1]

L. Barbosa, K. Pham, C. Silva, M. Vieira, and J. Freire. Structured Open Urban Data: Understanding the Landscape. Big Data, 2(3), 2014.

Google Scholar

[2]

F. Chirigati, H. Doraiswamy, T. Damoulas, and J. Freire. Data Polygamy: The Many-Many Relationships Among Urban Spatio-Temporal Data Sets. In Proceedings of the 2016 International Conference on Management of Data, SIGMOD '16, pages 1011--1025, 2016.

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Google Scholar

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NYC Open Data. NYC Open Data. https://nycopendata.socrata.com, 2017. Accessed: 2017-01-15.

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P. Riehmann, M. Hanfler, and B. Froehlich. Interactive Sankey Diagrams. In IEEE Symposium on Information Visualization, 2005. INFOVIS 2005., pages 233--240. IEEE, 2005.

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Cited By

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Eichmann PSolleza FTatbul NZdonik SBoncz PManegold SAilamaki ADeshpande AKraska T(2019)Visual Exploration of Time Series Anomalies with Metro-VizProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3320247(1901-1904)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3299869.3320247
Rocha LBessa AChirigati FOFriel EMoro MFreire J(2019)Understanding Spatio-Temporal Urban Processes2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006289(563-572)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9006289
Chan GXu PDai ZRen L(2018)ViBr: Visualizing Bipartite Relations at Scale with the Minimum Description Length PrincipleIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2018.286482625:1(321-330)Online publication date: 7-Dec-2018
https://dl.acm.org/doi/10.1109/TVCG.2018.2864826

Index Terms

Querying and Exploring Polygamous Relationships in Urban Spatio-Temporal Data Sets
1. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Data analytics
    2. Spatial-temporal systems

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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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Short-paper

Funding Sources

National Science Foundation
DARPA Memex program
Moore-Sloan Data Science Environment

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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

View all

Eichmann PSolleza FTatbul NZdonik SBoncz PManegold SAilamaki ADeshpande AKraska T(2019)Visual Exploration of Time Series Anomalies with Metro-VizProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3320247(1901-1904)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3299869.3320247
Rocha LBessa AChirigati FOFriel EMoro MFreire J(2019)Understanding Spatio-Temporal Urban Processes2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006289(563-572)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9006289
Chan GXu PDai ZRen L(2018)ViBr: Visualizing Bipartite Relations at Scale with the Minimum Description Length PrincipleIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2018.286482625:1(321-330)Online publication date: 7-Dec-2018
https://dl.acm.org/doi/10.1109/TVCG.2018.2864826

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Recommendations

Data Polygamy: The Many-Many Relationships among Urban Spatio-Temporal Data Sets

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