Querying and exploring polygamous relationships in urban spatio-temporal data sets

Yeuk Yin Chan, Fernando Chirigati, Harish Doraiswamy, Cláudio T. Silva, Juliana Freire

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationSIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages1643-1646
Number of pages4
ISBN (Electronic)9781450341974
DOIs
StatePublished - May 9 2017
Event2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017 - Chicago, United States
Duration: May 14 2017May 19 2017

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
VolumePart F127746
ISSN (Print)0730-8078

Other

Other2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017
CountryUnited States
CityChicago
Period5/14/175/19/17

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Keywords

  • Data Polygamy
  • Data set relationships
  • Urban data

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Chan, Y. Y., Chirigati, F., Doraiswamy, H., Silva, C. T., & Freire, J. (2017). Querying and exploring polygamous relationships in urban spatio-temporal data sets. In SIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data (pp. 1643-1646). (Proceedings of the ACM SIGMOD International Conference on Management of Data; Vol. Part F127746). Association for Computing Machinery. https://doi.org/10.1145/3035918.3058741