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Data polygamy : the many-many relationships among urban spatio-temporal data sets
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Chirigati, F., Doraiswamy, H., Damoulas, T. and Freire, J. (2016) Data polygamy : the many-many relationships among urban spatio-temporal data sets. In: ACM SIGMOD International Conference on Management of Data (SIGMOD 2016), San Francisco, 26 Jun - 01 Jul 2016. Published in: SIGMOD '16 Proceedings of the 2016 International Conference on Management of Data pp. 1011-1025. ISBN 9781450335317. doi:10.1145/2882903.2915245
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Official URL: http://dx.doi.org/10.1145/2882903.2915245
Abstract
The increasing ability to collect data from urban environments, coupled with a push towards openness by governments, has resulted in the availability of numerous spatio-temporal data sets covering diverse aspects of a city. Discovering relationships between these data sets can produce new insights by enabling domain experts to not only test but also generate hypotheses. However, discovering these relationships is difficult. First, a relationship between two data sets may occur only at certain locations and/or time periods. Second, the sheer number and size of the data sets, coupled with the diverse spatial and temporal scales at which the data is available, presents computational challenges on all fronts, from indexing and querying to analyzing them. Finally, it is nontrivial to differentiate between meaningful and spurious relationships. To address these challenges, we propose Data Polygamy, a scalable topology-based framework that allows users to query for statistically significant relationships between spatio-temporal data sets. We have performed an experimental evaluation using over 300 spatial-temporal urban data sets which shows that our approach is scalable and effective at identifying interesting relationships.
Item Type: | Conference Item (Paper) | ||||||
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Subjects: | H Social Sciences > HT Communities. Classes. Races Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Cities and towns -- Databases, Computational complexity | ||||||
Journal or Publication Title: | SIGMOD '16 Proceedings of the 2016 International Conference on Management of Data | ||||||
Publisher: | ACM | ||||||
ISBN: | 9781450335317 | ||||||
Official Date: | 26 June 2016 | ||||||
Dates: |
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Page Range: | pp. 1011-1025 | ||||||
DOI: | 10.1145/2882903.2915245 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Reuse Statement (publisher, data, author rights): | © ACM 2017. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Zhang, Jun, Cormode, Graham, Procopiuc, Cecilia, Srivastava, Divesh and Xiao, Xiaokui (2017) Privbayes : private data release via Bayesian networks. ACM Transactions on Database Systems, 42 (4). 25. doi:10.1145/3134428. | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 11 April 2016 | ||||||
Date of first compliant Open Access: | 11 August 2016 | ||||||
Funder: | National Science Foundation (U.S.) (NSF), Gordon and Betty Moore Foundation (GBMF), United States. Defense Advanced Research Projects Agency (DARPA) | ||||||
Grant number: | CNS-1229185, CI-EN-1405927 (NSF) | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | ACM SIGMOD International Conference on Management of Data (SIGMOD 2016) | ||||||
Type of Event: | Conference | ||||||
Location of Event: | San Francisco | ||||||
Date(s) of Event: | 26 Jun - 01 Jul 2016 | ||||||
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