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dc.contributor.authorCastro Fernandez, Raul
dc.contributor.authorDeng, Dong
dc.contributor.authorMansour, Essam
dc.contributor.authorQahtan, Abdulhakim A.
dc.contributor.authorTao, Wenbo
dc.contributor.authorAbedjan, Ziawasch
dc.contributor.authorElmagarmid, Ahmed
dc.contributor.authorIlyas, Ihab F.
dc.contributor.authorMadden, Samuel R
dc.contributor.authorOuzzani, Mourad
dc.contributor.authorStonebraker, Michael
dc.contributor.authorTang, Nan
dc.contributor.authorWenbo, Tao
dc.date.accessioned2019-06-28T22:48:10Z
dc.date.available2019-06-28T22:48:10Z
dc.date.issued2017-05-14
dc.identifier.isbn978-1-4503-4197-4
dc.identifier.urihttps://hdl.handle.net/1721.1/121460
dc.description.abstractFinding relevant data for a specific task from the numerous data sources available in any organization is a daunting task. This is not only because of the number of possible data sources where the data of interest resides, but also due to the data being scattered all over the enterprise and being typically dirty and inconsistent. In practice, data scientists are routinely reporting that the majority (more than 80%) of their effort is spent finding, cleaning, integrating, and accessing data of interest to a task at hand. We propose to demonstrate Data Civilizer to ease the pain faced in analyzing data "in the wild". Data Civilizer is an end-to-end big data management system with components for data discovery, data integration and stitching, data cleaning, and querying data from a large variety of storage engines, running in large enterprises.en_US
dc.language.isoen
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1145/3035918.3058740en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcewebsiteen_US
dc.titleA Demo of the Data Civilizer Systemen_US
dc.typeArticleen_US
dc.identifier.citationCastro Fernandez, Raul, et al. “A Demo of the Data Civilizer System.” Proceedings of the 2017 ACM International Conference on Management of Data - SIGMOD ’17, New York, NY, USA, ACM Press, 2017, pp. 1639–42.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.relation.journalProceedings of the 2017 International Conference on Management of Data - SIGMOD '17en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2019-06-18T13:52:49Z
dspace.date.submission2019-06-18T13:52:50Z


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