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A Demo of the Data Civilizer System

Author(s)
Castro Fernandez, Raul; Deng, Dong; Mansour, Essam; Qahtan, Abdulhakim A.; Tao, Wenbo; Abedjan, Ziawasch; Elmagarmid, Ahmed; Ilyas, Ihab F.; Madden, Samuel R; Ouzzani, Mourad; Stonebraker, Michael; Tang, Nan; Wenbo, Tao; ... Show more Show less
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Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
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Abstract
Finding 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.
Date issued
2017-05-14
URI
https://hdl.handle.net/1721.1/121460
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
Proceedings of the 2017 International Conference on Management of Data - SIGMOD '17
Publisher
Association for Computing Machinery (ACM)
Citation
Castro 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.
Version: Author's final manuscript
ISBN
978-1-4503-4197-4

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