A Demo of the Data Civilizer System
Name
sigdm1.pdf
Description
Accepted version
Size
777.53 KB
Format
Adobe PDF
Checksum (MD5)
cc4c5d355417ae21410f07fcf74b6e0b
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
Date Issued
May 14, 2017
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
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3035918.3058740
