Raul Castro Fernandez
Ahmed Elmagarmid
Ihab F. Ilyas
Michael Stonebraker
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.
- D. Agrawal, M. L. Ba, L. Berti-Equille, S. Chawla, A. K. Elmagarmid, H. Hammady, Y. Idris, Z. Kaoudi, Z. Khayyat, S. Kruse, M. Ouzzani, P. Papotti, J. Quiané-Ruiz, N. Tang, and M. J. Zaki. Rheem: Enabling multi-platform task execution. In SIGMOD 2016. Google Scholar
Digital Library
- M. J. Cafarella, A. Y. Halevy, and N. Khoussainova. Data integration for the relational web. PVLDB, 2(1):1090--1101, 2009. Google ScholarDigital Library
- D. Deng, R. C. Fernandez, Z. Abedjan, S. Wang, A. Elmagarmid, I. F. Ilyas, S. Madden, M. Ouzzani, M. Stonebraker, and N. Tang. The Data Civilizer System. In CIDR, 2017.Google Scholar
- J. Duggan, A. J. Elmore, M. Stonebraker, M. Balazinska, B. Howe, J. Kepner, S. Madden, D. Maier, T. Mattson, and S. B. Zdonik. The bigdawg polystore system. SIGMOD Record, 44(2):11--16, 2015. Google ScholarDigital Library
- E. Rahm and H. H. Do. Data cleaning: Problems and current approaches. IEEE Data Eng. Bull., 23(4):3--13, 2000.Google Scholar
Index Terms
- A Demo of the Data Civilizer System
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