Data Civilizer 2.0: a holistic framework for data preparation and analytics
Name
3352063.3352108.pdf
Description
Published version
Size
480.57 KB
Format
Unknown
Checksum (MD5)
1c752b5e79fd5ce0dfd4ca0c7069ea7d
Author(s)
Rezig, El Kindi
Cao, Lei
Stonebraker, Michael
Simonini, Giovanni
Tao, Wenbo
Madden, Samuel R
Ouzzani, Mourad
Tang, Nan
Elmagarmid, Ahmed K
Date Issued
2019
Journal
Proceedings of the VLDB Endowment
Publisher
VLDB Endowment
Citation
Rezig, El Kindi, Cao, Lei, Stonebraker, Michael, Simonini, Giovanni, Tao, Wenbo et al. 2019. "Data Civilizer 2.0: a holistic framework for data preparation and analytics." Proceedings of the VLDB Endowment, 12 (12).
Version
Final published version
Abstract
© 2019 VLDB Endowment. Data scientists spend over 80% of their time (1) parameter-tuning machine learning models and (2) iterating between data cleaning and machine learning model execution. While there are existing efforts to support the first requirement, there is currently no integrated workflow system that couples data cleaning and machine learning development. The previous version of Data Civilizer was geared towards data cleaning and discovery using a set of pre-defined tools. In this paper, we introduce Data Civilizer 2.0, an end-to-end workflow system satisfying both requirements. In addition, this system also supports a sophisticated data debugger and a workflow visualization system. In this demo, we will show how we used Data Civilizer 2.0 to help scientists at the Massachusetts General Hospital build their cleaning and machine learning pipeline on their 30TB brain activity dataset.
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-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.14778/3352063.3352108
