Data civilizer 2.0: A holistic framework for data preparation and analytics

El Kindi Rezig, Lei Cao, Michael Stonebraker, Giovanni Simonini, Wenbo Tao, Samuel Madden, Mourad Ouzzani, Nan Tang, Ahmed K. Elmagarmid

Research output: Contribution to journalConference article

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)1954-1957
Number of pages4
JournalProceedings of the VLDB Endowment
Issue number12
Publication statusPublished - 1 Jan 2018
Event45th International Conference on Very Large Data Bases, VLDB 2019 - Los Angeles, United States
Duration: 26 Aug 201730 Aug 2017


ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)

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