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15. A Demo of the Data Civilizer System.

Author

Raul Castro Fernandez, Dong Deng 0001, Essam Mansour 0001, Abdulhakim Ali Qahtan, Wenbo Tao, Ziawasch Abedjan, Ahmed K. Elmagarmid, Ihab F. Ilyas, Samuel Madden 0001, Mourad Ouzzani, Michael Stonebraker, and Nan Tang 0001

Published
2017
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21. Rheem: Enabling Multi-Platform Task Execution.

Author

Divy Agrawal, Mouhamadou Lamine Ba, Laure Berti-équille, Sanjay Chawla, Ahmed K. Elmagarmid, Hossam Hammady, Yasser Idris, Zoi Kaoudi, Zuhair Khayyat, Sebastian Kruse 0001, Mourad Ouzzani, Paolo Papotti, Jorge-Arnulfo Quiané-Ruiz, Nan Tang 0001, and Mohammed J. Zaki

Published
2016
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27. The Data Civilizer System.

Author

Dong Deng 0001, Raul Castro Fernandez, Ziawasch Abedjan, Sibo Wang 0001, Michael Stonebraker, Ahmed K. Elmagarmid, Ihab F. Ilyas, Samuel Madden 0001, Mourad Ouzzani, and Nan Tang 0001

Published
2017

48. Horizon

Author

Mourad Ouzzani, Ahmed K. Elmagarmid, El Kindi Rezig, Walid G. Aref, Ahmed R. Mahmood, and Michael Stonebraker

Subjects
Mathematical optimization, Dependency (UML), Horizon (archaeology), Computer science, Scalability, General Engineering
Abstract

A large class of data repair algorithms rely on integrity constraints to detect and repair errors. A well-studied class of constraints is Functional Dependencies (FDs, for short). Although there has been an increased interest in developing general data cleaning systems for a myriad of data errors, scalability has been left behind. This is because current systems assume data cleaning is performed offline and in one iteration. However, developing data science pipelines is highly iterative and requires efficient cleaning techniques to scale to millions of records in seconds/minutes, not days. In our efforts to re-think the data cleaning stack and bring it to the era of data science, we introduce Horizon , an end-to-end FD repair system to address two key challenges: (1) Accuracy: Most existing FD repair techniques aim to produce repairs that minimize changes to the data that may lead to incorrect combinations of attribute values (or patterns). Horizon leverages the interaction between the data patterns induced by the various FDs, and subsequently selects repairs that preserve the most frequent patterns found in the original data, and hence leading to a better repair accuracy. (2) Scalability: Existing data cleaning systems struggle when dealing with large-scale real-world datasets. Horizon features a linear-time repair algorithm that scales to millions of records, and is orders-of-magnitude faster than state-of-the-art cleaning algorithms. A benchmark of Horizon against state-of-the-art cleaning systems on multiple datasets and metrics shows that Horizon consistently outperforms existing techniques in repair quality and scalability.

Published
2021
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