T-REx: Table Repair Explanations

Daniel Deutch, Nave Frost, Amir Gilad, Oren Sheffer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Data repair is a common and crucial step in many frameworks today, as applications may use data from different sources and of different levels of credibility. Thus, this step has been the focus of many works, proposing diverse approaches. To assist users in understanding the output of such data repair algorithms, we propose T-REx, a system for providing data repair explanations through Shapley values. The system is generic and not specific to a given repair algorithm or approach: it treats the algorithm as a black box. Given a specific table cell selected by the user, T-REx employs Shapley values to explain the significance of each constraint and each table cell in the repair of the cell of interest. T-REx then ranks the constraints and table cells according to their importance in the repair of this cell. This explanation allows users to understand the repair process, as well as to act based on this knowledge, to modify the most influencing constraints or the original database.

Original languageEnglish
Title of host publicationSIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages2765-2768
Number of pages4
ISBN (Electronic)9781450367356
DOIs
StatePublished - 14 Jun 2020
Externally publishedYes
Event2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020 - Portland, United States
Duration: 14 Jun 202019 Jun 2020

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Conference

Conference2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020
Country/TerritoryUnited States
CityPortland
Period14/06/2019/06/20

Keywords

  • data repairs
  • database constraints
  • shapley value

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