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INCA: Inconsistency-Aware Data Profiling and Querying

Authors:

Ousmane Issa,

Angela Bonifati,

Farouk ToumaniAuthors Info & Claims

SIGMOD '21: Proceedings of the 2021 International Conference on Management of Data

Pages 2745 - 2749

https://doi.org/10.1145/3448016.3452760

Published: 18 June 2021 Publication History

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Abstract

When exploring and querying inconsistent data, inconsistency measures referring to constraint violations can help the user to quantify the quality of the underlying data and query results. We showcase INCA, a system that allows the user to execute data profiling and query answering tasks in an inconsistency-aware fashion. By using data instances annotated with novel inconsistency measures based on why-provenance and polynomial provenance, it becomes possible to visualize the share of the data which is consistent or inconsistent with respect to one or multiple denial constraints. Furthermore, data exploration by constraint or by subset of constraints allows to inspect the tuple violations according to multifaceted criteria. Finally, query profiling allows to enable inconsistency-aware query results accounting for most (in-)consistent top-k and threshold query results. To the best of our knowledge, INCA is the first system to allow such an inconsistency-driven analysis of both data and query results. Such an analysis is especially fruitful for enabling selective constraint-based data cleaning and inconsistency-aware ranking of query results in data science pipelines, thus leading to more explainable outputs of those processes.

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When exploring and querying inconsistent data, inconsistency measures referring to constraint violations can help the user to quantify the quality of the underlying data and query results. We showcase INCA, a system that allows the user to execute data profiling and query answering tasks in an inconsistency-aware fashion. By using data instances annotated with novel inconsistency measures based on why-provenance and polynomial provenance, it becomes possible to visualize the share of the data which is consistent or inconsistent with respect to one or multiple denial constraints. Furthermore, data exploration by constraint or by subset of constraints allows to inspect the tuple violations according to multifaceted criteria. Finally, query profiling allows to enable inconsistency-aware query results accounting for most (in-)consistent top-k and threshold query results. To the best of our knowledge, INCA is the first system to allow such an inconsistency-driven analysis of both data and query results. Such an analysis is especially fruitful for enabling selective constraint-based data cleaningand inconsistency-aware ranking of query results in data science pipelines, thus leading to more explainable outputs of those processes.

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Cited By

View all

Grant JParisi F(2024)On Measuring Inconsistency in Graph Databases with Regular Path ConstraintsArtificial Intelligence10.1016/j.artint.2024.104197(104197)Online publication date: Aug-2024
https://doi.org/10.1016/j.artint.2024.104197
Sancricca CSiracusa GCappiello C(2024)Enhancing data preparation: insights from a time series case studyJournal of Intelligent Information Systems10.1007/s10844-024-00867-8Online publication date: 25-Jul-2024
https://doi.org/10.1007/s10844-024-00867-8
Parisi FGrant JElkind E(2023)Relative inconsistency measures for indefinite databases with denial constraintsProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/370(3321-3329)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/370

Index Terms

INCA: Inconsistency-Aware Data Profiling and Querying
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Data model extensions
        Data provenance
        Inconsistent data
  2. Information retrieval
    1. Retrieval models and ranking
      1. Top-k retrieval in databases

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Published In

SIGMOD '21: Proceedings of the 2021 International Conference on Management of Data

June 2021

2969 pages

ISBN:9781450383431

DOI:10.1145/3448016

General Chairs:
Guoliang Li
Tsinghua University (China)
,
Zhanhuai Li
Northwestern Polytechnical University (China)
,
Program Chairs:
Stratos Idreos
Harvard University (USA)
,
Divesh Srivastava
AT&T (USA)

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 June 2021

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Funding Sources

ANR (grant nr. 18-CE23-0002 QualiHealth)

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SIGMOD/PODS '21

Sponsor:

SIGMOD

SIGMOD/PODS '21: International Conference on Management of Data

June 20 - 25, 2021

Virtual Event, China

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

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Grant JParisi F(2024)On Measuring Inconsistency in Graph Databases with Regular Path ConstraintsArtificial Intelligence10.1016/j.artint.2024.104197(104197)Online publication date: Aug-2024
https://doi.org/10.1016/j.artint.2024.104197
Sancricca CSiracusa GCappiello C(2024)Enhancing data preparation: insights from a time series case studyJournal of Intelligent Information Systems10.1007/s10844-024-00867-8Online publication date: 25-Jul-2024
https://doi.org/10.1007/s10844-024-00867-8
Parisi FGrant JElkind E(2023)Relative inconsistency measures for indefinite databases with denial constraintsProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/370(3321-3329)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/370

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