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International Conference on Scalable Uncertainty Management

SUM 2010: Scalable Uncertainty Management pp 292–305Cite as

Handling Dirty Databases: From User Warning to Data Cleaning — Towards an Interactive Approach

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6379))

Abstract

One can conceive many reasonable ways of characterizing how dirty a database is with respect to a set of integrity constraints (e.g., functional dependencies). However, dirtiness measures, as good as they can be, are difficult to interpret for an end-user and do not give the database administrator much hint about how to clean the base. This paper discusses these aspects and proposes some methods aimed at either helping the user or the administrator overcome the limitations of dirtiness measures when it comes to handling dirty databases.

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Author information

Authors and Affiliations

  1. Irisa – Enssat, University of Rennes 1, Technopole Anticipa, 22305, Lannion Cedex, France

    Olivier Pivert

  2. IRIT, CNRS and University of Toulouse, 31062, Toulouse Cedex 9, France

    Henri Prade

Authors
  1. Olivier Pivert
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  2. Henri Prade
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Maryland, MD 20910, College Park, USA

    Amol Deshpande

  2. Department of Computer Science, University College London, Gower Street, WC1E 6BT, London, UK

    Anthony Hunter

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Pivert, O., Prade, H. (2010). Handling Dirty Databases: From User Warning to Data Cleaning — Towards an Interactive Approach. In: Deshpande, A., Hunter, A. (eds) Scalable Uncertainty Management. SUM 2010. Lecture Notes in Computer Science(), vol 6379. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15951-0_28

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  • DOI: https://doi.org/10.1007/978-3-642-15951-0_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15950-3

  • Online ISBN: 978-3-642-15951-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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