IDEAS home Printed from
   My bibliography  Save this paper

Evaluating and Aggregating Data Believability across Quality Sub-Dimensions and Data Lineage


  • Prat, Nicolas
  • Madnick, Stuart E.


Data quality is crucial for operational efficiency and sound decision making. This paper focuses on believability, a major aspect of data quality. The issue of believability is particularly relevant in the context of Web 2.0, where mashups facilitate the combination of data from different sources. Our approach for assessing data believability is based on provenance and lineage, i.e. the origin and subsequent processing history of data. We present the main concepts of our model for representing and storing data provenance, and an ontology of the sub-dimensions of data believability. We then use aggregation operators to compute believability across the sub-dimensions of data believability and the provenance of data. We illustrate our approach with a scenario based on Internet data. Our contribution lies in three main design artifacts (1) the provenance model (2) the ontology of believability subdimensions and (3) the method for computing and aggregating data believability. To our knowledge, this is the first work to operationalize provenance-based assessment of data believability.

Suggested Citation

  • Prat, Nicolas & Madnick, Stuart E., 2008. "Evaluating and Aggregating Data Believability across Quality Sub-Dimensions and Data Lineage," Working papers 40085, Massachusetts Institute of Technology (MIT), Sloan School of Management.
  • Handle: RePEc:mit:sloanp:40085

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Donald P. Ballou & Harold L. Pazer, 1985. "Modeling Data and Process Quality in Multi-Input, Multi-Output Information Systems," Management Science, INFORMS, vol. 31(2), pages 150-162, February.
    2. Prat, Nicolas & Madnick, Stuart E., 2008. "Measuring Data Believability: A Provenance Approach," Working papers 40086, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    Full references (including those not matched with items on IDEAS)

    More about this item


    Data Lineage; Web 2.0;

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mit:sloanp:40085. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christian Zimmermann). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.