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Measuring Data Believability: A Provenance Approach


  • 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 quality, measured along three dimensions: trustworthiness, reasonableness, and temporality. We ground our approach on provenance, i.e. the origin and subsequent processing history of data. We present our provenance model and our approach for computing believability based on provenance metadata. The approach is structured into three increasingly complex building blocks: (1) definition of metrics for assessing the believability of data sources, (2) definition of metrics for assessing the believability of data resulting from one process run and (3) assessment of believability based on all the sources and processing history of data. We illustrate our approach with a scenario based on Internet data. To our knowledge, this is the first work to develop a precise approach to measuring data believability and making explicit use of provenance-based measurements.

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  • Prat, Nicolas & Madnick, Stuart E., 2008. "Measuring Data Believability: A Provenance Approach," Working papers 40086, Massachusetts Institute of Technology (MIT), Sloan School of Management.
  • Handle: RePEc:mit:sloanp:40086

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    1. Steven Tadelis, 2003. "Firm reputation with hidden information," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 21(2), pages 635-651, March.
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    1. 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.

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