Assessing accuracy degradation over time with a Markov-chain model

Alisa Wechsler, Adir Even

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

4 Scopus citations

Abstract

Accuracy, among the most discussed data quality dimensions in literature, reflects the extent to which data values match a baseline perceived to be correct – e.g., the true real-world attribute values, or another validated dataset. Even when data values are accurate when acquired, their accuracy may degrade over time - certain properties of real-world entities may change, while the data values that reflect them are not being updated. Drawing on that assumption, this study suggests a Markov-Chain model that describes accuracy degradation over time – this by assessing the likelihood of a data attribute to transition from one state to another within a given time period. Evaluation of the model with real-world data shows its potential contribution for a few key data-quality management tasks, such as the prediction of accuracy degradation, and the development of data auditing and maintenance policies.

Original languageEnglish
Title of host publicationProceedings of ICIQ 2012
Subtitle of host publication17th International Conference on Information Quality
EditorsLaure Berti-Equille, Isabelle Comyn-Wattiau, Monica Scannapieco
PublisherMIT
Pages99-110
Number of pages12
ISBN (Electronic)9781627483964
StatePublished - 1 Jan 2012
Event17th International Conference on Information Quality, ICIQ 2012 - Paris, France
Duration: 16 Nov 201217 Nov 2012

Conference

Conference17th International Conference on Information Quality, ICIQ 2012
Country/TerritoryFrance
CityParis
Period16/11/1217/11/12

Keywords

  • Accuracy
  • Currency
  • Data Quality
  • Markov-Chain Model

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