Using a Markov-Chain model for assessing accuracy degradation and developing data maintenance policies

Alisa Wechsler, Adir Even

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

6 Scopus citations

Abstract

Accuracy reflects the extent of correctness of data. It is often evaluated by comparing the values recorded to a baseline perceived as correct. Even when data values are accurate at the time of recording - their accuracy may degrade over time, as certain properties of real-world entities may change, while the data values that reflect them are not being updated. This study uses the Markov-Chain model to develop an analytical framework that describes accuracy degradation over time - this by assessing the likelihood of certain data attributes to transition between states within a given time period. Evaluation of the framework with real-world data shows its potential contribution for 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 publication18th Americas Conference on Information Systems 2012, AMCIS 2012
Pages2177-2182
Number of pages6
Volume3
StatePublished - 1 Dec 2012
Event18th Americas Conference on Information Systems 2012, AMCIS 2012 - Seattle, WA, United States
Duration: 9 Aug 201212 Aug 2012

Conference

Conference18th Americas Conference on Information Systems 2012, AMCIS 2012
Country/TerritoryUnited States
CitySeattle, WA
Period9/08/1212/08/12

Keywords

  • Accuracy
  • Currency
  • Data quality
  • Markov-Chain model

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Library and Information Sciences

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