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Evaluating and Improving Data Fusion Accuracy

  • John R. TalburtEmail author
  • Daniel Pullen
  • Melody Penning
Chapter
Part of the Information Fusion and Data Science book series (IFDS)

Abstract

Information fusion is the process of combining different sources of information for use in a particular application. The production of almost every information product incorporates some level of data fusion. Poor implementation of data and information fusion will have an impact on many other key data processes, most particularly data quality management, data governance, and data analytics. In this chapter we focus on a particular type of data fusion process called entity-based data fusion (EBDF) and on the application of EBDF in high-risk applications where accuracy of the fusion must be very high. One of the foremost examples is in healthcare. Fusing information belonging to different patients or failing to bring together all of the information for the same patient can both have dire, even life-threatening, implications.

Keywords

Entity-based data fusion Probabilistic matching Precision Recall F-Measure Data quality management Quality control Quality assurance 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • John R. Talburt
    • 1
    Email author
  • Daniel Pullen
    • 1
  • Melody Penning
    • 2
  1. 1.University of Arkansas at Little RockLittle RockUSA
  2. 2.University of Arkansas for Medical SciencesLittle RockUSA

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