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Data Science in Practice pp 61–78Cite as

Information Fusion

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Part of the book series: Studies in Big Data ((SBD,volume 46))

Abstract

The study of information fusion comprises methods and techniques to automatically or semi-automatically combine information stemming from homogeneous or heterogeneous sources into a representation that supports a human user’s situation awareness for the purposes of decision making. Information fusion is not an end in itself but studies, adapts, applies and combines methods, techniques and algorithms provided by many other research areas, such as artificial intelligence, data mining, machine learning and optimization, in order to customize solutions for specific tasks. There are many different models for information fusion that describe the overall process as tasks building upon each other on different levels of abstraction. Information fusion includes the analysis of information, the inference of new information and the evaluation of uncertainty within the information. Hence, uncertainty management plays a vital role within the information fusion process. Uncertainty can be expressed by probability theory or, in the form of non-specificity and discord, by, for example, evidence theory.

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Author information

Authors and Affiliations

  1. University of Skövde, Skövde, Sweden

    H. Joe Steinhauer & Alexander Karlsson

Authors
  1. H. Joe Steinhauer
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  2. Alexander Karlsson
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Correspondence to H. Joe Steinhauer .

Editor information

Editors and Affiliations

  1. University of Skövde, Skövde, Sweden

    Alan Said

  2. University of Skövde, Skövde, Sweden

    Vicenç Torra

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Steinhauer, H.J., Karlsson, A. (2019). Information Fusion. In: Said, A., Torra, V. (eds) Data Science in Practice. Studies in Big Data, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-319-97556-6_4

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