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Big Complex Biomedical Data: Towards a Taxonomy of Data

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E-Business and Telecommunications (ICETE 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 455))

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Abstract

Professionals in the Life Sciences are faced with increasing masses of complex data sets. Very few data is structured, where traditional information retrieval methods work perfectly. A large portion of data is weakly structured; however, the majority falls into the category of unstructured data. To discover previously unknown knowledge from this data, we need advanced and novel methods to deal with the data from two aspects: time (e.g. information entropy) and space (e.g. computational topology). In this paper we show some examples of biomedical data and discuss a taxonomy of data with the specifics on medical data sets.

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Authors and Affiliations

  1. Research Unit Human-Computer Interaction, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, 8036, Graz, Austria

    Andreas Holzinger & Christof Stocker

  2. Institute for Bioinformatics and Translational Research, UMIT Tyrol, Hall in Tirol, Austria

    Matthias Dehmer

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  1. Andreas Holzinger
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  2. Christof Stocker
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  3. Matthias Dehmer
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Correspondence to Andreas Holzinger .

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Editors and Affiliations

  1. Monmouth University, West Long Branch, New Jersey, USA

    Mohammad S. Obaidat

  2. Polytechnic Institute of Setúbal, INSTICC, Setubal, Portugal

    Joaquim Filipe

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Holzinger, A., Stocker, C., Dehmer, M. (2014). Big Complex Biomedical Data: Towards a Taxonomy of Data. In: Obaidat, M., Filipe, J. (eds) E-Business and Telecommunications. ICETE 2012. Communications in Computer and Information Science, vol 455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44791-8_1

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  • DOI: https://doi.org/10.1007/978-3-662-44791-8_1

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