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Optimized Management of BIG Data Produced in Brain Disorder Rehabilitation

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Big Data Optimization: Recent Developments and Challenges

Part of the book series: Studies in Big Data ((SBD,volume 18))

Abstract

Brain disorders resulting from injury, disease, or health conditions can influence function of most parts of human body. Necessary medical care and rehabilitation is often impossible without close cooperation of several diverse medical specialists who must work jointly to choose methods that improve and support healing processes as well as to discover underlying principles. The key to their decisions are data resulting from careful observation or examination of the patient. We introduce the concept of scientific dataspace that involves and stores numerous and often complex types of data, e.g., the primary data captured from the application, data derived by curation and analytic processes, background data including ontology and workflow specifications, semantic relationships between dataspace items based on ontologies, and available published data. Our contribution applies big data and cloud technologies to ensure efficient exploitation of this dataspace, namely, novel software architectures, algorithms and methodology for its optimized management and utilization. We present its service-oriented architecture using a running case study and results of its data processing that involves mining and visualization of selected patterns optimized towards big and complex data we are dealing with.

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Notes

  1. 1.

    This categorization is analogous to [32] that, however, addresses the generic science development trajectory.

  2. 2.

    The oldest known medical record was written in 2150 BC in Summeria.

  3. 3.

    http://en.wikipedia.org/wiki/John_Snow_(physician), http://en.wikipedia.org/wiki/Typhoid_Mary.

  4. 4.

    The diagnostic effect of X-rays, used for medical X-ray and computed tomography was discovered in 1895. Electrocardiograph was invented in 1903, electroencephalogram later in 1924.

  5. 5.

    In the scientific data management research literature, “dataset” is more commonly used than “data set”.

  6. 6.

    We are aware that with this e-Science life cycle definition we cannot stop the development of scientific research methodology and, therefore, it is assumed that it will be actualized in the future.

  7. 7.

    An additional data flow to the dataspace can originate from the Web considered as “the biggest database” and information resource. There already exist advanced Web data extraction tools, e.g. [47].

  8. 8.

    Publications [10, 49] deal with the development of an epilepsy and seizure ontology and a data mining ontology, respectively.

  9. 9.

    Private cloud services operate solely for a single organization, typically managed and hosted internally or by a third-party, whereas public clouds services are offered by a service provider, they may be free or offered on a pay-per-usage model.

  10. 10.

    In a typical, almost standard notation, \(\rightarrow \) is used instead ‘then’.

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Acknowledgments

The work described in this chapter is being carried out as part of four projects, namely the bilateral Austrian-Czech S&T cooperation project “Optimizing Large-Scale Data-flows” granted by the OeAD-GmbH/BMWFW, the project “SPES: Support Patients through E-service Solutions” supported by the CENTRAL EUROPE 3CE286P2 programme, the Czech National Sustainability Program grant LO1401 provided by the Czech and Austrian Ministries of Research and Education, and the project SGS16/231/OHK3/3T/13 provided by CVUT in Prague. We also express our deep gratitude to Dr. Ibrahim Elsayed; our research projects presented in this book chapter expands on his pioneering work on scientific dataspace and scientific research life cycle modelling, and his vision for new applications of these novel paradigms. Unfortunately, Dr. Ibrahim Elsayed suddenly passed away before the realization of these ideas was possible. This book chapter is dedicated to his memory.

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Authors and Affiliations

  1. Faculty of Computer Science, University of Vienna, Vienna, Austria

    Peter Brezany & Marek Lenart

  2. SIX Research Center, Brno University of Technology, Brno, Czech Republic

    Peter Brezany

  3. Katedra Kybernetiky FEL CVUT, Prague, Czech Republic

    Olga Štěpánková & Miroslav Uller

  4. Joint Department of FBMI CVUT and CU, Prague, Czech Republic

    Markéta Janatová

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  1. Peter Brezany
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  3. Markéta Janatová
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  4. Miroslav Uller
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  5. Marek Lenart
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Correspondence to Peter Brezany .

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  1. Aston Business School, Aston University, Birmingham, United Kingdom

    Ali Emrouznejad

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Brezany, P., Štěpánková, O., Janatová, M., Uller, M., Lenart, M. (2016). Optimized Management of BIG Data Produced in Brain Disorder Rehabilitation. In: Emrouznejad, A. (eds) Big Data Optimization: Recent Developments and Challenges. Studies in Big Data, vol 18. Springer, Cham. https://doi.org/10.1007/978-3-319-30265-2_13

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  • DOI: https://doi.org/10.1007/978-3-319-30265-2_13

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