Abstract
Mobile real-time decision support systems (RTDSS) find themselves deployed in a highly dynamic environment. Decision makers must be assisted, taking into account the various time-critical requirements. Perhaps even more important is the fact that the quality of the support given by the system depends heavily on the knowledge of the current and future contexts of the system. A DSS should exhibit inherent proactive behaviour and automatically derive the decision-making person (DMP)’s needs for specific information from the context that surrounds him/her. We propose to run a DSS on top of a middleware that helps the decision maker to contextualise information. Moreover, we give a set of requirements that the middleware should fulfil to learn, detect, and predict patterns in context to optimise the information flow to the decision maker. The approach is made concrete and validated in a case study in the domain of medical health care. Representative location prediction algorithms are evaluated using an existing dataset.
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Acknowledgements
The authors would like to thank their partners in the MUSIC-IST project and acknowledge the partial financial support given to this research by the European Union (6th Framework Programme, contract number 35166).
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Vanrompay, Y., Berbers, Y. (2011). Efficient Context Prediction for Decision Making in Pervasive Health Care Environments: A Case Study. In: Burstein, F., Brézillon, P., Zaslavsky, A. (eds) Supporting Real Time Decision-Making. Annals of Information Systems, vol 13. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-7406-8_15
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DOI: https://doi.org/10.1007/978-1-4419-7406-8_15
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