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Strategy for Data Stream Processing Based on Measurement Metadata: An Outpatient Monitoring Scenario

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DOI: 10.4236/jsea.2011.412077    4,399 Downloads   7,234 Views   Citations

ABSTRACT

In this work we discuss SDSPbMM, an integrated Strategy for Data Stream Processing based on Measurement Metadata, applied to an outpatient monitoring scenario. The measures associated to the attributes of the patient (entity) under monitoring, come from heterogeneous data sources as data streams, together with metadata associated with the formal definition of a measurement and evaluation project. Such metadata supports the patient analysis and monitoring in a more consistent way, facilitating for instance: i) The early detection of problems typical of data such as missing values, outliers, among others; and ii) The risk anticipation by means of on-line classification models adapted to the patient. We also performed a simulation using a prototype developed for outpatient monitoring, in order to analyze empirically processing times and variable scalability, which shed light on the feasibility of applying the prototype to real situations. In addition, we analyze statistically the results of the simulation, in order to detect the components which incorporate more variability to the system.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

M. Diván, L. Olsina and S. Gordillo, "Strategy for Data Stream Processing Based on Measurement Metadata: An Outpatient Monitoring Scenario," Journal of Software Engineering and Applications, Vol. 4 No. 12, 2011, pp. 653-665. doi: 10.4236/jsea.2011.412077.

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