A framework for discovering and classifying ubiquitous services in digital health ecosystems
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NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Computer and System Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Computer and System Sciences, Vol. 77, No. 4 (2011). DOI: 10.1016/j.jcss.2010.02.009
A digital ecosystem is a widespread type of ubiquitous computing environment comprised of ubiquitous, geographically dispersed, and heterogeneous species, technologies and services. As a subdomain of the digital ecosystems, digital health ecosystems are crucial for the stability and sustainable development of the digital ecosystems. However, since the service information in the digital health ecosystems exhibits the same features as those in the digital ecosystems, it is difficult for a service consumer to precisely and quickly retrieve a service provider for a given health service request. Consequently, it is a matter of urgency that a technology is developed to discover and classify the health service information obtained from the digital health ecosystems. A survey of state-of-the-art semantic service discovery technologies reveals that no significant research effort has been made in this area. Hence, in this paper, we present a framework for discovering and classifying the vast amount of service information present in the digital health ecosystems. The framework incorporates the technology of semantic focused crawler and social classification. A series of experiments are conducted in order to respectively evaluate the framework and the employed mathematical model.
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