Ontology-Based Construction of Grid Data Mining Workflows

Ontology-Based Construction of Grid Data Mining Workflows

Peter Brezany (University of Vienna, Austria), Ivan Janciak (University of Vienna, Austria) and A Min Tjoa (Vienna University of Technology, Austria)
DOI: 10.4018/978-1-60566-370-8.ch021


This chapter introduces an ontology-based framework for automated construction of complex interactive data mining workflows as a means of improving productivity of Grid-enabled data exploration systems. The authors first characterize existing manual and automated workflow composition approaches and then present their solution called GridMiner Assistant (GMA), which addresses the whole life cycle of the knowledge discovery process. GMA is specified in the OWL language and is being developed around a novel data mining ontology, which is based on concepts of industry standards like the predictive model markup language, cross industry standard process for data mining, and Java data mining API. The ontology introduces basic data mining concepts like data mining elements, tasks, services, and so forth. In addition, conceptual and implementation architectures of the framework are presented and its application to an example taken from the medical domain is illustrated. The authors hope that the further research and development of this framework can lead to productivity improvements, which can have significant impact on many real-life spheres. For example, it can be a crucial factor in achievement of scientific discoveries, optimal treatment of patients, productive decision making, cutting costs, and so forth.
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In the context of modern service-oriented Grid architectures, the data mining workflow can be seen as a collection of Grid services that are processed on distributed resources in a well-defined order to accomplish a larger and sophisticated data exploration goal. At the highest level, functions of Grid workflow management systems could be characterized into build-time functions and run-time functions. The build-time functions are concerned with defining and modeling workflow tasks and their dependencies while the run-time functions are concerned with managing the workflow execution and interactions with Grid resources for processing workflow applications. Users interact with workflow modeling tools to generate a workflow specification, which is submitted for execution to a run-time service called workflow enactment service, or workflow engine. Many languages, mostly based on XML, were defined for workflow description, like XLANG (Thatte, 2001), WSFL (Leymann, 2001), DSCL (Kickinger et al., 2003) and BPML (Arkin, 2002). Eventually the WSBPEL (Arkin et al., 2005) and BPEL4WS (BEA et al., 2003) specifications emerged as the de facto standard.

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