Analysis and Design of Service-Oriented Framework for Executing Data Mining Services on Grids

Data mining services on grids is the need of today’s era. Workflow environments are widely used in data mining systems to manage data and execution flows associated to complex applications. Weka, one of the most used open-source data mining systems, includes the Knowledge-Flow environment which provides a drag-and-drop inter-face to compose and execute data mining workflows. It allows users to execute a whole workflow only on a single computer on the basis of simplicity. There are several workflows in today’s scene. Most data mining workflows include several independent branches that could be run in parallel on a set of distributed machines to reduce the overall execution time. We analyzes several aspects of distributed workflow execution in Weka4WS, a framework that extends Weka and its Knowledge Flow environment to exploit distributed resources available in a Grid using Web Service technologies and also some other workflows and design which is better in efficiency and work. We also discuss several architectures prospective for betterment.

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