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2015 IEEE International Conference on Web Services (ICWS) (2015)
New York, NY, USA
June 27, 2015 to July 2, 2015
ISBN: 978-1-4673-7271-8
pp: 495-502
Big Data analytics provide support for decision making by discovering patterns and other useful information from large set of data. Organizations utilizing advanced analytics techniques to gain real value from Big Data will grow faster than their competitors and seize new opportunities. Cross-Industry Standard Process for Data Mining (CRISP-DM) is an industry-proven way to build predictive analytics models across the enterprise. However, the manual process in CRISP-DM hinders faster decision making on real-time application for efficient data analysis. In this paper, we present an approach to automate the process using Automatic Service Composition (ASC). Focusing on the planning stage of ASC, we propose an ontology-based workflow generation method to automate the CRISP-DM process. Ontology and rules are designed to infer workflow for data analytics process according to the properties of the datasets as well as user needs. Empirical study of our prototyping system has proved the efficiency of our workflow generation method.
Delays, Ontologies, Planning, Analytical models, Data models, Data mining, Business

B. T. Kumara, I. Paik, J. Zhang, T. Siriweera and K. R. Koswatte, "Ontology-Based Workflow Generation for Intelligent Big Data Analytics," 2015 IEEE International Conference on Web Services (ICWS), New York, NY, USA, 2015, pp. 495-502.
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