NDLI: Ontology-Based Workflow Generation for Intelligent Big Data Analytics

Content Provider	IEEE Xplore Digital Library
Author	Kumara, B.T.G.S. Incheon Paik Jia Zhang Siriweera, T.H.A.S. Koswatte, K.R.C.
Copyright Year	2015
Description	Author affiliation: Fac. of Appl. Sci., Sabaragamuwa Univ. of Sri Lanka, Sri Lanka (Kumara, B.T.G.S.) \|\| Carnegie Mellon Univ., Silicon Valley, CA, USA (Jia Zhang) \|\| Sch. of Comput. Sci. & Eng., Univ. of Aizu, Aizu-Wakamatsu, Japan (Incheon Paik; Siriweera, T.H.A.S.; Koswatte, K.R.C.)
Abstract	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.
Starting Page	495
Ending Page	502
File Size	475762
Page Count	8
File Format	PDF
e-ISBN	9781467372725
DOI	10.1109/ICWS.2015.72
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Delays Ontologies Planning Analytical models Data models Data mining Business Ontology Big data analytics Workflow
Content Type	Text
Resource Type	Article

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