Paper Info
Title
Onotology-based service discovery for intelligent Big Data analytics
Abstract
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. Thus, in our previous work, we presented an approach to automate the CRISP-DM process using Automatic Service Composition (ASC). Further, we proposed an ontology-based workflow generation method to automate by considering the planning stage. In this paper, we focus on discovery stage of the ASC. Here, we apply ontology based discovery method to identify candidate services for abstract tasks in workflow. We compute the degree of semantic matching for a given pair of abstract task and web service instance by applying different filters. Further, we consider clustering based discovery based method to increase the efficiency of the discovery. Experimental results show that our discovery approach works efficiently.
Year
DOI
Venue
2015
10.1109/ICAwST.2015.7314022
2015 IEEE 7th International Conference on Awareness Science and Technology (iCAST)
Keywords
Field
DocType
Service Composition,Web Service Discovery,Big data analytics,Ontology,Data mining
Data science,Ontology-based data integration,Web analytics,Predictive analytics,Computer science,Semantic analytics,Service discovery,Analytics,Workflow,Big data
Conference
ISSN
Citations 
PageRank 
2325-5986
1
0.37
References 
Authors
7
4
Name
Order
Citations
PageRank
t h akila110.37
s siriweera210.37
Incheon Paik324138.80
Banage T. G. S. Kumara4629.65