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Journal of Information Science and Engineering, Vol. 30 No. 4, pp. 1601-1618 (September 2014)


Semantic-Based Data Mashups Using Hierarchical Clustering and Pattern Analysis Methods


YONG-JU LEE
School of Computer Information
Kyungpook National University
Sangju, 742-711 Korea

Data mashups enable users to create new applications by combining Web APIs from several data sources. However, the existing data mashup framework requires some programming knowledge, hence it is not suitable for use by non-expert users. In this paper, we present hierarchical clustering and pattern analysis methods that build semantic ontologies automatically, and propose similarity searching algorithms that support the operation semantic matching and composable API discovery. These algorithms allow mashup developers to automate the discovery and composition of Web APIs eliminating the need for programmer involvement. We describe an experimental study on a collection of 168 REST APIs and 50 SOAP APIs. The experimental results show that our approach performs better in terms of both the rate of recall and precision performance compared with existing methods.

Keywords: data mashups, Web API, hierarchical clustering, pattern analysis, similarity searching, semantic techniques

Full Text () Retrieve PDF document (201409_17.pdf)

Received September 22, 2012; revised December 30, 2012 & January 29, 2013; accepted February 24, 2013.
Communicated by Hahn-Ming Lee.
* This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology (No. 2010-0008303).