Utilizing Linked Open Data for Web Service Selection and Composition to Support e-Commerce Transactions

Vesyropoulos, Nikolaos; Georgiadis, Christos K.; Pimenidis, Elias

doi:10.1007/978-3-319-45243-2_49

Cited by 1 publication

(1 citation statement)

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“…We focus on WS that can be utilized in order to support e-commerce transactions, as in this domain any differentiation in the Quality of Service (QoS) characteristics of involved services in a WS composition can play a pivotal role for the completion or abnormal termination of a business transaction, thus highlighting the importance of selecting the optimal services according to predefined business needs. A preliminary version of this work has been reported in [30].…”

Section: Introductionmentioning

confidence: 99%

An Approach for Web Service Selection and Dynamic Composition Based on Linked Open Data

Vesyropoulos

Georgiadis

Pimenidis

2018

Transactions on Computational Collective Intelligence XXX

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The wide adoption of the Service Oriented Architecture (SOA) paradigm has provided a means for heterogeneous systems to seamlessly interact and exchange data. Thus, enterprises and end-users have widely utilized Web Services (WS), either as stand-alone applications or as part of more complex service compositions, in order to fulfill their business needs. But, while WS offer a plethora of benefits, a significant challenge rises due to the abundance of available services that can be retrieved online. In this work, we propose a framework for the selection and dynamic composition of WS, by utilizing Linked open Data (LoD). In addition, we propose a hybrid algorithm that uses as input the user's personalized weights for non-functional characteristics and the results produced by appropriate SPARQL queries that are filtered results using a top-k approach. It then handles the ranking of alternatives based on their population. Finally, using two case studies and a dataset that describes realworld WS, we argue on the feasibility and performance of the proposed method.

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Section: Introductionmentioning

confidence: 99%