scholarly journals Using Case-Based Reasoning to Support Web Service Composition

Author(s):  
Ruixing Cheng ◽  
Sen Su ◽  
Fangchun Yang ◽  
Yong Li
Author(s):  
Chiung-Hon Leon Lee ◽  
◽  
Alan Liu ◽  
Huan-Hsian Huang ◽  

Planning commonly applied to automating Web Service composition involves two problems - (i) overlooked user needs combined with services provided by the systems themselves and outside services providing a much more flexible service model. (ii) “Speeding up” and “facilitating” services by not recording information about service providers already having served users and about planning already processed. We propose merging internal and external services to meet user needs. Internal services include system functions designed to meet user needs. External services mean Web services provided by outside service providers. We plan to combine both types of services to create planning to meet user needs. We apply case-based reasoning to store planning and related information in a case base to make planning much faster when users have similar needs.


2011 ◽  
pp. 604-622
Author(s):  
Taha Osman ◽  
Dhavalkumar Thakker ◽  
David Al-Dabass

With the rapid proliferation of Web services as the medium of choice to securely publish application services beyond the firewall, the importance of accurate, yet flexible matchmaking of similar services gains importance both for the human user and for dynamic composition engines. In this article, we present a novel approach that utilizes the case based reasoning methodology for modelling dynamic Web service discovery and matchmaking, and investigate the use of case adaptation for service composition. Our framework considers Web services execution experiences in the decision making process and is highly adaptable to the service requester constraints. The framework also utilizes OWL semantic descriptions extensively for implementing both the components of the CBR engine and the matchmaking profile of the Web services.


Author(s):  
Fouad Henni ◽  
Baghdad Atmani

Web services have emerged as a major technology for deploying automated interactions between distributed and heterogeneous applications. The main advantage of Web services composition is the possibility of creating value-added services by combining existing ones to achieve customized tasks. How to combine these services efficiently into an arrangement that is both functionally sound and architecturally realizable is a very challenging topic that has founded a significant research area within computer science. A great deal of recent Web-related research has concentrated on dynamic Web service composition. Most of proposed models for dynamic composition use semantic descriptions of Web services through the construction of domain ontology. In this paper, we present our approach to dynamically produce composite services. It is based on the use of two Artificial Intelligence (AI) techniques: Case-Based Reasoning (CBR) and AI planning. Our motivating scenario concerns a national system for the monitoring of childhood immunization.


2014 ◽  
Vol 6 (4) ◽  
pp. 58-71
Author(s):  
Yamina Hachemi ◽  
Sidi Mohamed Benslimane

Web services composition has emerged as a solution to answer the requester's requirements. However, the selection of an appropriate Web service has become a difficult task due to the number of Web services present on the Web and mostly they offer similar functionalities. User preferences are a key factor that can be used to rank candidate services and retain only the best ones. To improve the process of web service composition the authors propose a Case-Based Planning (CBP) approach based on preferences which uses successful experiences in past to solve similar problems at present or/and in the future. How to make a choice base on non-functional factors becomes a problem that need to be solved. This paper, argues that the selection should be considered in a global manner based on the user's preferences. The authors present a framework that deals with web service composition based on user preferences and CBP method. Results obtained offer more than a solution to the user and taking both functional and non-functional requirements.


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