Soutenance Thèse Ngoc Chan NGUYEN

Ngoc Chan NGUYEN soutiendra sa thèse de doctorat jeudi 13 décembre 2012

Sujet : Service Recommendation for Individual and Process Use

La soutenance aura lieu jeudi 13 décembre 2012 à 10h00 dans la salle C06, à Télécom SudParis, 9 rue Charles Fourier, 91000 Evry devant un jury composé de :

Marlon Dumas, University of Tartu, Estonia, Rapporteur
Schahram Dustdar, Vienna University of Technology, Austria, Rapporteur
Bruno Defude, TELECOM SudParis, France, Examinateur
François Charoy, Université de Lorraine, France, Examinateur
Sami Bhiri, National University of Ireland, Ireland, Examinateur
Walid Gaaloul, TELECOM SudParis, France, Encadrant
Samir Tata, TELECOM SudParis, France, Directeur de thèse

Résumé :

Web services have been developed as an attractive paradigm for publishing, discovering and consuming services. They are loosely-coupled applications that can be run alone or be composed to create new value-added services. They can be consumed as individual services which provide a unique interface to receive inputs and return outputs; or they can be consumed as components to be integrated into business processes. We call the first consumption case individual use and the second case business process use.

The requirement of specific tools to assist consumers in the two service consumption cases involves many researches in both academics and industry. On the one hand, many service portals and service crawlers have been developed as specific tools to assist users to search and invoke Web services for individual use. However, current approaches take mainly into account explicit knowledge presented by service descriptions. They make recommendations without considering data that reflect user interest and may require additional information from users. On the other hand, some business process mechanisms to search for similar business process models or to use reference models have been developed. These mechanisms are used to assist process analysts to facilitate business process design. However, they are still labor-intense, error-prone, time-consuming, and may make business analyst confused.

In our work, we aim at facilitating the service consumption for individual use and business process use using recommendation techniques. We target to recommend users services that are close to their interest and to recommend business analysts services that are relevant to an ongoing designed business process. To recommend services for individual use, we take into account the user's usage data which reflect the user's interest. We apply well-known collaborative filtering techniques which are developed for making recommendations. We propose five algorithms and develop a web-based application that allows users to use services. To recommend services for business process use, we take into account the relations between services in business processes. We target to recommend relevant services to selected positions in a business process. We define the neighborhood context of a service. We make recommendations based on the neighborhood context matching. Besides, we develop a query language to allow business analysts to formally express constraints to filter services. We also propose an approach to extract the service's neighborhood context from business process logs. Finally, we develop three applications to validate our approach. We perform experiments on the data collected by our applications and on two large public datasets. Experimental results show that our approach is feasible, accurate and has good performance in real use-cases.