SAMOVAR UMR 5157

  • Accueil
  • Accueil
  • Accueil
  • Accueil

CNRS

Rechercher




Accueil > Productions scientifiques > Thèses SAMOVAR > Thèses 2012

Soutenance de thèse de Ngoc Chan NGUYEN "Service Recommendation for Individual and Process Use".

ANNONCE DE SOUTENANCE DE THESE DE DOCTORAT
Monsieur Ngoc Chan NGUYEN
Département Informatique - Télécom SudParis - Université d’Evry Val
d’Essonne - Ecole doctorale Sciences & Ingénierie

"Service Recommendation for Individual and Process Use".

Cette thèse a été réalisée sous la direction de Samir Tata et Walid
Gaaloul. *_Elle aura lieu le 13 décembre 2012 à 10h00 - Salle C06 à TELECOM SudParis, 9 rue Charles Fourier, 91000 Evry.

Le jury sera composé de :

- Marlon Dumas, Rapporteur - University of Tartu, Estonia

- Schahram Dustdar, Rapporteur - Vienna University of Technology, Austria

- Bruno Defude, Examinateur - TELECOM SudParis, France

- François Charoy, Examinateur - Université de Lorraine, France

- Sami Bhiri, Examinateur - National University of Ireland, Ireland

- Walid Gaaloul, Encadrant - TELECOM SudParis, France

- Samir Tata, Directeur de thèse - TELECOM SudParis, France

Abstract :

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.

*Keywords : *Service recommendation - business process design -
neighborhood context - query language - process mining