Résumé: With the advent of the social Web (Web 2.0) and the massive use of online social networks (OSNs) (e.g. Facebook, LinkedIn). OSNs have become new opportunity that provides huge Masses of data about users’, rich in their diversity and important in their quantity. Exploring the profiles data among these OSNs attract a great deal of attention among researchers in several research areas: social information retrieval systems, social recommendation systems. Social Recommender Systems aim to generate meaningful recommendations to a collection of users for items that might be interesting for them. In this paper we propose to investigate social recommender systems for improving Business process (BP) models reuse in process models repositories. The recommender system we propose to integrate we called SBPR recommender. SBPR recommender aims to recommend to the users of such repositories BP models for reuse. LinkedIn User profile is the source of social data for SBPR recommender; BP models are target items to be recommended to user. We propose a framework based on Model Driven Engineering (MDE) approach where techniques of models, metamodels, transformation and weaving are used to implement a generic recommendation process.

Edition: Alger: CERIST
Langue: Anglais
Collation: 17 p. ill. en coul. ;30 cm
Thème Informatique

Mots clés:
BP model reuse
Weaving
MDA
Metamodels
Transformation
LinkedIn
Recommender system
User business profile
Business process modeling
Business process model reuse
Recommendation business process model

Note: Réf. [pp.15-17]