Dr. Salma Najar Salma.Najar@malix.univ-paris1.fr fr.linkedin/in/salmanajar/

1. Salma NajarManuele Kirsch-PinheiroCarine Souveyet A CONTEXT-AWARE INTENTIONAL SERVICE PREDICTION MECHANISM IN PIS • Pervasive Information System (PIS) • Integration of IS in dynamic and heterogeneous environment • Context-awareness anduser’s needs satisfaction • Predictable and expected behavior Reduce user’s effort understanding Transparency User centred Vision Hide complexity Better understanding of user’s future needs and intentions • Pervasive Environment • Integration of new invisible technologies in the daily life • Information System • User’s needs satisfaction • Controllable and predictable Proactivity Intention Prediction Answer to user’s needs with a non intrusive way Transparency? Context-Awareness? Proactivity? • Problem: Non exploitation of the close relation between intention and context in existing • prediction and recommendation approaches Most appropriate services? User’s intentions satisfaction? • Innovative approach : User-centred contextual vision of PIS • Exploitation of the dynamic between intention, context and service Hypothesis: A service prediction mechanism, capable of anticipating user’s intentions in a given context, may improve the overall transparency of PIS. Contextual approach User’s current context & service required context execution Intentional approach User’s intention & intention that service can satisfy Service Discovery [Abbar et al., 2009] Abbar, S., Bouzeghoub, M., and Lopez, S. (2009). Context-Aware Recommender Systems: A Service-Oriented Approach. In 3rd Int Workshop on Personalized Access, Profile Management, and Context Awareness in Databases (PersDB), Lyon, France. [Meiners et al., 2010] Meiners, M., Zaplata, S., and Lamersdorf, W. (2010). Structured Context Prediction: A Generic Approach. In Distributed Applications and Interoperable Systems, F. Eliassen, and R. Kapitza, eds. (Springer Berlin Heidelberg), pp. 84–97. [Sigg et al., 2010] Sigg, S., Haseloff, S., and David, K. (2010). An Alignment Approach for Context Prediction Tasks in UbiComp Environments. IEEE Pervasive Computing, 9(4), pp. 90–97. [Xiao et al., 2010] Xiao, H., Zou, Y., Ng, J., and Nigul, L. (2010). An Approach for Context-Aware Service Discovery and Recommendation. In 2010 IEEE International Conference on Web Services (ICWS), pp. 163–170. Research Problem Background Most appropriate services • Context-Aware Intentional Services Prediction Mechanism Key Contribution Results Experimentation • Evaluation of the Prediction Algorithm • Desktop profile: Machine Intel Core i5 1.3 GHz with 4 GB memory • Dataset • Extended OWLS-TC2 with intentional and contextual information • Traces database • Observations • Scalability: Average execution time (performance) • Result Quality: precision and recall History ontologies User situations <Intention, Contexte, Service> Trace Management Most appropriate service Predicted Intention Prediction clustering classification Prediction Process Learning Process Context-Aware Intentional Semantic Matching Algorithm Context-Aware Intentional Services Prediction Algorithm Prediction Algorithm Quality Results Prediction Algorithm Performance • Polynomial trend of degree three • The number of states increased about 25x, while the execution time has only increased about 2.5x • More interesting results with a higher quality • Good results depends on: • Completeness of the ontologies • Setting of the matching threshold Markov Chain Algorithm The prediction mechanism allows selecting the most appropriate future service according to the predicted intention in a given context Intentional approach: more transparent to user Contextual approach: limits states to those that are valid & executable Dr. Salma Najar Salma.Najar@malix.univ-paris1.fr fr.linkedin.com/in/salmanajar/