1. Dr. Salma Najar Salma.Najar@malix.univ-paris1.fr fr.linkedin.com/in/salmanajar/ Salma Najar Manuele Kirsch-Pinheiro Carine Souveyet Pervasive Information System (PIS) Integration of IS in dynamic and heterogeneous environment Context-awareness and user’s needs satisfaction Predictable and expected behavior Pervasive Environment Integration of new invisible technologies in the daily life Information System User’s needs satisfaction Controllable and predictable Transparency?Proactivity?Context-Awareness? Most appropriate services? User’s intentions satisfaction? Innovative approach : User-centred contextual vision of PIS Intentional approach User’s intention & intention that service can satisfy Contextual approach User’s current context & service required context execution Service Discovery Service Discovery Most appropriate services Exploitation of the dynamic between intention, context and service A CONTEXT-AWARE INTENTIONAL SERVICE PREDICTION MECHANISM IN PIS Transparency Proactivity Reduce user’s effort understanding Hide complexity User centred Vision Intention Prediction Better understanding of user’s future needs and intentions Answer to user’s needs with a non intrusive way Context predictionContext-Aware service Recommendation Approaches- [Sigg et al., 2010] - SCP [Meiners et al., 2010] - [Abbar et al., 2009] - [Xiao et al., 2010] Topics- Anticipate user’s next context - Fulfill missing context information - Proactively propose services to the user (+)- Provide a proactive behavior - Take into account contextual aspects (-)- Ignore user’s intentions that emerge in a given context or that hide behind service request - Propose user a service realization, ignoring why it is necessary - Based only on operational variability: the shift between the operational and the intentional layers is not taken into account Problem: Non exploitation of the close relation between intention and context in existing prediction and recommendation approaches Background Hypothesis: A service prediction mechanism, capable of anticipating user’s intentions in a given context, may improve the overall transparency of PIS. Research Problem Research Problem Key Contribution Key Contribution Results Experimentation Results Experimentation User situations History Time /Dat e IntentionContextServic e T 1 I U1 Cx 1 Sv 1 ….. T i I U i Cx i Sv i T n I U n Cx n Sv n Trace Management Predicted Intention ontologies Prediction Process Learning Process Prediction Context-Aware Intentional Semantic Matching Algorithm Context-Aware Intentional Semantic Matching Algorithm Markov Chain Algorithm Markov Chain Algorithm Context-Aware Intentional Services Prediction Algorithm Context-Aware Intentional Services Prediction Algorithm Context-Aware Intentional Services Prediction Mechanism Prediction Algorithm Quality Results Prediction Algorithm Performance 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 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 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 [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. clusteringclassification Most appropriate service