dr. salma najar salma.najar@malix.univ-paris1.fr fr.linkedin.com/in/salmanajar/ salma najar manuele...

Dr. Salma NajarSalma.Najar@malix.univ-paris1.frfr.linkedin.com/in/salmanajar/

Salma NajarManuele Kirsch-PinheiroCarine 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 approachUser’s intention & intention that

service can satisfy

Contextual approachUser’s current context & service

required context execution ServiceDiscovery

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 prediction Context-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

Backg

round

Backg

round

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

Contributio

n

Key

Contributio

nResults

Experimentation

Results

Experimentation

User situations<Intention, Contexte, Service>

History

Time/

Date

Intention Context Service

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 nTrace ManagementTrace Management

Predicted Intention

ontologies

Prediction ProcessPrediction Process Learning ProcessLearning 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 ResultsPrediction 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.

clustering classification

Most appropriate service