The frequent pattern mining generates a huge amount of patterns and therefore requires the establishment of an effective post-treatment to target the most useful. This paper proposes an approach to discover the useful frequent patterns that integrates knowledge described by the expert and represented in an ontology associated with the data. The approach uses the ontology for benefit from more structured information to remove some frequent patterns of the analysis. The experiments realized with our approach give satisfactory results.

ABSTRACT. An effective solution to deal with this dynamic nature of distributed systems is to implement a self-adaptive mechanism to sustain the distributed architecture. Self-adaptive systems can autonomously modify their behavior at run-timein response to changes in their environment. Our paper describes the self-adaptive algorithm that we developed for an existing middleware. Once the middleware is deployed, it can detects a set of events which indicate an unstable deployment state. When an event is detected, some instructions are executed to handle the event. We have proposed a sketch proof of the self-stabilizing property of the algorithm. We have designed a simulator to have a deeper insights of our proposed self-adaptive algorithm. Results of our simulated experiments validate the safe convergence of the algorithm.

A property (of an object) is opaque to an observer when he or she cannot deduce the property from its set of observations. If each observer is attached to a given set of properties (the so-called secrets), then the system is said to be opaque if each secret is opaque to the corresponding observer. We study in this paper, the complexity of opacity algorithm in data-centric workflows systems. We show that the complexity of this algorithm is EXPTIME-complete. Using the reduction problem, whe show that we can reduce the complexity of opacity problem to wellknow problem, the intersection of nonemptyness problem of Tree automata in polynomial time.

In this paper, we propose an extension and experimental evaluation of our self-adaptive structuring solution in an large-scale P2P Grid environment. The proposed specification, enables both services deployment, location and invocation of while respecting the P2P networks paradigm. Moreover, the specification is generic i.e. not linked to a particular P2P architecture. The increasing size of resources and users in large-scale distributed systems has lead to a scalability problem. To ensure the scalability, we propose to organize the P2P grid nodes in virtual communities. A particular node called ISP (Information System Proxy) acts as service directory within each cluster. On the other hand, resource discovery is one of the essential challenges in large-scale Grid environment. In this sense, we propose to build a spanning tree which will be constituted by the set of formed ISPs in order to allow an efficient service lookup in the system. An experimental validation, through simulation, shows that our approach ensures a high scalability in terms of clusters distribution and communication cost.

This paper presents a MIMO-OFDM " Beamforming " approach in a IEEE 802.11ac context. This technique of " Beamforming " has the same performance as the conventional technique while allowing to perform the precoding and postcoding at one time and whatever the number of OFDM subcarriers.