Résumé: In this paper we present preliminary work studying the interactions of a community of focussed forum users and their discussions around several television series. We use k-means clustering and a number of novel citation-analysis inspired measures to perform bottom-up role detection on this community of TV fans, and show that these emergent roles correspond well with the positions assigned to users using traditional graph-based measures.

Ref HAL: hal-00718865_v1
Résumé: Self-service business intelligence is about enabling non-expert users to make well-informed decisions by enriching the decision process with situational data, i.e., data that have a narrow focus on a specific business problem and, typically, a short lifespan for a small group of users. Often, these data are not owned and controlled by the decision maker; their search, extraction, integration, and storage for reuse or sharing should be accomplished by decision makers without any intervention by designers or programmers. The goal of this paper is to present the framework we envision to support self-service business intelligence and the related research challenges; the underlying core idea is the notion of fusion cubes, i.e., multidimensional cubes that can be dynamically extended both in their schema and their instances, and in which situational data and metadata are associated with quality and provenance annotations.

Résumé: Hybrid evolutionary metaheuristics tend to enhance search capabilities, by improving intensication and diversication, through incorporating dierent cooperative metaheuristics. In this paper, an improved version of the Hybrid Evolutionary Metaheuristics (HEMH) [7] is presented. Unlike HEMH, HEMH2 uses simple inverse greedy algorithm to construct its initial population. Then, the search eorts is directed to improve these solutions by exploring the search space using binary differential evolution. After a certain number of evaluations, path-relinking is applied on high quality solutions to investigate the non-visited regions in the search space. During evaluations, the dynamic-sized neighborhood structure is adopted to shrink/extend the mating/updating range. Furthermore, the Pareto adaptive epsilon concept is used to control the archiving process with preserving the extreme solutions. HEMH2 is verified against its predecessor HEMH and the MOEA/D [13], using a set of MOKSP instances from the literature. The experimental results indicate that the HEMH2 is highly competitive and can achieve better results.

Résumé: Data warehouses are nowadays extensively used to perform analyses on huge volume of data. This success is partly due to the capacity of considering data at several granularity levels thanks to the use of hierarchies. However, in previous work, we showed that the experts knowledge was not much considered in the generalization process. To overcome this drawback, we introduced a new category of hierarchies, namely the contextual hierarchies. Unfortunately, in contrast to the complexity of expert knowledge that should be considered, the knowledge definition process was too rigid. In this paper, we extend these hierarchies and their related techniques to drastically increase their flexibility and expressivity. To this purpose, we adopt a fuzzy-based methodology which allows to express expert knowledge in a very convenient way. Experiment results obtained on synthetic datasets show that the contextual generalization process is very fast and can thus be used in practice.

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Résumé: Recently, information security has become a key issue in information technology as the number of computer security breaches are exposed to an increasing number of security threats. A variety of intrusion detection systems (IDS) have been employed for protecting computers and networks from malicious network-based or host-based attacks by using traditional statistical methods to new data mining approaches in last decades. In effect, the detection of the anomalies in the data-processing networks is regarded as one problem of data classification where the use of the data mining techniques and machine learning. In this paper, we present a new method performing the intrusion detection system. This approach, called MCSAS, is based on a multiple classifier System that uses an adaptive strategy for intrusion detection. The adaptive strategy is inspired from Boosting, an ensemble method that distinguishes attacks from normal behaviors and identifies different types of intrusions. The experimental results, conducted on the KDD99 dataset, prove that our proposed solution approach outperforms several state-of-the-art methods, particularly indetecting rare attack types.

Ref HAL: hal-00712141_v1
Résumé: Particle swarm optimisation for data warehouse logical design