WorldCat Identities

Poncelet, Pascal

Works: 31 works in 71 publications in 2 languages and 899 library holdings
Roles: Author, Other, Editor, Thesis advisor, Opponent, Contributor
Publication Timeline
Most widely held works by Pascal Poncelet
Successes and new directions in data mining by Florent Masseglia( )

23 editions published between 2007 and 2008 in English and held by 543 WorldCat member libraries worldwide

"This book addresses existing solutions for data mining, with particular emphasis on potential real-world applications. It captures defining research on topics such as fuzzy set theory, clustering algorithms, semi-supervised clustering, modeling and managing data mining patterns, and sequence motif mining"--Provided by publisher
Data mining patterns : new methods and applications by Pascal Poncelet( )

18 editions published between 2007 and 2008 in English and held by 295 WorldCat member libraries worldwide

"This book provides an overall view of recent solutions for mining, and explores new patterns, offering theoretical frameworks and presenting challenges and possible solutions concerning pattern extractions, emphasizing research techniques and real-world applications. It portrays research applications in data models, methodologies for mining patterns, multi-relational and multidimensional pattern mining, fuzzy data mining, data streaming and incremental mining"--Provided by publisher
Fouille de données d'opinions( Book )

1 edition published in 2009 in French and held by 16 WorldCat member libraries worldwide

Extraction et gestion des connaissances EGC'2011( Book )

1 edition published in 2011 in French and held by 8 WorldCat member libraries worldwide

Contribution à la conception de bases de données avancées : modélisation, évolution et dérivation by Pascal Poncelet( Book )

2 editions published in 1993 in French and held by 6 WorldCat member libraries worldwide

Les modèles traditionnels répondent mal aux besoins des nouvelles applications qui nécessitent des constructeurs plus puissants ainsi qu'un important degré de flexibilité. C'est pourquoi de nombreux travaux de recherche s'intéressent à la définition de nouvelles approches de modélisation capables de répondre aussi bien aux besoins des applications traditionnelles qu'à ceux des applications variées. Le travail présenté, dans cette thèse, s'inscrit dans ce contexte puisqu'il propose une nouvelle approche pour la conception de bases de données avancées. Elle se base sur le modèle ifo2, extension du modèle sémantique ifo défini par S Aiteboul et R. Hull. Son objectif est de concilier deux idées apparemment opposées: d'une part une représentation optimisée des données et d'autre part une modélisation la plus complète possible de l'univers réel. Il s'agit ainsi de conserver les atouts des modèles sémantiques tout en intégrant les concepts du paradigme objet. Le modèle ifo2 est un modèle formel qui permet, outre une spécification non ambiguë du monde réel, une approche incrémentale pour la conception de schéma grâce à la définition de règles rigoureuses de mise à jour. Ces dernières sont cruciales car elles assistent le concepteur lors des modifications du schéma rendant compte des évolutions de l'univers réel ou rectifiant les spécifications. Enfin, pour concevoir une base de données, le schéma ifo2 est traduit, de manière automatique, dans un modèle cible (o2 et relationnel). Les trois éléments: modélisation, évolution et dérivation sont intégrés dans un système ifo2
New MP-SoC profiling tools based on data mining techniques by Sofiane Lagraa( )

1 edition published in 2014 in English and held by 2 WorldCat member libraries worldwide

Miniaturization of electronic components has led to the introduction of complex electronic systems which are integrated onto a single chip with multiprocessors, so-called Multi-Processor System-on-Chip (MPSoC). The majority of recent embedded systems are based on massively parallel MPSoC architectures, hence the necessity of developing embedded parallel applications. Embedded parallel application design becomes more challenging: It becomes a parallel programming for non-trivial heterogeneous multiprocessors with diverse communication architectures and design constraints such as hardware cost, power, and timeliness. A challenge faced by many developers is the profiling of embedded parallel applications so that they can scale over more and more cores. This is especially critical for embedded systems powered by MPSoC, where ever demanding applications have to run smoothly on numerous cores, each with modest power budget. Moreover, application performance does not necessarily improve as more cores are added. Application performance can be limited due to multiple bottlenecks including contention for shared resources such as caches and memory. It becomes time consuming for a developer to pinpoint in the source code the bottlenecks decreasing the performance. To overcome these issues, in this thesis, we propose a fully three automatic methods which detect the instructions of the code which lead to a lack of performance due to contention and scalability of processors on a chip. The methods are based on data mining techniques exploiting gigabytes of low level execution traces produced by MPSoC platforms. Our profiling approaches allow to quantify and pinpoint, automatically the bottlenecks in source code in order to aid the developers to optimize its embedded parallel application. We performed several experiments on several parallel application benchmarks. Our experiments show the accuracy of the proposed techniques, by quantifying and pinpointing the hotspot in the source code
FEEL: a French Expanded Emotion Lexicon by Amine Abdaoui( )

1 edition published in 2016 in English and held by 2 WorldCat member libraries worldwide


1 edition published in 1984 in French and held by 2 WorldCat member libraries worldwide

Abstraction et comparaison de traces d'exécution pour l'analyse d'applications multimédias embarquées by Christiane Kamdem Kengne( )

1 edition published in 2014 in English and held by 2 WorldCat member libraries worldwide

The SoC-Trace project aims to develop a set of methods and tools based on execution traces of multicore embedded applications to meet the growing needs of observability and 'débogability' required by the industry. The project aims in particular the development of new analytical methods, based on different data analysis techniques such as probabilistic analysis, data mining, and data aggregation. They should allow the automatic identification of anomalies, the analysis of complex correlations and dependencies between different components of an embedded application and control of the volume traces that can now exceed the gigabyte. The aim of the thesis is to provide a high-level representation of information in the trace based semantics. It will initially develop an effective tool for comparing traces, to define a semantic distance for execution traces, then a second time to analyze and interpret the results of comparisons of traces based on the defined distance
Local community detection in multilayer networks by Roberto Interdonato( )

1 edition published in 2017 in English and held by 2 WorldCat member libraries worldwide

Données semi structurées : Découverte, maintenance et analyse de tendances by Pierre Alain Laur( Book )

in French and held by 1 WorldCat member library worldwide

Preserving Behaviour: Why and How( )

1 edition published in 1997 in Undetermined and held by 1 WorldCat member library worldwide

Extraction de séquences inattendues : des motifs séquentiels aux règles d'implication by Dong Haoyuan Li( Book )

in French and held by 1 WorldCat member library worldwide

The sequential patterns can be viewed as an extension of the notion of association rules with integrating temporal constraints, which are effective for representing statistical frequency based behaviors between the elements contained in sequence data, that is, the discovered patterns are interesting because they are frequent. However, with considering prior domain knowledge of the data, another reason why the discovered patterns are interesting is because they are unexpected. In this thesis, we investigate the problems in the discovery of unexpected sequences in large databases with respect to prior domain expertise knowledge. We first methodically develop the framework Muse with integrating the approaches to discover the three forms of unexpected sequences. We then extend the framework Muse by adopting fuzzy set theory for describing sequence occurrence. We also propose a generalized framework SoftMuse with respect to the concept hierarchies on the taxonomy of data. We further propose the notions of unexpected sequential patterns and unexpected implication rules, in order to evaluate the discovered unexpected sequences by using a self-validation process. We finally propose the discovery and validation of unexpected sentences in free format text documents. The usefulness and effectiveness of our proposed approaches are shown with the experiments on synthetic data, real Web server access log data, and text document classification
La visualisation d'information pour les données massives : une approche par l'abstraction de données by Joris Sansen( )

1 edition published in 2017 in French and held by 1 WorldCat member library worldwide

The evolution and spread of technologies have led to a real explosion of information and our capacity to generate data and our need to analyze them have never been this strong. Still, the problems raised by such accumulation (storage, computation delays, diversity, speed of gathering/generation, etc. ) is as strong as the data are big, complex and varied. Information visualization,by its ability to summarize and abridge data was naturally established as appropriate approach. However, it does not solve the problem raised by Big Data. Actually, classical visualization techniques are rarely designed to handle such mass of information. Moreover, the problems raised by data storage and computation time have repercussions on the analysis system. For example,the increasing distance between the data and the analyst : the place where the data is stored and the place where the user will perform the analyses arerarely close. In this thesis, we focused on these issues and more particularly on adapting the information visualization techniques for Big Data. First of all focus on relational data : how does the existence of a relation between entity istransmitted and how to improve this transmission for hierarchical data. Then,we focus on multi-variate data and how to handle their complexity for the required computations. Finally, we present the methods we designed to make our techniques compatible with Big Data
Extraction de séquences fréquentes : des bases de données statiques aux flots de données by Chedy Raissi( Book )

in French and held by 1 WorldCat member library worldwide

Modeling and mining of web discussions by Anna Stavrianou( )

1 edition published in 2010 in English and held by 1 WorldCat member library worldwide

Le développement du Web 2.0 a donné lieu à la production d'une grande quantité de discussions en ligne. La fouille et l'extraction de données de qualité de ces discussions en ligne sont importantes dans de nombreux domaines (industrie, marketing) et particulièrement pour toutes les applications de commerce électronique. Les discussions de ce type contiennent des opinions et des croyances de personnes et cela explique l'intérêt de développer des outils d'analyse efficaces pour ces discussions.L'objectif de cette thèse est de définir un modèle qui représente les discussions en ligne et facilite leur analyse. Nous proposons un modèle basé sur des graphes. Les sommets du graphe représentent les objets de type message. Chaque objet de type message contient des informations comme son contenu, son auteur, l'orientation de l'opinion qui y été exprimée et la date où il a été posté. Les liens parmi les objets message montrent une relation de type "répondre à". En d'autres termes, ils montrent quels objets répondent à quoi, conséquence directe de la structure de la discussion en ligne.Avec ce nouveau modèle, nous proposons un certain nombre de mesures qui guident la fouille au sein de la discussion et permettent d'extraire des informations pertinentes. Les mesures sont définies par la structure de la discussion et la façon dont les objets messages sont liés entre eux. Il existe des mesures centrées sur l'analyse de l'opinion qui traitent de l'évolution de l'opinion au sein de la discussion. Nous définissons également des mesures centrées sur le temps, qui exploitent la dimension temporelle du modèle, alors que les mesures centrées sur le sujet peuvent être utilisées pour mesurer la présence de sujets dans une discussion. La représentation d'une discussion en ligne de la manière proposée permet à un utilisateur de "zoomer" dans une discussion. Une liste de messages clés est recommandée à l'utilisateur pour permettre une participation plus efficace au sein de la discussion. De plus, un système prototype a été implémenté pour permettre à l'utilisateur de fouiller les discussions en ligne en sélectionnant un sous ensemble d'objets de type message et naviguer à travers ceux-ci de manière efficace
The role of location and social strength for friendship prediction in location-based social networks( )

1 edition published in 2018 in English and held by 1 WorldCat member library worldwide

Highlights: A taxonomy for friendship prediction methods for location-based social networks is proposed. Five new methods for friendship prediction in location-based social networks are proposed. A comprehensive analysis of selected current methods on link prediction for LBSNs is carried out. From a set of fifteen friendship prediction methods, we identified the top-5 methods with feasibility for real-world applications. Two of our proposals are in this top-5. Abstract: Recent advances in data mining and machine learning techniques are focused on exploiting location data. These advances, combined with the increased availability of location-acquisition technology, have encouraged social networking services to offer to their users different ways to share their location information. These social networks, called location-based social networks (LBSNs), have attracted millions of users and the attention of the research community. One fundamental task in the LBSN context is the friendship prediction due to its role in different applications such as recommendation systems. In the literature exists a variety of friendship prediction methods for LBSNs, but most of them give more importance to the location information of users and disregard the strength of relationships existing between these users. The contributions of this article are threefold, we: 1) carried out a comprehensive survey of methods for friendship prediction in LBSNs and proposed a taxonomy to organize the existing methods; 2) put forward a proposal of five new methods addressing gaps identified in our survey while striving to find a balance between optimizing computational resources and improving the predictive power; and 3) used a comprehensive evaluation to quantify the prediction abilities of ten current methods and our five proposals and selected the top-5 friendship prediction methods for LBSNs. We thus present a general panorama of friendship prediction task in the LBSN domain with balanced depth so as to facilitate research and real-world application design regarding this important issue
Fouille de documents et d'opinions multilingue by Motaz Saad( )

1 edition published in 2015 in English and held by 1 WorldCat member library worldwide

The aim of this thesis is to study sentiments in comparable documents. First, we collect English, French and Arabic comparable corpora from Wikipedia and Euronews, and we align each corpus at the document level. We further gather English-Arabic news documents from local and foreign news agencies. The English documents are collected from BBC website and the Arabic documents are collected from Al-jazeera website. Second, we present a cross-lingual document similarity measure to automatically retrieve and align comparable documents. Then, we propose a cross-lingual sentiment annotation method to label source and target documents with sentiments. Finally, we use statistical measures to compare the agreement of sentiments in the source and the target pair of the comparable documents. The methods presented in this thesis are language independent and they can be applied on any language pair
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Successes and new directions in data mining
Data mining patterns : new methods and applicationsExtraction et gestion des connaissances EGC'2011
English (48)

French (11)