pr\u00e9sentent<\/p>\n\n\n\n
Autoris\u00e9e \u00e0 pr\u00e9senter ses travaux en vue de l\u2019obtention du Doctorat de l’Institut Polytechnique de Paris, pr\u00e9par\u00e9 \u00e0 T\u00e9l\u00e9com SudParis en :<\/p>\n\n\n\n
le JEUDI 28 NOVEMBRE 2024 \u00e0 14h00<\/p>\n\n\n\n
\u00e0<\/p>\n\n\n\n
Amphith\u00e9\u00e2tre 6
19 Pl. Marguerite Perey, 91120 Palaiseau, France<\/p>\n\n\n\n
Membres du jury :<\/strong><\/p>\n\n\n\n M. Olivier CUR\u00e9<\/strong>, Ma\u00eetre de conf\u00e9rences, Universit\u00e9 Paris-Est Marne-la-Vall\u00e9e, FRANCE – Rapporteur R\u00e9sum\u00e9 :<\/strong><\/p>\n\n\n\n Aujourd’hui, la prise de d\u00e9cision en temps r\u00e9el bas\u00e9e sur des flux de donn\u00e9es provenant de sources h\u00e9t\u00e9rog\u00e8nes est de plus en plus r\u00e9pandue dans de nombreux sc\u00e9narios industriels. En cons\u00e9quence, de nouveaux m\u00e9canismes sont n\u00e9cessaires pour g\u00e9rer l’int\u00e9gration des donn\u00e9es. Pour relever ce d\u00e9fi, la communaut\u00e9 du Traitement de Flux RDF (RDF Stream Processing – RSP) a activement promu le mod\u00e8le de donn\u00e9es RDF et propos\u00e9 plusieurs mod\u00e8les et langages pour interroger en continu et raisonner sur les flux RDF. Ainsi, le traitement de flux RDF et le raisonnement sont devenus un domaine de recherche important. Le raisonnement sur les flux donne un sens aux flux de donn\u00e9es h\u00e9t\u00e9rog\u00e8nes, en constante \u00e9volution et souvent bruyants. Atteindre un tel objectif n\u00e9cessite une r\u00e9\u00e9valuation des paradigmes de r\u00e9solution de probl\u00e8mes, car les conclusions bas\u00e9es sur des donn\u00e9es obsol\u00e8tes doivent \u00eatre r\u00e9vis\u00e9es, de nouvelles informations arrivent constamment, et les d\u00e9rivations pr\u00e9c\u00e9dentes peuvent \u00eatre reconsid\u00e9r\u00e9es. Malgr\u00e9 les progr\u00e8s r\u00e9alis\u00e9s au cours de la derni\u00e8re d\u00e9cennie, de nombreux d\u00e9fis restent \u00e0 relever. Tout d’abord, aucun moteur RSP ne peut g\u00e9rer des requ\u00eates complexes, prendre en charge le raisonnement temporel ou int\u00e9grer des sources de connaissances de fond. De plus, les probl\u00e8mes li\u00e9s \u00e0 la gestion de r\u00e8gles conflictuelles, au raisonnement \u00e0 la vol\u00e9e et de mani\u00e8re incremental, ainsi qu’au soutien du raisonnement multi-contexte dans des applications du monde r\u00e9el soulignent la n\u00e9cessit\u00e9 de progresser dans ce domaine. Cette th\u00e8se explore le domaine du raisonnement sur les flux et aborde le compromis entre le d\u00e9bit de donn\u00e9es et l’expressivit\u00e9, et la contribution est double : (i) Tout d’abord, nous avons introduit un cadre bas\u00e9 sur des agents multiples pour le traitement continu distribu\u00e9. Ce cadre permet l’utilisation simultan\u00e9e de plusieurs moteurs RSP, en exploitant leurs forces respectives et compl\u00e9mentaires. Il permet l’utilisation simultan\u00e9e ou s\u00e9quentielle de ces moteurs pour g\u00e9rer des requ\u00eates complexes, offrant ainsi une solution plus polyvalente et compl\u00e8te. (ii) Deuxi\u00e8mement, nous avons propos\u00e9 une nouvelle technique de raisonnement incr\u00e9mentiel sp\u00e9cifiquement adapt\u00e9e au fragment pratique de la logique temporelle lin\u00e9aire (Linear Temporal Logic – LARS). Cette technique vise \u00e0 optimiser les programmes LARS, am\u00e9liorant ainsi leur efficacit\u00e9. De plus, les capacit\u00e9s du Syst\u00e8me de Maintenance de la V\u00e9rit\u00e9 des Assomptions (Assumption Truth Maintenance System – ATMS) sont \u00e9tendues pour la mise \u00e0 jour des mod\u00e8les. Cette extension facilite une gestion plus compl\u00e8te des informations conflictuelles, contribuant \u00e0 la robustesse du raisonnement bas\u00e9 sur des r\u00e8gles dans les sc\u00e9narios de flux. Cette double contribution implique la construction d’un cadre complet. Ce cadre comprend une couche de traitement des flux de donn\u00e9es qui int\u00e8gre plusieurs moteurs RSP, permettant l’expression de requ\u00eates plus complexes en exploitant les avantages et les capacit\u00e9s de ces moteurs. De plus, il int\u00e8gre une couche de raisonnement incr\u00e9mentiel pour le raisonnement sur les flux bas\u00e9 sur des r\u00e8gles, introduisant une nouvelle technique de raisonnement incr\u00e9mentiel et \u00e9tendant le Syst\u00e8me de Maintenance de la V\u00e9rit\u00e9 des Assomptions. Cette contribution fait progresser de mani\u00e8re significative la th\u00e9orie et la pratique du traitement de flux expressif et du raisonnement. En abordant syst\u00e9matiquement ces d\u00e9fis, nos am\u00e9liorations propos\u00e9es ouvrent la voie \u00e0 des syst\u00e8mes plus sophistiqu\u00e9s et adaptables dans le paysage \u00e9volutif du traitement et du raisonnement sur les flux. Les implications pratiques de ces contributions sont d\u00e9montr\u00e9es par une \u00e9valuation exp\u00e9rimentale am\u00e9lior\u00e9e, test\u00e9e et valid\u00e9e \u00e0 l’aide de donn\u00e9es obtenues dans des sc\u00e9narios du monde r\u00e9el. Today, real-time decision-making based on data streams from heterogeneous sources is increasingly widespread in many industrial scenarios. As a result, new mechanisms are required to manage data integration. To address this challenge, the RDF Stream Processing (RSP) community actively promoted the RDF data model and proposed several models and languages for continuously querying and reasoning over RDF streams. Hence, RDF stream processing and reasoning became an important research field. Stream reasoning provides meaning to the heterogeneous, ever-evolving, and often noisy data streams. Achieving such a goal requires a re-evaluation of problem-solving paradigms, as conclusions based on obsolete data need to be revised, new information is constantly arriving, and previous derivations may be reconsidered. Despite the achievements made over the last decade, many challenges remain to overcome. Firstly, no RSP engine can handle complex queries, support temporal reasoning, or integrate background knowledge sources. Furthermore, the issues of managing conflicting rules, reasoning on-the-fly and incrementally, and supporting multi-context reasoning in real-world applications emphasize the necessity of pushing forward in this area. This thesis investigates the area of Stream Reasoning and tackles the trade-off between data throughput and expressiveness, and the contribution is twofold : (i) First, we introduced a multi-agent-based framework for distributed continuous processing. This framework allows the utilization of multiple RSP engines simultaneously, leveraging their respective and complementary strengths. It enables the concurrent or sequential use of these engines to handle complex queries, offering a more versatile and comprehensive solution. (ii) Second, we proposed a novel technique for incremental reasoning explicitly tailored for the practical plain Linear Temporal Logic (LARS) fragment. This technique aims to optimize LARS programs, enhancing their efficiency. In addition, the Assumption Truth Maintenance System (ATMS) capabilities are extended for model updating. This extension facilitates a more comprehensive handling of conflicting information, contributing to the robustness of rule-based reasoning in streaming scenarios. This dual contribution involves the construction of a comprehensive framework. This framework includes a data stream processing layer that integrates multiple RSP engines, enabling the expression of more complex queries by leveraging their benefits and capabilities. Additionally, it incorporates an incremental reasoning layer for rule-based stream reasoning, introducing a novel incremental reasoning technique and extending the Assumption Truth Maintenance System. This contribution significantly advances the theory and practice of expressive stream processing and reasoning. By systematically addressing these challenges, our proposed enhancements pave the way for more sophisticated and adaptable systems in the evolving landscape of stream processing and reasoning. The practical implications of these contributions are demonstrated through an improved experimental evaluation, which has been tested and validated using data obtained from real-world scenarios.<\/p>\n","protected":false},"excerpt":{"rendered":" L’Ecole doctorale : Ecole Doctorale de l’Institut Polytechnique de Paris et le Laboratoire de recherche SAMOVAR – Services r\u00e9partis, Architectures, Mod\u00e9lisation, Validation, Administration des R\u00e9seaux pr\u00e9sentent l\u2019AVIS DE SOUTENANCE de Madame Wafaa MEBREK Autoris\u00e9e \u00e0 pr\u00e9senter ses travaux en vue de l\u2019obtention du Doctorat de l’Institut Polytechnique de Paris, pr\u00e9par\u00e9 \u00e0 T\u00e9l\u00e9com SudParis en : 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M. Frederique LAFOREST <\/strong>, Professeure, INSA Lyon, laboratoire LIRIS, FRANCE – Rapporteur
M. Maxime LEFRAN\u00e7OIS <\/strong>, Associate Professor, IMT – MINES Saint-\u00c9tienne – Institut Henri Fayol , Laboratoire LIMOS – UMR CNRS 6158 , FRANCE – Examinateur
Mme Zoubida KEDAD <\/strong>, Professeure, Universit\u00e9 de Versailles Saint-Quentin-en-Yvelines, FRANCE – Examinateur
Mme Nadia ABCHICHE-MIMOUNI<\/strong>, Ma\u00eetresse de conf\u00e9rences, Universit\u00e9 C\u00f4te d\u2019Azur, FRANCE – Examinateur
Mme Amel BOUZEGHOUB<\/strong>, Professeure, Telecom SudParis , FRANCE – Directeur de these<\/p>\n\n\n\n\u00ab Traitement et raisonnement incr\u00e9mental sur les flux RDF \u00bb<\/h2>\n\n\n\n
pr\u00e9sent\u00e9 par Madame Wafaa MEBREK<\/h2>\n\n\n\n
Abstract :<\/strong><\/p>\n\n\n\n