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 21 D\u00c9CEMBRE 2023 \u00e0 9h00<\/p>\n\n\n\n
\u00e0<\/p>\n\n\n\n
Amphith\u00e9\u00e2tre 2
19 place Marguerite Perey 91120 PALAISEAU<\/p>\n\n\n\n
Zoom Link: https:\/\/telecom-paris.zoom.us\/j\/97992186985?pwd=SDlmTjRNZzdpdW45anRGMHlURG0rdz09<\/a> Membres du jury :<\/strong><\/p>\n\n\n\n M. Bruno DEFUDE<\/strong>, Professor, T\u00e9l\u00e9com SudParis, FRANCE – Directeur de these R\u00e9sum\u00e9 :<\/strong><\/p>\n\n\n\n Les processus m\u00e9tier (BP) sont les piliers fondamentaux des organisations, englobant toute une gamme d’activit\u00e9s structur\u00e9es visant \u00e0 atteindre des objectifs organisationnels distincts. Ces processus, caract\u00e9ris\u00e9s par une multitude de t\u00e2ches, d’interactions et de flux de travail, offrent une m\u00e9thodologie structur\u00e9e pour superviser les op\u00e9rations cruciales dans divers secteurs. Une d\u00e9couverte essentielle pour les organisations a \u00e9t\u00e9 la reconnaissance de la valeur profonde inh\u00e9rente aux donn\u00e9es produites pendant ces processus. L’analyse des processus, une discipline sp\u00e9cialis\u00e9e, explore ces journaux de donn\u00e9es, facilitant une compr\u00e9hension plus profonde et l’am\u00e9lioration des BP. Cette analyse peut \u00eatre cat\u00e9goris\u00e9e en deux perspectives : le niveau d’instance, qui se concentre sur les ex\u00e9cutions individuelles de processus, et le niveau de processus, qui examine le processus global. Cependant, l’application de l’analyse des processus pose des d\u00e9fis aux utilisateurs, impliquant la n\u00e9cessit\u00e9 d’acc\u00e9der aux donn\u00e9es, de naviguer dans les API de bas niveau et d’utiliser des m\u00e9thodes d\u00e9pendantes d’outils. L’application dans le monde r\u00e9el rencontre souvent des complexit\u00e9s et des obstacles centr\u00e9s sur l’utilisateur. Plus pr\u00e9cis\u00e9ment, l’analyse de niveau d’instance exige des utilisateurs qu’ils acc\u00e8dent aux donn\u00e9es d’ex\u00e9cution de processus stock\u00e9es, une t\u00e2che qui peut \u00eatre complexe pour les professionnels de l’entreprise en raison de l’exigence de ma\u00eetriser des langages de requ\u00eate complexes tels que SQL et CYPHER. En revanche, l’analyse de niveau de processus des donn\u00e9es de processus implique l’utilisation de m\u00e9thodes et d’algorithmes qui exploitent les donn\u00e9es d’ex\u00e9cution de processus extraites des syst\u00e8mes d’information. Ces m\u00e9thodologies sont regroup\u00e9es sous le terme de techniques d’exploration de processus. L’application de l’exploration de processus confronte les analystes \u00e0 la t\u00e2che complexe de s\u00e9lection de m\u00e9thodes, qui consiste \u00e0 trier des descriptions de m\u00e9thodes non structur\u00e9es. De plus, l’application des m\u00e9thodes d’exploration de processus d\u00e9pend d’outils sp\u00e9cifiques et n\u00e9cessite un certain niveau d’expertise technique. Pour relever ces d\u00e9fis, cette th\u00e8se pr\u00e9sente des solutions bas\u00e9es sur l’IA, mettant l’accent sur l’int\u00e9gration de capacit\u00e9s cognitives dans l’analyse des processus pour faciliter les t\u00e2ches d’analyse tant au niveau de l’instance qu’au niveau du processus pour tous les utilisateurs. Les objectifs principaux sont doubles : premi\u00e8rement, am\u00e9liorer l’accessibilit\u00e9 des donn\u00e9es d’ex\u00e9cution de processus en cr\u00e9ant une interface capable de construire automatiquement la requ\u00eate de base correspondante \u00e0 partir du langage naturel. Ceci est compl\u00e9t\u00e9 par la proposition d’une technique de stockage adapt\u00e9e et d’un langage de requ\u00eate autour desquels l’interface doit \u00eatre con\u00e7ue. \u00c0 cet \u00e9gard, nous introduisons un m\u00e9ta-mod\u00e8le graphique bas\u00e9 sur le graphe de propri\u00e9t\u00e9s \u00e9tiquet\u00e9es (LPG) pour le stockage efficace des donn\u00e9es. Deuxi\u00e8mement, pour rationaliser la d\u00e9couverte et l’accessibilit\u00e9 des techniques d’exploration de processus, nous pr\u00e9sentons une architecture orient\u00e9e services. Pour valider notre m\u00e9ta-mod\u00e8le graphique, nous avons utilis\u00e9 deux ensembles de donn\u00e9es de processus accessibles au public disponibles \u00e0 la fois au format CSV et OCEL. Ces ensembles de donn\u00e9es ont \u00e9t\u00e9 essentiels pour \u00e9valuer les performances de notre pipeline de requ\u00eates en langage naturel. Nous avons recueilli des requ\u00eates en langage naturel aupr\u00e8s d’utilisateurs externes et en avons g\u00e9n\u00e9r\u00e9 d’autres \u00e0 l’aide d’outils de paraphrase. Notre cadre orient\u00e9 services a \u00e9t\u00e9 \u00e9valu\u00e9 \u00e0 l’aide de requ\u00eates en langage naturel sp\u00e9cialement con\u00e7ues pour les descriptions de services d’exploration de processus. De plus, nous avons men\u00e9 une \u00e9tude de cas avec des participants externes pour \u00e9valuer l’exp\u00e9rience utilisateur et recueillir des commentaires. Nous fournissons publiquement les r\u00e9sultats de l’\u00e9valuation pour garantir la reproductibilit\u00e9 dans le domaine \u00e9tudi\u00e9.<\/p>\n\n\n\n Business processes (BP) are the foundational pillars of organizations, encapsulating a range of structured activities aimed at fulfilling distinct organizational objectives. These processes, characterized by a plethora of tasks, interactions, and workflows, offer a structured methodology for overseeing crucial operations across diverse sectors. A pivotal insight for organizations has been the discernment of the profound value inherent in the data produced during these processes. Process analysis, a specialized discipline, ventures into these data logs, facilitating a deeper comprehension and enhancement of BPs. This analysis can be categorized into two perspectives: instance-level, which focuses on individual process executions, and process-level, which examines the overarching process. However, applying process analysis in practice poses challenges for users, involving the need to access data, navigate low-level APIs, and employ tool-dependent methods. Real-world application often encounters complexities and user-centric obstacles. Specifically, instance-level analysis demands users to access stored process execution data, a task that can be intricate for business professionals due to the requirement of mastering complex query languages like SQL and CYPHER. Conversely, process-level analysis of process data involves the utilization of methods and algorithms that harness process execution data extracted from information systems. These methodologies collectively fall under the umbrella of process mining techniques. The application of process mining confronts analysts with the intricate task of method selection, which involves sifting through unstructured method descriptions. Additionally, the application of process mining methods depends on specific tools and necessitates a certain level of technical expertise. To address these challenges, this thesis introduces AI-driven solutions, with a focus on integrating cognitive capabilities into process analysis to facilitate analysis tasks at both the instance level and the process level for all users. The primary objectives are twofold: Firstly, to enhance the accessibility of process execution data by creating an interface capable of automatically constructing the corresponding database query from natural language. This is complemented by proposing a suitable storage technique and query language that the interface should be designed around. In this regard, we introduce a graph metamodel based on Labeled Property Graph (LPG) for efficient data storage. Secondly, to streamline the discovery and accessibility of process mining techniques, we present a service-oriented architecture. This architecture comprises three core components: an LPG meta-model detailing process mining methods, a service-oriented REST API design tailored for these methods, and a component adept at matching user requirements expressed in natural language with appropriate services. For the validation of our graph metamodel, we utilized two publicly accessible process datasets available in both CSV and OCEL formats. These datasets were instrumental in evaluating the performance of our NL querying pipeline. We gathered NL queries from external users and produced additional ones through paraphrasing tools. Our service-oriented framework underwent an assessment using NL queries specifically designed for process mining service descriptions. Additionally, we carried out a use case study with external participants to evaluate user experience and to gather feedback. We publically provide the evaluation results to ensure reproducibility in the studied area.<\/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 Meriana KOBEISSI 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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Meeting ID: 979 9218 6985<\/a>
Passcode: 306290<\/p>\n\n\n\n
M. Dirk FAHLAND<\/strong>, Professor, Eindhoven University of Technology (TU\/e), PAYS-BAS – Rapporteur
M. Bassem HAIDAR<\/strong>, Professor, \u00c9cole sup\u00e9rieure d’informatique, \u00e9lectronique, automatique (ESIEA), FRANCE – Directeur de these
M. Jan MENDLING<\/strong>, Professeur, Humboldt-Universit\u00e4t zu Berlin, ALLEMAGNE – Rapporteur
M. Phillipe MERLE<\/strong>, Directeur de recherche, Inria, FRANCE – Examinateur
Mme Amel BOUZEGHOUB<\/strong>, Professor, T\u00e9l\u00e9com SudParis, FRANCE – Examinateur<\/p>\n\n\n\n\u00ab Un cadre d’IA conversationnel pour l’analyse des processus cognitifs \u00bb<\/h2>\n\n\n\n
pr\u00e9sent\u00e9 par Madame Meriana KOBEISSI<\/h2>\n\n\n\n
Abstract :<\/strong><\/p>\n\n\n\n