Auswahl der wissenschaftlichen Literatur zum Thema „API (langue artificielle)“
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Dissertationen zum Thema "API (langue artificielle)"
Kobeissi, Meriana. „A conversational AI Framework for Cognitive Process Analysis“. Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS025.
Der volle Inhalt der QuelleBusiness 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
Bücher zum Thema "API (langue artificielle)"
Ben, Du Boulay, Hrsg. Artificial intelligence: Strategies, applications, and models through SEARCH. 2. Aufl. New York: Amacom, 1998.
Den vollen Inhalt der Quelle findenThornton, Christopher James. Artificial intelligence: Strategies, applications, and models through search. 2. Aufl. Chicago: Glenlake Pub. Co., 1998.
Den vollen Inhalt der Quelle findenBoulay, Benedict du, und Chris Thornton. Artificial Intelligence: Strategies, Applications and Models Through SEARCH. Global Professional Publishing, 2000.
Den vollen Inhalt der Quelle findenThornton, Chris, und Benedict DuBoulay. Artificial Intelligence: Strategies, Applications, and Models Through SEARCH. Taylor & Francis Group, 1998.
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