Query Optimization using Machine Learning Techniques / Fatima Zahra Chellama

Public ISBD Titre : Query Optimization using Machine Learning Techniques Type de document : document multimédia Auteurs : Fatima Zahra Chellama, Auteur ; Laradj Chellama, Directeur de thèse ; Sarah Saida Boudouh, Directeur de thèse Editeur : Laghouat : Université Amar Telidji - Département d'informatique Année de publication : 2024 Importance : 44 p. Accompagnement : 1 disque optique numérique (CD-ROM) Note générale : Option : Information and Decision Systems Langues : Anglais Mots-clés : Query Optimization  Deep Learning  PPO  UVFA  IMDB dataset Résumé : Query optimization is an important aspect in the design of relational database management systems (DBMS), aiming to find an optimal execution plan by minimizing the total execution time of queries. With this in mind, our work involves using a new paradigm such as deep reinforcement learning (Deep RL) is a sub-domain of machine learning that combines reinforcement learning (RL) and deep learning to improve query optimization approaches which is a complete NP problem. Through this task, we aim to reimplemente and adapt DRL algorithms to prove their performance. We use the Proximal Policy Optimization (PPO) algorithm as a model-Free , and the Universal Value Function Approximators (UVFA) with Hindsight Experience Replay. The tests of our modest expreinces on the IMDB dataset allowed us to observe a gradual performance by playing on the hyperparametres of PPO such as the activation function and a slight difference in favor of UVFA with HER. note de thèses : Mémoire de master en informatique Query Optimization using Machine Learning Techniques [document multimédia] / Fatima Zahra Chellama, Auteur ; Laradj Chellama, Directeur de thèse ; Sarah Saida Boudouh, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2024 . - 44 p. + 1 disque optique numérique (CD-ROM). Option : Information and Decision Systems Langues : Anglais Mots-clés : Query Optimization  Deep Learning  PPO  UVFA  IMDB dataset Résumé : Query optimization is an important aspect in the design of relational database management systems (DBMS), aiming to find an optimal execution plan by minimizing the total execution time of queries. With this in mind, our work involves using a new paradigm such as deep reinforcement learning (Deep RL) is a sub-domain of machine learning that combines reinforcement learning (RL) and deep learning to improve query optimization approaches which is a complete NP problem. Through this task, we aim to reimplemente and adapt DRL algorithms to prove their performance. We use the Proximal Policy Optimization (PPO) algorithm as a model-Free , and the Universal Value Function Approximators (UVFA) with Hindsight Experience Replay. The tests of our modest expreinces on the IMDB dataset allowed us to observe a gradual performance by playing on the hyperparametres of PPO such as the activation function and a slight difference in favor of UVFA with HER. note de thèses : Mémoire de master en informatique

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
MF 02-66	MF 02-66	CD	BIBLIOTHEQUE DE FACULTE DES SCIENCES	théses (sci)	Disponible

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