From 2a5cc39d9a3d75d79ba8a573c07d3cfc4ba667ca Mon Sep 17 00:00:00 2001 From: Manos Athanassoulis Date: Fri, 21 Jun 2024 12:25:50 -0400 Subject: [PATCH] fix --- hdms/2024/program.html | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/hdms/2024/program.html b/hdms/2024/program.html index bccc89d..478b890 100644 --- a/hdms/2024/program.html +++ b/hdms/2024/program.html @@ -382,15 +382,14 @@

Download the program 3:30 - 4:00pm - Keynote 2: Robust Query Optimization in the Era of Machine Learning, Verena Kantere (Univeristy of Ottawa) -
Click to display the abstract and bio - - Abstract: Query optimizers are an essential component of database management systems (DBMSs) as they search for an execution plan that is expected to be optimal for a given query. However, they commonly use parameter estimates that are often inaccurate and make assumptions that may not hold in practice. Consequently, the optimizer may select sub-optimal execution plans at runtime, when these estimates and assumptions are not valid, which may result in poor query performance. Therefore, query optimizers do not adequately support the robustness of the database system. In this talk, we will explore the notion of robustness in the context of query optimization, as well as how it is evaluated or supported. We focus on comparing traditional cost-model-based methods with modern ML-based techniques in terms of their ability to tackle the challenge of robustness in query optimization. In this context we will discuss briefly recent research results on the creation of robust ML-based query optimization techniques in the M2oDA lab https://www.verenakantere.com/moda/home.html -

- Bio: Dr Verena Kantere is a Full Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa. She has been an Assistant Professor in the School of Electrical and Computer Engineering (ECE) at National Technical University of Athens (NTUA), as well as a Maître Assistante and later a Maître d’Enseignement et de Recherche, at the Centre Universitaire d’ Informatique (CUI) of the University of Geneva (UniGe), where she started working after winning the interdisciplinary competition for young researchers “Boursière d’ Excellence”. Before coming to (UniGe) Dr Kantere was a tenure-track junior assistant professor at the Department of Electrical Engineering and Information Technology at the Cyprus University of Technology (CUT). Dr Kantere has been working towards the provision of data management and services in large-scale systems, including cloud computing systems distributed systems and hybrid systems, focusing on properties of Big Data, the performance of Big Data analytics and multi-objective optimization, query optimization etc. She has developed methods, algorithms and fully fledged systems. Dr Kantere has been a member of more than 160 program committees and served as member of editorial board or guest editor in many journals. More information in: https://www.verenakantere.com/ - - - + Keynote 2: Robust Query Optimization in the Era of Machine Learning, Verena Kantere (Univeristy of Ottawa) +
Click to display the abstract and bio + + Abstract: Query optimizers are an essential component of database management systems (DBMSs) as they search for an execution plan that is expected to be optimal for a given query. However, they commonly use parameter estimates that are often inaccurate and make assumptions that may not hold in practice. Consequently, the optimizer may select sub-optimal execution plans at runtime, when these estimates and assumptions are not valid, which may result in poor query performance. Therefore, query optimizers do not adequately support the robustness of the database system. In this talk, we will explore the notion of robustness in the context of query optimization, as well as how it is evaluated or supported. We focus on comparing traditional cost-model-based methods with modern ML-based techniques in terms of their ability to tackle the challenge of robustness in query optimization. In this context we will discuss briefly recent research results on the creation of robust ML-based query optimization techniques in the M2oDA lab https://www.verenakantere.com/moda/home.html +

+ Bio: Dr Verena Kantere is a Full Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa. She has been an Assistant Professor in the School of Electrical and Computer Engineering (ECE) at National Technical University of Athens (NTUA), as well as a Maître Assistante and later a Maître d’Enseignement et de Recherche, at the Centre Universitaire d’ Informatique (CUI) of the University of Geneva (UniGe), where she started working after winning the interdisciplinary competition for young researchers “Boursière d’ Excellence”. Before coming to (UniGe) Dr Kantere was a tenure-track junior assistant professor at the Department of Electrical Engineering and Information Technology at the Cyprus University of Technology (CUT). Dr Kantere has been working towards the provision of data management and services in large-scale systems, including cloud computing systems distributed systems and hybrid systems, focusing on properties of Big Data, the performance of Big Data analytics and multi-objective optimization, query optimization etc. She has developed methods, algorithms and fully fledged systems. Dr Kantere has been a member of more than 160 program committees and served as member of editorial board or guest editor in many journals. More information in: https://www.verenakantere.com/ + +