Learned cost models (LCMs) have recently gained traction as a promising alternative to traditional cost estimation techniques in data management, offering improved accuracy by capturing complex interactions between queries, data, and runtime …
This paper presents the very first approach for opening the black box by bringing AI explainability approaches to Learned Cost Models (LCMs). New explanation techniques are proposed that extend and adapt existing methods for the general …
Query optimizers traditionally rely on cost models to choose the best execution plan, and while machine learning-based cost models have been proposed to overcome weaknesses of traditional models, limited efforts have been made to investigate how well …
In this paper, we present a novel pipelined GPU join that accelerates the performance of distributed DBMSs by leveraging GPU resources on fast networks. A key insight is that we enable pipelined join execution by overlapping the network shuffling …
This tutorial workshop at BTW 2023 explores the intersection of machine learning and systems, covering both the application of ML techniques to optimize and improve systems (ML for Systems) as well as systems designed to support and accelerate ML …
Recently, machine learning has successfully been applied to many database problems such as query optimization, physical design tuning, or cardinality estimation. However, the predominant paradigm to design such learned database components is …