P331 - BTW2023- Datenbanksysteme für Business, Technologie und Web

Permanent URI for this collectionhttps://dl.gi.de/handle/20.500.12116/40312

Authors with most Documents  

König-Ries, Birgitta
6
Habich, Dirk
5
Lehner, Wolfgang
4
Binnig, Carsten
3
Hartmann, Claudio
3

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Author: search.filters.author.Hartmann, Claudio

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  • Conference Paper
    Optimizing Query Processing in PostgreSQL Through Learned Optimizer Hints
    (Gesellschaft für Informatik e.V., 2023) Thiessat, Jerome; Woltmann, Lucas; Hartmann, Claudio; Habich, Dirk; König-Ries, Birgitta; Scherzinger, Stefanie; Lehner, Wolfgang; Vossen, Gottfried
    Query optimization in database systems is an important aspect and despite decades of research, it isstill far from being solved. Nowadays, query optimizers usually provide hints to be able to steer theoptimization on a query-by-query basis. However, setting the best-fitting hints is challenging. To tacklethat, we present a learning-based approach to predict the best-fitting hints for each incoming query. Inparticular, our learning approach is based on simple gradient boosting, where we learn one modelper query context for fine-grained predictions rather than a single global context-agnostic model asproposed in related work. We demonstrate the efficiency as well as effectiveness of our learning-basedapproach using the open-source database system PostgreSQL and show that our approach outperformsrelated work in that context.
  • Conference Paper
    JumpXClass: Explainable AI for Jump Classification in Trampoline Sports
    (Gesellschaft für Informatik e.V., 2023) Woltmann, Lucas; Ferger, Katja; Hartmann, Claudio; Lehner, Wolfgang; König-Ries, Birgitta; Scherzinger, Stefanie; Lehner, Wolfgang; Vossen, Gottfried
    Movement patterns in trampoline gymnastics have become faster and more complex with the increase in the athletes’ capabilities. This makes the assessment of jump type, pose, and quality during training or competitions by humans very difficult or even impossible. To counteract this development, data-driven solutions are thought to be a solution to improve training. In recent work, sensor measurements and machine learning is used to automatically predict jumps and give feedback to the athletes and trainers. However, machine learning models, and especially neural networks, are black boxes most of the time. Therefore, the athletes and trainers cannot gain any insights about the jump from the machine learning-based jump classification. To better understand the jump execution during training, we propose JumpXClass: a tool for automatic machine learning-based jump classification with explainable artificial intelligence. Using elements of explainable artificial intelligence can improve the training experience for athletes and trainers. This work will demonstrate a live system capable to classify and explain jumps from trampoline athletes.
  • Conference Paper
    PostBOUND: PostgreSQL with Upper Bound SPJ Query Optimization
    (Gesellschaft für Informatik e.V., 2023) Bergmann, Rico; Hertzschuch, Axel; Hartmann, Claudio; Habich, Dirk; Lehner, Wolfgang; König-Ries, Birgitta; Scherzinger, Stefanie; Lehner, Wolfgang; Vossen, Gottfried
    A variety of query optimization papers have shown the disastrous effect of poor cardinality estimates on the overall run time for arbitrary select-project-join (SPJ) queries.Especially, underestimating join cardinalities for multi-joins can lead to catastrophic join orderings. A promising solution to overcome this problem is query optimization based on upper bounds for the join cardinalities. In this domain, our proposed UES concept is presently the most efficient technique featuring a simple, yet effective upper bound for an arbitrary number of joins. To foster research in that direction, we introduce PostBOUND, our generalized framework making upper bound SPJ query optimization a first class citizen in PostgreSQL.PostBOUND provides abstractions to calculate arbitrary upper bounds, to model joins required by an SPJ query and to iteratively construct an optimized join order.To highlight the extensibility of PostBOUND and to show the research potential, we additionally present two tighter upper bound UES variants using top-k statistics in this paper.In our evaluation, we show the efficiency and applicability of PostBOUND on different workloads as well as using different PostgreSQL versions. Additionally, we evaluate both presented tighter upper bound variant ideas.
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