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Auflistung nach Schlagwort "DBMS"

1 - 3 von 3
  • Konferenzbeitrag
    Experiences from Building the Open Database Performance Ranking with benchANT
    (Softwaretechnik-Trends Band 43, Heft 1, 2023) Seybold, Daniel; Domaschka, Jörg
    Benchmarking is an important method to advance database management systems (DBMS) from the industry and research perspective. Ensuring transparent and reproducible results is a key requirement to ensure the acceptance and credibility of benchmarking. To advance the research towards transparent and reproducible benchmark data, we report on building an open DBMS performance ranking with 130 benchmark configurations and ensuring comparability, transparency and reproducibility. We derive the required data on cloud, resource, DBMS and benchmark level to enable transparency and reproducibility and demonstrate the generation of such data sets with benchANT. Building upon such data, we outline future research directions for DBMS performance modelling, DBMS auto-tuning and decision support.
  • Zeitschriftenartikel
    The Design and Implementation of CoGaDB: A Column-oriented GPU-accelerated DBMS
    (Datenbank-Spektrum: Vol. 14, No. 3, 2014) Breß, Sebastian
    Nowadays, the performance of processors is primarily bound by a fixed energy budget, the power wall. This forces hardware vendors to optimize processors for specific tasks, which leads to an increasingly heterogeneous hardware landscape. Although efficient algorithms for modern processors such as GPUs are heavily investigated, we also need to prepare the database optimizer to handle computations on heterogeneous processors. GPUs are an interesting base for case studies, because they already offer many difficulties we will face tomorrow.In this paper, we present CoGaDB, a main-memory DBMS with built-in GPU acceleration, which is optimized for OLAP workloads. CoGaDB uses the self-tuning optimizer framework HyPE to build a hardware-oblivious optimizer, which learns cost models for database operators and efficiently distributes a workload on available processors. Furthermore, CoGaDB implements efficient algorithms on CPU and GPU and efficiently supports star joins. We show in this paper, how these novel techniques interact with each other in a single system. Our evaluation shows that CoGaDB quickly adapts to the underlying hardware by increasing the accuracy of its cost models at runtime.
  • Zeitschriftenartikel
    Towards Integrated Data Analytics: Time Series Forecasting in DBMS
    (Datenbank-Spektrum: Vol. 13, No. 1, 2013) Fischer, Ulrike; Dannecker, Lars; Siksnys, Laurynas; Rosenthal, Frank; Boehm, Matthias; Lehner, Wolfgang
    Integrating sophisticated statistical methods into database management systems is gaining more and more attention in research and industry in order to be able to cope with increasing data volume and increasing complexity of the analytical algorithms. One important statistical method is time series forecasting, which is crucial for decision making processes in many domains. The deep integration of time series forecasting offers additional advanced functionalities within a DBMS. More importantly, however, it allows for optimizations that improve the efficiency, consistency, and transparency of the overall forecasting process. To enable efficient integrated forecasting, we propose to enhance the traditional 3-layer ANSI/SPARC architecture of a DBMS with forecasting functionalities. This article gives a general overview of our proposed enhancements and presents how forecast queries can be processed using an example from the energy data management domain. We conclude with open research topics and challenges that arise in this area.