Logo des Repositoriums

Auflistung nach Autor:in "Uflacker, Matthias"

1 - 2 von 2
  • Konferenzbeitrag
    Generic Business Simulation Using an In-Memory Column Store
    (Datenbanksysteme für Business, Technologie und Web (BTW 2015), 2015) Butzmann, Lars; Klauck, Stefan; Müller, Stephan; Uflacker, Matthias; Sinzig, Werner; Plattner, Hasso
    Value driver trees are a well-known methodology to model dependencies such as the definition of key performance indicators. While the models have well-known semantics, they lack the right tool support for business simulations, because a flexible implementation that supports multidimensional, hierarchical value driver trees and data bindings is very complex and computationally challenging. This paper tackles this problem by proposing an approach for generic enterprise simulations which are based on value driver trees. Our approach is two-fold: we present the definition of a simulation meta model at design time, and the run-time simulation tool. The simulation meta model describes the structure of the dependency graph, the data binding, and the parametrization of the model to simulate data changes. The simulation tool can then be used to create and edit simulation model instances and run simulations in real-time by leveraging an in-memory column store. Besides the formal description of the approach, this work presents a prototypical implementation of the simulation tool and an evaluation using data of a consumer packaged goods company.
  • Textdokument
    Workload-Driven Data Placement for GPU-Accelerated Database Management Systems
    (BTW 2019 – Workshopband, 2019) Schmidt, Christopher; Uflacker, Matthias
    An increase in the memory capacity of current Graphics Processing Unit (GPU) generations and advances in multi-GPU systems enables a large unified GPU memory space to be utilized by modern coprocessor-accelerated Database Management System (DBMS). We take this as an opportunity to revisit the idea of using GPU memory as a hot cache for the DBMS. In particular, we focus on the data placement for the hot cache. Based on previous approaches and their shortcomings, we present a new workload-driven data placement for a GPU-accelerated DBMS. Lastly, we outline how we aim to implement and evaluate our proposed approach by comparing it to existing data placement approaches in future work.