Auflistung nach Autor:in "Lehner, Wolfgang"
1 - 10 von 371
Treffer pro Seite
Sortieroptionen
- KonferenzbeitragAccelerating Large Table Scan using Processing-In-Memory Technology(BTW 2023, 2023) Baumstark, Alexander; Jibril, Muhammad Attahir; Sattler, Kai-UweToday’s systems are capable of storing large amounts of data in main memory. In-memoryDBMSs can benefit particularly from this development. However, the processing of the data fromthe main memory necessarily has to run via the CPU. This creates a bottleneck, which affects thepossible performance of the DBMS. The Processing-In-Memory (PIM) technology is a paradigm toovercome this problem, which was not available in commercial systems for a long time. However, withthe availability of UPMEM, a commercial system is finally available that provides PIM technologyin hardware. In this work, the main focus was on the optimization of the table scan, a fundamental,and memory-bound operation. Here a possible approach is shown, which can be used to optimizethis operation by using PIM. This method was then tested for parallelism and execution time inbenchmarks with different table sizes and compared to the usual table scan. The result is a table scanthat outperforms the scan on the usual CPU significantly.
- KonferenzbeitragAdaptive Architectures for Robust Data Management Systems(BTW 2023, 2023) Bang, TiemoForm follows function is a well-known expression by the architect Sullivan asserting that the architecture of a building should follow its function. 'Adaptive Architectures for Robust Data Management Systems' is a dissertation asserting that DBMS architectures should follow changing workload and hardware to robustly achieve high DBMS performance. The dissertation first evaluates how workload and hardware affect the performance of DBMSs with static architectures. This evaluation concludes that static DBMS architectures degrade DBMS performance under changing workload and hardware, and hence the DBMS architecture has to become adaptive. Subsequently, adaptation concepts for the architecture of single-server and multi-server DBMSs are proposed. These concepts focus fine-grained adaptation of DBMS architectures and are realized through asynchronous programming models. These programming models decouple the implementation of DBMS components from fine-grained architectural optimization. Thereby, optimizers can derive novel architectures better fitting individual DBMS components, leading to high and robust DBMS performance under changing conditions.
- KonferenzbeitragAdvanced cardinality estimation in the XML query graph model(Datenbanksysteme für Business, Technologie und Web (BTW), 2011) Weiner, Andreas M.Reliable cardinality estimation is one of the key prerequisites for effective cost-based query optimization in database systems. The XML Query Graph Model (XQGM) is a tuple-based XQuery algebra that can be used to represent XQuery expressions in native XML database management systems. This paper enhances previous works on reliable cardinality estimation for XQuery and introduces several inference rules that deal with the unique features of XQGM, such as native support for Structural Joins, nesting, and multi-way merging. These rules allow to estimate the runtime cardinalities of XQGM operators. Using this approach, we can support classical join reordering with appropriate statistical information, perform cost-based query unnesting, and help to find the best evaluation strategy for value-based joins. The effectiveness of our approach for query optimization is evaluated using the query optimizer of XTC.
- TextdokumentAggregate-based Training Phase for ML-based Cardinality Estimation(BTW 2021, 2021) Woltmann, Lucas; Hartmann, Claudio; Habich, Dirk; Lehner, WolfgangCardinality estimation is a fundamental task in database query processing and optimization. As shown in recent papers, machine learning (ML)-based approaches may deliver more accurate cardinality estimations than traditional approaches. However, a lot of training queries have to be executed during the model training phase to learn a data-dependent ML model making it very time-consuming. Many of those training or example queries use the same base data, have the same query structure, and only differ in their selective predicates. To speed up the model training phase, our core idea is to determine a predicate-independent pre-aggregation of the base data and to execute the example queries over this pre-aggregated data. Based on this idea, we present a specific aggregate-based training phase for ML-based cardinality estimation approaches in this paper. As we are going to show with different workloads in our evaluation, we are able to achieve an average speedup of 63 with our aggregate-based training phase and thus outperform indexes.
- KonferenzbeitragAIMS: an SQL-based system for airspace monitoring(Datenbanksysteme für Business, Technologie und Web (BTW), 2011) Schüller, Gereon; Behrend, AndreasThe “Airspace Monitoring System” (AIMS) is a system for monitoring and analyzing flight data streams with respect to the occurrence of arbitrary complex events. It is a general system that allows for a comprehensive analysis of aircraft movements, in contrast to already existing tools which focus on a single task like flight delay detection. For instance, the system is able to detect critical deviations from the current flight plan, abnormal approach parameters of landing flights as well as areas with an increased risk of collisions. To this end, tracks are extracted from cluttered radar data and SQL views are employed for a timely processing of these tracks. Additionally, the data is stored for later analysis.
- KonferenzbeitragAnalyse des Einsatzpotenzials von In-Memory-Technologien in Handelsinformationssystemen(IMDM 2011 – Proceedings zur Tagung Innovative Unternehmensanwendungen mit In-Memory Data Management, 2011) Schütte, ReinhardIn-Memory-Technologien eröffnen vor allem bei Anwendungen, in denen große Datenmengen zu verarbeiten sind, interessante Einsatzpotentiale. In dem Beitrag wird mit dem Lunar-Programm das weltweit größte Projekt im Handel vorgestellt, um darauf aufbauend Einsatzpotentiale aus einer betriebswirtschaftlichen Perspektive in und aus einer technologischen Perspektive beim Betrieb von Handelsinformationssystemen heraus zu identifizieren.
- KonferenzbeitragAnforderungen an Datenbanksysteme für Multi-Tenancy- und Software-as-a-Service-Applikationen(Datenbanksysteme in Business, Technologie und Web (BTW) – 13. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 2009) Aulbach, Stefan; Jacobs, Dean; Primsch, Jürgen; Kemper, AlfonsFür Multi-Tenancy-Applikationen und Software as a Service (SaaS) stellen sich konventionelle Datenbanksysteme oftmals als ungeeignet heraus. In einem solchen Umfeld müssen Anforderungen, wie Erweiterbarkeit und Verfügbarkeit, trotz geringer Kosten für den
- KonferenzbeitragAnfragegetriebene Indizierung räumlicher Daten(Informatik 2009 – Im Focus das Leben, 2009) Voigt, Hannes; Preißler, Steffen; Böhm, Matthias; Lehner, Wolfgang
- KonferenzbeitragAnnotationsbasierte Prozessmodellierung in SOA – dargestellt an einem Beispiel aus dem Precision Dairy Farming(Precision Agriculture Reloaded – Informationsgestützte Landwirtschaft, 2010) Gietl, Franziska; Spilke, Joachim; Habich, Dirk; Lehner, WolfgangBei der Entwicklung einer serviceorientierten Architektur im Bereich des Precision Dairy Farmings haben wir uns mit der Modellierung unternehmens-übergreifender Prozesse mit Hilfe der Business Process Modeling Notation (BPMN) beschäftigt. Da diese Modellierung stellenweise sehr abstrakt ist, schlagen wir einen angepassten Modellierungsansatz unter der Verwendung von Annotationen vor. Damit können notwendige Bedingungen direkt dem betreffenden Objekt zugeordnet werden, wodurch die Modellierung fachbezogener und damit für den Nutzer transparenter wird.
- KonferenzbeitragApproach to Synthetic Data Generation for Imbalanced Multi-class Problems with Heterogeneous Groups(BTW 2023, 2023) Treder-Tschechlov, Dennis; Reimann, Peter; Schwarz, Holger; Mitschang, BernhardTo benchmark novel classification algorithms, these algorithms should be evaluated on data with characteristics that also appear in real-world use cases. Important data characteristics that often lead to challenges for classification approaches are multi-class imbalance and heterogeneous groups. Real-world data that comprise these characteristics are usually not publicly available, e. g., because they constitute sensible patient information or due to privacy concerns. Further, the manifestations of the characteristics cannot be controlled specifically on real-world data. A more rigorous approach is to synthetically generate data such that different manifestations of the characteristics can be controlled. However, existing data generators are not able to generate data that feature both data characteristics, i. e., multi-class imbalance and heterogeneous groups. In this paper, we propose an approach that fills this gap as it allows to synthetically generate data that exhibit both characteristics. In particular, we make use of a taxonomy model that organizes real-world entities in domain-specific heterogeneous groups to generate data reflecting the characteristics of these groups. In addition, we incorporate probability distributions to reflect the imbalances of multiple classes and groups from real-world use cases. Our approach is applicable in different domains, as taxonomies are the simplest form of knowledge models and thus are available in many domains. The evaluation shows that our approach can generate data that feature the data characteristics multi-class imbalance and heterogeneous groups and that it allows to control different manifestations of these characteristics.