http://dl.gi.de/handle/20.500.12116/11568 2026-07-21T14:08:47Z http://dl.gi.de/handle/20.500.12116/11751 Query Optimization in Heterogenous Event Processing Federations Pinnecke, Marcus; Hoßbach, Bastian Continuous processing of event streams evolved to an important class of data management over the last years and will become even more important due to novel applications such as the Internet of Things. Because systems for data stream and event processing have been developed independent of each other, often in competition and without the existence of any standards, the Stream Processing System (SPS) landscape is extremely heterogeneous today. To overcome the problems caused by this heterogeneity, a novel event processing middleware, the Java Event Processing Connectivity (JEPC), has been presented recently. However, despite the fact that SPSs can be accessed uniformly using JEPC, their different performance profiles caused by different algorithms and implementations remain. This gives the opportunity to query optimization, because individual system strengths can be exploited. In this paper, we present a novel query optimizer that exploits the technical heterogeneity in a federation of different unified SPSs. Taking into account different performance profiles of SPSs, we address query plan partitioning, candidate selection, and reducing inter-system communication in order to improve the overall query performance. We suggest a heuristic that finds a good initial mapping of sub-plans to a set of heterogenous SPSs. An experimental evaluation clearly shows that heterogeneous federations outperform homogeneous federations, in general, and that our heuristic performs well in practice. 2015-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11759 Genome sequence analysis with MonetDB Cijvat, Robin; Manegold, Stefan; Kersten, Martin; Klau, Gunnar W.; Schönhuth, Alexander; Marschall, Tobias; Zhang, Ying Next-generation sequencing (NGS) technology has led the life sciences into the big data era. Today, sequencing genomes takes little time and cost, but yields terabytes of data to be stored and analyzed. Biologists are often exposed to excessively time consuming and error-prone data management and analysis hurdles. In this paper, we propose a database management system (DBMS) based approach to accelerate and substantially simplify genome sequence analysis. We have extended MonetDB, an open-source column-based DBMS, with a BAM module, which enables easy, flexible, and rapid management and analysis of sequence alignment data stored as Sequence Alignment/Map (SAM/BAM) files. We describe the main features of MonetDB/BAM using a case study on Ebola virusgenomes. 2015-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11760 VAT: A System for Visualizing, Analyzing and Transforming Spatial Data in Science Authmann, Christian; Beilschmidt, Christian; Drönner, Johannes; Mattig, Michael; Seeger, Bernhard The amount of available data changes the style of research in geo-scientific domains, and thus influences the requirements for spatial processing systems. To support data-driven research and exploratory workflows, we propose the Visualization, Analysis & Transformation system (VAT). We first identify ten fundamental requirements, which span from supporting spatial data types over low latency computations to visualization techniques. Based on these we evaluate state-of-the-art systems from the domains of spatial frameworks, GIS, workflow systems, scientific databases and Big Data solutions. The goal of the VAT system is to overcome the identified limitations by a holistic approach to raster and vector data, demand-driven and tiled processing, and the efficient usage of heterogeneous hardware architectures. A first comparison with other systems shows the validity of our approach. 2015-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11758 Editorial Härder, Theo 2015-01-01T00:00:00Z