http://dl.gi.de/handle/20.500.12116/18382 2026-07-21T13:23:19Z http://dl.gi.de/handle/20.500.12116/18392 Realisierung einer serviceorientierten Business Intelligence Architektur anhand von In-Memory-Technologien Pospiech, Marco; Felden, Carsten Lehner, Wolfgang; Piller, Gunther Business Intelligence (BI) verspricht eine verbesserte Entscheidungsfindung. [G108] Um den veränderten Unternehmensanforderungen gerecht zu werden, ist das traditionelle Konzeot zu erweitern. Infolgedessen sind Real Time, Active, Operational, Embedded oder prozessorientierte BI entstanden, um den steigenden Bedürfnissen nachzukommen. Diese verlangen jdeoch eine technologische Umsetzung. In diesem Zusammenhang gehen aktuelle Ansätze dazu über, den wechselnden BI-Paradigmen durch einen serviceorientierten Ansatz zu begegnen [Di08]. Störend erweist sich hierbei, dass dieses Konzept dem Datenaufkommen nicht gewachsen ist. [Vo08] Der vorliegende Beitrag adressiert diese Lücke, indem unter Verwendung der Referenzmodellierung eine serviceorientierte Business Intelligence (SoBI) erarbeitet wird, die mit Hilfe von In-Memory-Technologien den aufkommenden Bedürfnissen gerecht werden soll. Als erstes Artefakt entsteht ein Konzept, welches für spätere Realisierungen die entsprechende Grundlage bietet. 2011-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/18393 The mainframe strikes back: multi tenancy in the main memory database HyPer on a TB-server Mühe, Henrik; Kemper, Alfons; Neumann, Thomas Lehner, Wolfgang; Piller, Gunther Contrary to recent trends in database systems research focussing on scaling out workloads on a cluster of commodity computers, this presentation will break grounds for scale-up. We show that an elastic multi-tenancy solution can be achieved by combining a many-core server with a low footprint main memory database system. Total transactional throughput for TPC-C like order-entry transactions reaches up to 2 million transactions per second on a 32 core server while the number of tenants sharing a single server can be varied from a few to hundreds of separate tenants without diminishing total throughput. Contrary to common belief, a scale-up solution provides high flexibility for tenants with growing throughput needs and allows for simple sharing of common resources between different tenants while minimizing hardware and computing overhead. We show that our approach can handle changes in tenant requirements with minimal impact on other tenants on the server. Additionally, we prove that our architecture provides sufficient per-tenant throughput to handle big tenants and scales well with database size. 2011-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/18394 Real-time semantic process change impact analysis Emrich, Andreas; Werth, Dirk; Loos, Peter Lehner, Wolfgang; Piller, Gunther Today's environment and business is constantly changing on a rapid pace. Models have to be adapted in order to fit the changing situations. In contrast, the complexity of enterprise models is a huge problem wrt the management of such complex models. High-performance computing technologies offer a great opportunity to leverage increased computational power to cope with that complexity. In this paper we will present an approach for determining the impact of process changes using a semantic context model for BPM that enables semantic querying on complex business process models. Based on this context models large ontologies such as Cyc and process repositories such as the MIT process handbook are queried in complex scenarios of our evaluation. In-memory databases such as Couch DB and Hadoop serve as technological basis for this evaluation. The paper concludes that such queries can be performed nearly in real-time. 2011-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/18390 Business benefits and application capabilities enabled by in-memory data management Piller, Gunther; Hagedorn, Jürgen Lehner, Wolfgang; Piller, Gunther Current developments in the area of in-memory data management can significantly change the way business applications will be used in the future. Thus, it will be possible to store huge volumes of single documents directly in main memory for high-speed processing. To introduce and evolve in-memory data management successfully, it is necessary to understand which types of applications benefit most from this new technology. To address this question, we develop typical application patterns. They help to identify promising domains for this innovative technology. We also introduce parameters which support a systematic assessment of corresponding benefits. Our approach is illustrated with examples from different industries. 2011-01-01T00:00:00Z