http://dl.gi.de/handle/20.500.12116/11565 2026-07-21T14:08:53Z http://dl.gi.de/handle/20.500.12116/11742 News 2015-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11747 Managed Query Processing within the SAP HANA Database Platform May, Norman; Böhm, Alexander; Block, Meinolf; Lehner, Wolfgang The SAP HANA database extends the scope of traditional database engines as it supports data models beyond regular tables, e.g. text, graphs or hierarchies. Moreover, SAP HANA also provides developers with a more fine-grained control to define their database application logic, e.g. exposing specific operators which are difficult to express in SQL. Finally, the SAP HANA database implements efficient communication to dedicated client applications using more effective communication mechanisms than available with standard interfaces like JDBC or ODBC. These features of the HANA database are complemented by the extended scripting engine–an application server for server-side JavaScript applications–that is tightly integrated into the query processing and application lifecycle management. As a result, the HANA platform offers more concise models and code for working with the HANA platform and provides superior runtime performance.This paper describes how these specific capabilities of the HANA platform can be consumed and gives a holistic overview of the HANA platform starting from query modeling, to the deployment, and efficient execution. As a distinctive feature, the HANA platform integrates most steps of the application lifecycle, and thus makes sure that all relevant artifacts stay consistent whenever they are modified. The HANA platform also covers transport facilities to deploy and undeploy applications in a complex system landscape. 2015-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11748 Complex Event Processing on Linked Stream Data Saleh, Omran; Hagedorn, Stefan; Sattler, Kai-Uwe Social networks and Sensor Web technologies typically generate a massive amount of data published as streams. In order to give these streams a meaningful sense and enrich them with semantic descriptions, the concept of Linked Stream Data (LSD) has emerged. However, to support a wide range of LSD scenarios and queries comprehensive solutions providing not only classic data stream operators such as windows, but also for processing of complex events, linking of (static) datasets, and scalable processing are required. In this paper, we present our approach for processing LSD and addressing these requirements. In contrast to existing LSD engines relying on streaming extensions to SPARQL, our PipeFlow system is a (relational) dataflow language and engine providing support for complex event processing (CEP) and a few dedicated operators for RDF data. We describe this language and particularly the CEP model as well as the system architecture for parallel CEP and LSD processing by exploiting partitioning techniques for cluster environments. Finally, we report results from experiments evaluating our system in comparison to existing LSD engines. 2015-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11744 Editorial Mitschang, Bernhard; Nicklas, Daniela; Härder, Theo 2015-01-01T00:00:00Z