Aus der Einführung: "Die Verwendung von Prototypen besitzt eine zentrale Rolle bei der Produktentwicklung und im Designprozess (Camere et al. 2016). Wie Camere und Bordegoni feststellen hat sich der Fokus der Designdisziplin auf Funktionalität um die Aspekte der Usability und der User Experience erweitert. Damit einhergehend hat sich auch die Rolle der Prototypen von Funktionsmustern und Präsentationsobjekten um die Funktionen als Evaluierungs- und Versuchsobjekte ergänzt. Die Integration von Nutzern in den Designprozess ist dabei ein weiterer Punkt bei denen Prototypen ein wichtiges Werkzeug zur Kommunikation und kooperativen Arbeit liefert (Schneider 1996). Die Integration von Prototyping-Methoden in den unterschiedlichen Phasen des Designprozesses führt zu unterschiedlichen Anforderungen hinsichtlich der Form und Aufgabe der Prototypen. Entsprechend vielfältig sind die heute verwendeten Arten von Prototyping. ..."

Automatic summary tables (ASTs), more commonly known as materialized views, are widely used to enhance query performance, particularly for aggregate queries. Such queries access a huge number of rows to retrieve aggregated summary data while performing multiple joins in the context of a typical data warehouse star schema. To keep ASTs consistent with their underlying base data, the ASTs are either immediately synchronized or fully recomputed. This paper proposes an optimization strategy for simultaneously refreshing multiple ASTs, thus avoiding multiple scans of a large fact table (one pass for AST computation). A query stacking strategy detects common sub-expressions using the available query matching technology of DB2. Since exact common sub-expressions are rare, the novel query sharing approach systematically generates common subexpressions for a given set of 'related' queries, considering different predicates, grouping expressions, and sets of base tables. The theoretical framework, a prototype implementation of both strategies in the IBM DB2 UDB/UWO database system, and performance evaluations based on the TPC/R data schema are presented in this paper.

This paper reflects the results of the evaluation phase of building a data production system for the retail research division of the GfK, Europe's largest market research company. The application specific requirements like end-user needs or data volume are very different from data warehouses discussed in the literature, making it a real data warehouse. In a case study, these requirements are compared with state-of-the-art solutions offered by leading software vendors. Each of the common architectures (MOLAP, ROLAP, HOLAP) was represented by a product. The result of this comparison is that all systems have to be massively tailored to GfK's needs, especially to cope with meta data management or the maintenance of aggregations.

This paper reflects the results of the evaluation phase of building a data production system for the retail research division of the GfK, Europe's largest market research company. The application specific requirements like end-user needs or data volume are very different from data warehouses discussed in the literature, making it a real data warehouse. In a case study, these requirements are compared with state-of-the-art solutions offered by leading software vendors. Each of the common architectures (MOLAP, ROLAP, HOLAP) was represented by a product. The result of this comparison is that all systems have to be massively tailored to GfK's needs, especially to cope with meta data management or the maintenance of aggregations.

The integration of heterogeneous data sources is one of the main challenges within the area of data engineering. Due to the absence of an independent and universal benchmark for data-intensive integration processes, we propose a scalable benchmark, called DIPBench (Data intensive integration Process Benchmark), for evaluating the performance of integration systems. This benchmark could be used for subscription systems, like replication servers, distributed and federated DBMS or message-oriented middleware platforms like Enterprise Application Integration (EAI) servers and Extraction Transformation Loading (ETL) tools. In order to reach the mentioned universal view for integration processes, the benchmark is designed in a conceptual, process-driven way. The benchmark comprises 15 integration process types. We specify the source and target data schemas and provide a toolsuite for the initialization of the external systems, the execution of the benchmark and the monitoring of the integration system's performance. The core benchmark execution may be influenced by three scale factors. Finally, we discuss a metric unit used for evaluating the measured integration system's performance, and we illustrate our reference benchmark implementation for federated DBMS.