Die Arbeit diskutiert Lösungsvarianten und beschreibt die Implementation eines Prototyps zur Re-Organisation der Nutzerverwaltung des URZ. Besondere Schwerpunkte liegen auf der Erstellung eines Web-Interfaces zur Datenverwaltung und auf der Untersuchung relationaler Datenbanksysteme hinsichtlich der Eignung zur Lösung der Aufgabe. Der Prototyp stellt lediglich eine beispielhafte Implementierung einiger Teilkomplexe des Gesamtsystems dar.

Die Arbeit diskutiert Lösungsvarianten und beschreibt die Implementation eines Prototyps zur Re-Organisation der Nutzerverwaltung des URZ. Besondere Schwerpunkte liegen auf der Erstellung eines Web-Interfaces zur Datenverwaltung und auf der Untersuchung relationaler Datenbanksysteme hinsichtlich der Eignung zur Lösung der Aufgabe. Der Prototyp stellt lediglich eine beispielhafte Implementierung einiger Teilkomplexe des Gesamtsystems dar.

Relational database management systems build on the closed world assumption requiring upfront modeling of a usually stable schema. However, a growing number of today's database applications are characterized by self-descriptive data. The schema of self-descriptive data is very dynamic and prone to frequent changes; a situation which is always troublesome to handle in relational systems. This demo presents the relational database management system FRDM. With flexible relational tables FRDM greatly simplifies the management of self-descriptive data in a relational database system. Self-descriptive data can reside directly next to traditionally modeled data and both can be queried together using SQL. This demo presents the various features of FRDM and provides first-hand experience of the newly gained freedom in relational database systems.

The progress in genome research demands for an adequate infrastructure to analyze the data sets. Database systems reflect a key technology to organize data and speed up the analysis process. This paper discusses the role of a relational database system based on the problem of finding frequent substructures in multi-dimensional protein databases. The specific problem consists of producing a set of association rules regarding frequent substructures with different lengths and gaps between the amino acid residues of a protein. From a database point of view, the process of finding association rules building the base for a more in-depth analysis of the data material is split into two parts. The first part performs a discretization of the conformational angle space of a single amino acid residue by computing the nearest neighbor of a given set of representatives. The second part consists in adapting a well-known association rule algorithm to determine the frequent substructures. Both steps within this comprehensive analysis task requires substantial support of the underlying database in order to reduce the programming overhead at the application level.

In the area of online analytical processing (OLAP), the concept of multidimensional databases is receiving much popularity. Thus, a couple of different multidimensional data models were proposed from the research as well as from the commercial product side, each emphasizing different perspectives. However, very little work has been done investigating guidelines for good schema design within such a multidimensional data model. Based on a logical reconstruction of multidimensional schema design, this paper proposes two multidimensional normal forms. These normal forms define modeling constraints for summary attributes describing the cells within a multidimensional data cube and constraints to model complex dimensional structures appropriately. Multidimensional schemas compliant to these normal forms do not only ensure the validity of analytical computations on the multidimensional database, but also favor an efficient physical database design.

In smart item environments, multitude of sensors are applied to capture data about product conditions and usage to guide business decisions as well as production automation processes. A big issue in this application area is posed by the restricted quality of sensor data due to limited sensor precision as well as sensor failures and malfunctions. Decisions derived on incorrect or misleading sensor data are likely to be faulty. The issue of how to efficiently provide applications with information about data quality (DQ) is still an open research problem. In this paper, we present a flexible model for the efficient transfer and management of data quality for streaming as well as static data. We propose a data stream metamodel to allow for the propagation of data quality from the sensors up to the respective business application without a significant overhead of data. Furthermore, we present the extension of the traditional RDBMS metamodel to permit the persistent storage of data quality information in a relational database. Finally, we demonstrate a data quality metadata mapping to close the gap between the streaming environment and the target database. Our solution maintains a flexible number of DQ dimensions and supports applications directly consuming streaming data or processing data filed in a persistent database.

Modern Web or ¿Eternal-Beta¿ applications necessitate a flexible and easy-to-use data management platform that allows the evolutionary development of databases and applications. The classical approach of relational database systems following strictly the ACID properties has to be extended by an extensible and easy-to-use persistency layer with specialized DB features. Using the underlying concept of Software as a Service (SaaS) also enables an economic advantage based on the ¿economy of the scale¿, where application and system environments only need to be provided once but can be used by thousands of users. Within this tutorial, we are looking at the current state-of-the-art from different perspectives. We outline foundations and techniques to build database services based on the SaaS-paradigm. We discuss requirements from a programming perspective, show different dimensions in the context of consistency and reliability, and also describe different non-functional properties under the umbrella of Service-Level agreements (SLA).