Shared Disk database systems offer a high flexibility for parallel transaction and query processing. This is because each node can process any transaction, query or subquery because it has access to the entire database. Compared to Shared Nothing database systems, this is particularly advantageous for scan queries for which the degree of intra-query parallelism as well as the scan processors themselves can dynamically be chosen. On the other hand, there is the danger of disk contention between subqueries, in particular for index scans. We present a detailed simulation study to analyze the effectiveness of parallel scan processing in Shared Disk database systems. In particular, we investigate the relationship between the degree of declustering and the degree of scan parallelism for relation scans, clustered index scans, and non-clustered index scans. Furthermore, we study the usefulness of disk caches and prefetching for limiting disk contention. Finally, we show that disk contention in multi-user mode can be limited for Shared Disk database systems by dynamically choosing the degree of scan parallelism.

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Die effiziente Nutzung von Zugriffsstrukturen ist eine wichtige Voraussetzung für die performante Durchführung von Datenbankanfragen. Die in Parallelen Datenbanksystemen vom Typ Shared-Nothing übliche, durch die Allokationsstrategie für Relationen weitgehend vorgegebene Indexallokation führt oftmals zu unnötigen I/O-, Verarbeitungs- und Kommunikationskosten. Parallele Shared-Disk Datenbanksysteme bieten durch ihren gemeinsamen Plattenzugriff ein hohes Potential zur flexiblen Allokation von Indexstrukturen. Wir präsentieren eine Klassifikation und eine qualitative Bewertung von Indexallokations-Strategien für diese Architekturklasse, die zeigt, daß sich durch die flexible Wahl von Größen wie Verteilattribut und Verteilgrad die Performanz der parallelen Indexverarbeitung steigern läßt.

Data allocation is a key performance factor for parallel database systems (PDBS). This holds especially for data warehousing environments where huge amounts of data and complex analytical queries have to be dealt with. While there are several studies on data allocation for relational PDBS, the specific requirements of data warehouses have not yet been sufficiently addressed. In this study, we consider the allocation of relational data warehouses based on a star schema and utilizing bitmap index structures. We investigate how a multi-dimensional hierarchical data fragmentation of the fact table supports queries referencing different subsets of the schema dimensions. Our analysis is based on realistic parameters derived from a decision support benchmark. The performance implications of different allocation choices are evaluated by means of a detailed simulation model.

We present the Warlock tool to automatically determine a parallel data warehouse’s allocation to disk. This GUI-equipped tool is implemented in Java and utilizes an internal cost model and heuristics to determine a disk allocation minimizing both I/O work and query response times. Warlock recommends a ranked list of fragmentation candidates, a detailed query performance analysis and a tailored physical allocation scheme for relational star schemas and bitmap indexes. It supports multi-dimensional fragmentations and can deal with data skew for parallel data warehouses based on a Shared Everything or Shared Disk architecture.

No description available.

Anhand eines konkreten Einsatzbeispieles (Vorlesungsverzeichnis SS '971 des Instituts für Informatik der Universität Leipzig) wird ein auf einer objektorientierten Datenbank und SGML basierendes System für elektronisches Publizieren vorgestellt. Unter Verwendung einer einfachen Substitutionssprache lassen sich Layout-Informationen für SGML-Dokumente kodieren, die in mehrerenFormaten (z.B. HTML, LaTeX, ASCII) ausgegeben werden können. Passwortgeschützte Modifizierung von Dokumenten erfolgt direkt im Web. Dank Implementierung in Java ist das System äußerst portabel.

Data warehouse queries pose challenging performance problems that often necessitate the use of parallel database systems (PDBS). Although dynamic load balancing is of key importance in PDBS, to our knowledge it has not yet been investigated thoroughly for parallel data warehouses. In this study, we propose a scheduling strategy that simultaneously considers both processors and disks while utilizing the load balancing potential of a Shared Disk architecture. We compare the performance of this new method to several other approaches in a comprehensive simulation study, incorporating skew aspects and typical data warehouse features such as star schemas.

Ontology matching has been widely studied. However, the resulting on-tology mappings can be rather unstable when the participating ontologies or util-ized secondary sources (e.g., instance sources, thesauri) evolve. We propose an evolution-based approach for assessing ontology mappings by annotating their cor-respondences by information about similarity values for past ontology versions. These annotations allow us to assess the stability of correspondences over time and they can thus be used to determine better and more robust ontology mappings. The approach is generic in that it can be applied independently from the utilized match technique. We define different stability measures and show results of a first evaluation for the life science domain.

Parallel database systems have to support the effective parallelization of complex queries in multi-user mode, i.e. in combination with inter-query/inter-transaction parallelism. For this purpose, dynamic scheduling and load balancing strategies are necessary that consider the current system state for determining the degree of intra-query parallelism and for selecting the processors for executing subqueries. We study these issues for parallel hash join processing and show that the two subproblems should be addressed in an integrated way. Even more importantly, however, is the use of a multiresource load balancing approach that considers all potential bottleneck resources, in particular memory, disk and CPU. We discuss basic performance tradeoffs to consider and evaluate the performance of several load balancing strategies by means of a detailed simulation model. Simulation results will be analyzed for multi-user configurations with both homogeneous and heterogeneous (query/OLTP) workloads.