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.

Skew effects are still a significant problem for efficient query processing in parallel database systems. Especially in shared-nothing environments, this problem is aggravated by the substantial cost of data redistribution. Shared-disk systems, on the other hand, promise much higher flexibility in the distribution of workload among processing nodes because all input data can be accessed by any node at equal cost. In order to verify this potential for dynamic load balancing, we have devised a new technique for skew-tolerant join processing. In contrast to conventional solutions, our algorithm is not restricted to estimating processing costs in advance and assigning tasks to nodes accordingly. Instead, it monitors the actual progression of work and dynamically allocates tasks to processors, thus capitalizing on the uniform access pathlength in shared-disk architectures. This approach has the potential to alleviate not only any kind of data-inherent skew, but also execution skew caused by query- external workloads, by disk contention, or simply by inaccurate estimates used in predictive scheduling. We employ a detailed simulation system to evaluate the new algorithm under different types and degrees of skew.

Thesis, (Ph. D.)--Oregon Graduate Institute of Science and Technology, 1995.

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