891 research outputs found
improving query performance using distributed computing
Data warehouses are used to store large amounts of data. This data is often
used for On-Line Analytical Processing (OLAP) where short response times are
essential for on-line decision support. One of the most important requirements
of a data warehouse server is the query performance. The principal aspect from
the user perspective is how quickly the server processes a given query: “the
data warehouse must be fast”. The main focus of our research is finding
adequate solutions to improve query response time of typical OLAP queries and
improve scalability using a distributed computation environment that takes
advantage of characteristics specific to the OLAP context. Our proposal
provides very good performance and scalability even on huge data warehouses
A distributed Linda server on a network of heterogeneous processors
Linda is an approach to parallelism which relies on a virtual associative shared memory called tuple space. Tuple space is accessed through a small set of primitive operations and is conceptually easy to understand and manipulate. The physical implementation of a Linda tuple space may of course be completely different from the conceptual model. Rhodes has implemented versions of Linda on a ring of RS-232 joined PC's and on a cluster of T800 transputers with a single copy of tuple space on one transputer. Current research targets the implementation of a distributed Linda server on a network of heterogeneous processors. This work describes the design and implementation of a distributed Linda server. Emphasis is placed on aspects of the design which enhance portability and efficiency
Reasoning-Supported Quality Assurance for Knowledge Bases
The increasing application of ontology reuse and automated knowledge acquisition tools in ontology engineering brings about a shift of development efforts from knowledge modeling towards quality assurance. Despite the high practical importance, there has been a substantial lack of support for ensuring semantic accuracy and conciseness. In this thesis, we make a significant step forward in ontology engineering by developing a support for two such essential quality assurance activities
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