Abstract
Database systems and knowledge discovery tools are two key technologies for storing, querying, and mining large volumes of data available today. The number of people and organizations that use data analysis techniques in their daily activities is increasing significantly and in several application domains the need of processing and mining large data sets is becoming a standard task. However, these intensive data consuming applications suffer from performance problems and single database sources. Introducing data distribution and parallel processing help to overcome resource bottlenecks and to achieve guaranteed throughput, quality of service, and system scalability. High-performance computers supported by high speed networks and intelligent data management middleware offer parallel and distributed databases and knowledge discovery systems a great opportunity to support cost-effective every day applications.
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© 2003 Springer-Verlag Berlin Heidelberg
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Mitschang, B., Skillicorn, D., Bonnet, P., Talia, D. (2003). Topic 5 Parallel and Distributed Databases, Data Mining, and Knowledge Discovery. In: Kosch, H., Böszörményi, L., Hellwagner, H. (eds) Euro-Par 2003 Parallel Processing. Euro-Par 2003. Lecture Notes in Computer Science, vol 2790. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45209-6_46
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DOI: https://doi.org/10.1007/978-3-540-45209-6_46
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