Artikel in Tagungsband INPROC-1999-01

Rantzau, Ralf; Schwarz, Holger: A Multi-Tier Architecture for High-Performance Data Mining.
In: Buchmann, A. P. (Hrsg): Proceedings of the Conference Datenbanksysteme in Büro, Technik und Wissenschaft (BTW 1999), Freiburg, Germany, March 1999.
Universität Stuttgart, Fakultät Informatik.
Informatik aktuell, S. 151-163, englisch.
Berlin, Heidelberg, New York: Springer, März 1999.
ISBN: 3-540-65606-5.
Artikel in Tagungsband (Konferenz-Beitrag).
Körperschaft8. GI-Fachtagung BTW 1999
CR-Klassif.H.2.8 (Database Applications)

Data mining has been recognised as an essential element of decision support, which has increasingly become a focus of the database industry. Like all computationally expensive data analysis applications, for example Online Analytical Processing (OLAP), performance is a key factor for usefulness and acceptance in business. In the course of the CRITIKAL project (Client-Server Rule Induction Technology for Industrial Knowledge Acquisition from Large Databases), which is funded by the European Commission, several kinds of architectures for data mining were evaluated with a strong focus on high performance. Specifically, the data mining techniques association rule discovery and decision tree induction were implemented into a prototype. We present the architecture developed by the CRITIKAL consortium and compare it to alternative architectures.

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Abteilung(en)Universität Stuttgart, Institut für Parallele und Verteilte Höchstleistungsrechner, Anwendersoftware
Eingabedatum29. November 2000
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