Bibliograph. Daten | 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).
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Körperschaft | 8. GI-Fachtagung BTW 1999 |
CR-Klassif. | H.2.8 (Database Applications)
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Kurzfassung | 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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Volltext und andere Links | PDF (135443 Bytes) PostScript (275472 Bytes) HTML (aus PostScript generiert)
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Kontakt | rrantzau@acm.org |
Abteilung(en) | Universität Stuttgart, Institut für Parallele und Verteilte Höchstleistungsrechner, Anwendersoftware
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Projekt(e) | CRITIKAL
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Eingabedatum | 29. November 2000 |
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