Bibliography | Rantzau, Ralf; Schwarz, Holger: A Multi-Tier Architecture for High-Performance Data Mining. In: Buchmann, A. P. (ed.): Proceedings of the Conference Datenbanksysteme in Büro, Technik und Wissenschaft (BTW 1999), Freiburg, Germany, March 1999. University of Stuttgart, Faculty of Computer Science. Informatik aktuell, pp. 151-163, english. Berlin, Heidelberg, New York: Springer, March 1999. ISBN: 3-540-65606-5. Article in Proceedings (Conference Paper).
|
Corporation | 8. GI-Fachtagung BTW 1999 |
CR-Schema | H.2.8 (Database Applications)
|
Abstract | 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.
|
Full text and other links | PDF (135443 Bytes) PostScript (275472 Bytes) HTML (generated from PostScript)
|
Contact | rrantzau@acm.org |
Department(s) | University of Stuttgart, Institute of Parallel and Distributed High-Performance Systems, Applications of Parallel and Distributed Systems
|
Project(s) | CRITIKAL
|
Entry date | November 29, 2000 |
---|