Multi-level Data Mining - One way to sophisticated Web-based applications

Hildegard Rumetshofer; Rainer Dlapka

Multi-level Data Mining - One way to sophisticated Web-based applications

Hildegard Rumetshofer
, Rainer Dlapka

Institut für Anwendungsorientierte Wissensverarbeitung

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

Abstract

With the possibility of world wide access current Web applications have an almost unlimited amount of potential users. Methods of data mining can be used to classify the user groups and to learn from their behavior. With the collected information changes and optimizations can be applied to the Web application to hit the users requirements. One already established method for data mining is Web usage mining. Web usage mining is based on the logging information that is generated by the Web server when the user clicks through the site. A new approach that offers more flexibility as presented in this paper is multi-level data mining. It takes advantage of the several layers in a Web application. Guidelines that may be regarded as good practice in the field of data tracking will be introduced in this paper.

Originalsprache	Englisch
Titel	The Sixth Int. Conf. on Information Integration and Web-based Applications and Services, iiWAS 2004
Seitenumfang	10
Publikationsstatus	Veröffentlicht - Sep. 2004

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

SDG 9 – Industrie, Innovation und Infrastruktur
SDG 16 – Frieden, Gerechtigkeit und starke Institutionen

Wissenschaftszweige

102001 Artificial Intelligence
102006 Computer Supported Cooperative Work (CSCW)
102010 Datenbanksysteme
102014 Informationsdesign
102015 Informationssysteme
102016 IT-Sicherheit
102028 Knowledge Engineering
102019 Machine Learning
102022 Softwareentwicklung
102025 Verteilte Systeme
502007 E-Commerce
505002 Datenschutz
506002 E-Government
509018 Wissensmanagement
202007 Computer Integrated Manufacturing (CIM)
102033 Data Mining
102035 Data Science

Andere Dateien und Links

FAW-Publikation 2004, Volltext

Dieses zitieren

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