Titre
Extreme Datamining
Type
article de conférence/colloque
Institution
Externe
Série
Studies in Classification, Data Analysis, and Knowledge Organization
Auteur(s)
Chavez-Demoulin, V.
Auteure/Auteur
Jarvis, S.
Auteure/Auteur
Perera, R.
Auteure/Auteur
Roehrl, A.
Auteure/Auteur
Schmiedl, S.
Auteure/Auteur
Sondergaard, M. P.
Auteure/Auteur
Éditeur(s)
Schader, M.
Gaul, W.
Vichi, M.
Liens vers les personnes
Maison d’édition
Springer
Titre du livre ou conférence/colloque
Between Data Science and Applied Data Analysis - Proceedings of the 26th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Mannheim, July 2002
Adresse
Mannheim, Germany
ISBN
978-3-540-40354-8
Statut éditorial
Publié
Date de publication
2003
Première page
387
Dernière page/numéro d’article
394
Peer-reviewed
Oui
Langue
anglais
Résumé
In recent years there have been a number of developments in the datamining techniques used in the analysis of terrabyte-sized logfiles resulting from Internet-based applications. The information which these datamining techniques provide allow knowledge engineers to rapidly direct business decisions. Current datamining methods however, are generally efficient only in the cases when the information obtained in the logfiles is close to the average. This means that in cases where non-standard logfiles (extreme data) are being studied, these methods provide unrealistic and erroneous results. Non-standard logfiles often have a large bearing on the analysis of web applications, the information which they provide can impact on new or even well established services. In this paper a recent Extreme Value Theory methodology is applied as a unique toolkit to describe, understand and predict the non-standard fluctuations as discovered in real-life Internet-sourced log data.
PID Serval
serval:BIB_266CF8230A3B
Date de création
2011-08-23T07:53:31.089Z
Date de création dans IRIS
2025-05-20T13:58:17Z