ABS: The Anti Bouncing Model for Usage Data Streams

Chongsheng Zhang 1 Florent Masseglia 1, 2 Yves Lechevallier 1
1 AxIS - Usage-centered design, analysis and improvement of information systems
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Paris-Rocquencourt
2 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Usage data mining is an important research area with applications in various fields. However, usage data is usually considered streaming, due to its high volumes and rates. Because of these characteristics, we only have access, at any point in time, to a small fraction of the stream. When the data is observed through such a limited window, it is challenging to give a reliable description of the recent usage data. We study the important consequences of these constraints, through the "bounce rate" problem and the clustering of usage data streams. Then, we propose the ABS (Anti-Bouncing Stream) model which combines the advantages of previous models but discards their drawbacks. First, under the same resource constraints as existing models in the literature, ABS can better model the recent data. Second, owing to its simple but effective management approach, the data in ABS is available at any time for analysis. We demonstrate its superiority through a theoretical study and experiments on two real-world data sets.
Type de document :
Communication dans un congrès
Geoffrey I. Webb and Bing Liu and Chengqi Zhang and Dimitrios Gunopulos and Xindong Wu. ICDM'10 : The 10th IEEE International Conference on Data Mining, Dec 2010, Sydney, Australia. IEEE, pp.1169-1174, 2010, 〈10.1109/ICDM.2010.91〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00653732
Contributeur : Florent Masseglia <>
Soumis le : mardi 20 décembre 2011 - 10:02:32
Dernière modification le : vendredi 25 mai 2018 - 12:02:04

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Chongsheng Zhang, Florent Masseglia, Yves Lechevallier. ABS: The Anti Bouncing Model for Usage Data Streams. Geoffrey I. Webb and Bing Liu and Chengqi Zhang and Dimitrios Gunopulos and Xindong Wu. ICDM'10 : The 10th IEEE International Conference on Data Mining, Dec 2010, Sydney, Australia. IEEE, pp.1169-1174, 2010, 〈10.1109/ICDM.2010.91〉. 〈lirmm-00653732〉

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