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Web Usage Data Cleaning

A Rule-Based Approach for Weblog Data Cleaning

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Big Data Analytics and Knowledge Discovery (DaWaK 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11031))

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Abstract

This paper addresses the issue of Weblog Data cleaning within the scope of Web Usage Mining. Weblog data are information on end-user clicks and underlying user-agent hits recorded by webservers. Since Web Usage Mining is interested in end-user behavior, user-agent hits are referred to as noise to be cleaned before mining. The most referenced and implemented cleaning methods are the conventional and advanced cleaning. They are content-centric filtering heuristics, based on the requested resource attribute of the weblog database. These cleaning methods are limited in terms of relevancy, workability and cost constraints, within the context of dynamic and responsive web. In order to deal with dynamic and responsive web constraints, this contribution introduces a rule-based cleaning method focused on the logging structure rules. The rule-based cleaning method experimentation demonstrates significant advantages compared to the content-centric methods.

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Author information

Authors and Affiliations

  1. University of Vincennes in Saint-Denis, 2 Rue de la Liberté, 93526, Saint-Denis, France

    Amine Ganibardi & Chérif Arab Ali

Authors
  1. Amine Ganibardi
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  2. Chérif Arab Ali
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Corresponding author

Correspondence to Amine Ganibardi .

Editor information

Editors and Affiliations

  1. University of Houston, Houston, Texas, USA

    Carlos Ordonez

  2. LIAS/ISAE-ENSMA, Chasseneuil-du-Poitou, France

    Ladjel Bellatreche

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Ganibardi, A., Ali, C.A. (2018). Web Usage Data Cleaning. In: Ordonez, C., Bellatreche, L. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2018. Lecture Notes in Computer Science(), vol 11031. Springer, Cham. https://doi.org/10.1007/978-3-319-98539-8_15

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  • DOI: https://doi.org/10.1007/978-3-319-98539-8_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98538-1

  • Online ISBN: 978-3-319-98539-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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