User Identification in the Process of Web Usage Data Preprocessing

Authors

  • Jozef Kapusta Constantine the Philosopher University in Nitra, Tr. A. Hlinku 1, 949 74, Nitra
  • Michal Munk Constantine the Philosopher University in Nitra, Tr. A. Hlinku 1, 949 74, Nitra
  • Dominik Halvoník Constantine the Philosopher University in Nitra, Tr. A. Hlinku 1, 949 74, Nitra
  • Martin Drlík Constantine the Philosopher University in Nitra, Tr. A. Hlinku 1, 949 74, Nitra

DOI:

https://doi.org/10.3991/ijet.v14i09.9854

Keywords:

web usage mining, cookies, session time thresholds, sequence rules

Abstract


If we are talking about user behavior analytics, we have to understand what the main source of valuable information is. One of these sources is definitely a web server. There are multiple places where we can extract the necessary data. The most common ways are to search for these data in access log, error log, custom log files of web server, proxy server log file, web browser log, browser cookies etc. A web server log is in its default form known as a Common Log File (W3C, 1995) and keeps information about IP address; date and time of visit; ac-cessed and referenced resource. There are standardized methodologies which contain several steps leading to extract new knowledge from provided data. Usu-ally, the first step is in each one of them to identify users, users’ sessions, page views, and clickstreams. This process is called pre-processing. Main goal of this stage is to receive unprocessed web server log file as input and after processing outputs meaningful representations which can be used in next phase. In this pa-per, we describe in detail user session identification which can be considered as most important part of data pre-processing. Our paper aims to compare the us-er/session identification using the STT with the identification of user/session us-ing cookies. This comparison was performed concerning the quality of the se-quential rules generated, i.e., a comparison was made regarding generation useful, trivial and inexplicable rules.

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Published

2019-05-14

How to Cite

Kapusta, J., Munk, M., Halvoník, D., & Drlík, M. (2019). User Identification in the Process of Web Usage Data Preprocessing. International Journal of Emerging Technologies in Learning (iJET), 14(09), pp. 21–33. https://doi.org/10.3991/ijet.v14i09.9854

Issue

Vol. 14 No. 09 (2019)

Section

Papers

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