Conceptualizing Mining of Firm’s Web Log Files

Trakunphutthirak Ruangsak (), Cheung Yen () and Lee Vincent C. S. ()
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Trakunphutthirak Ruangsak: Faculty of IT, Clayton Campus, Monash University, 25 Exhibition Walk, Clayton, Australia
Cheung Yen: Faculty of IT, Clayton Campus, Monash University, 25 Exhibition Walk, Clayton, Australia
Lee Vincent C. S.: Faculty of IT, Clayton Campus, Monash University, 25 Exhibition Walk, Clayton, Australia

Journal of Systems Science and Information, 2017, vol. 5, issue 6, 489-510

Abstract: In this era of a data-driven society, useful data (Big Data) is often unintentionally ignored due to lack of convenient tools and expensive software. For example, web log files can be used to identify explicit information of browsing patterns when users access web sites. Some hidden information, however, cannot be directly derived from the log files. We may need external resources to discover more knowledge from browsing patterns. The purpose of this study is to investigate the application of web usage mining based on web log files. The outcome of this study sets further directions of this investigation on what and how implicit information embedded in log files can be efficiently and effectively extracted. Further work involves combining the use of social media data to improve business decision quality.

Keywords: web usage mining; web log files; Big Data; machine learning; business intelligence (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:jossai:v:5:y:2017:i:6:p:489-510:n:1

DOI: 10.21078/JSSI-2017-489-22

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