Online and incremental mining of separately-grouped web access logs

The rising popularity of electronic commerce makes data mining an indispensable technology for business competitiveness. The World Wide Web provides abundant raw data in the form of web access logs, web transaction logs and web user profiles. Without data mining tools, it is impossible to make any s...

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Main Authors: WOON, Yew-Kwong, NG, Wee-Keong, LIM, Ee Peng
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2002
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Online Access:https://ink.library.smu.edu.sg/sis_research/1016
http://doi.ieeecomputersociety.org/10.1109/WISE.2002.1181643
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-20152018-06-20T05:38:37Z Online and incremental mining of separately-grouped web access logs WOON, Yew-Kwong NG, Wee-Keong LIM, Ee Peng The rising popularity of electronic commerce makes data mining an indispensable technology for business competitiveness. The World Wide Web provides abundant raw data in the form of web access logs, web transaction logs and web user profiles. Without data mining tools, it is impossible to make any sense of such massive data. In this paper, we focus on web usage mining because it deals most appropriately with understanding user behavioral patterns which is the key to successful customer relationship management. Previous work deals separately on specific issues of web usage mining and make assumptions without taking a holistic view and thus, have limited practical applicability. We formulate a novel and more holistic version of web usage min-ing termed TRAnsactionized LOgfile Mining (TRALOM) to effectively and correctly identify transactions as well as to mine useful knowledge from web access logs. We also introduce a new data structure, called the Webrie, to efficiently hold useful preprocessed data so that TRALOM can be done in an online and incremental fashion. Experiments conducted on real web server logs verify the usefulness and practicality of our proposed techniques. 2002-12-01T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/1016 info:doi/10.1109/WISE.2002.1181643 http://doi.ieeecomputersociety.org/10.1109/WISE.2002.1181643 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle Databases and Information Systems
Numerical Analysis and Scientific Computing
WOON, Yew-Kwong
NG, Wee-Keong
LIM, Ee Peng
Online and incremental mining of separately-grouped web access logs
description The rising popularity of electronic commerce makes data mining an indispensable technology for business competitiveness. The World Wide Web provides abundant raw data in the form of web access logs, web transaction logs and web user profiles. Without data mining tools, it is impossible to make any sense of such massive data. In this paper, we focus on web usage mining because it deals most appropriately with understanding user behavioral patterns which is the key to successful customer relationship management. Previous work deals separately on specific issues of web usage mining and make assumptions without taking a holistic view and thus, have limited practical applicability. We formulate a novel and more holistic version of web usage min-ing termed TRAnsactionized LOgfile Mining (TRALOM) to effectively and correctly identify transactions as well as to mine useful knowledge from web access logs. We also introduce a new data structure, called the Webrie, to efficiently hold useful preprocessed data so that TRALOM can be done in an online and incremental fashion. Experiments conducted on real web server logs verify the usefulness and practicality of our proposed techniques.
format text
author WOON, Yew-Kwong
NG, Wee-Keong
LIM, Ee Peng
author_facet WOON, Yew-Kwong
NG, Wee-Keong
LIM, Ee Peng
author_sort WOON, Yew-Kwong
title Online and incremental mining of separately-grouped web access logs
title_short Online and incremental mining of separately-grouped web access logs
title_full Online and incremental mining of separately-grouped web access logs
title_fullStr Online and incremental mining of separately-grouped web access logs
title_full_unstemmed Online and incremental mining of separately-grouped web access logs
title_sort online and incremental mining of separately-grouped web access logs
publisher Institutional Knowledge at Singapore Management University
publishDate 2002
url https://ink.library.smu.edu.sg/sis_research/1016
http://doi.ieeecomputersociety.org/10.1109/WISE.2002.1181643
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