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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한산업공학회 Industrial Engineering & Management Systems Industrial Engineering & Management Systems 제11권 제1호
- 발행연도
- 2012.3
- 수록면
- 82 - 86 (5page)
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초록· 키워드
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In For the past few years, query log data has been collected to find user"s behavior in using the site. Many researches have studied on the usage of query logs to extract user"s preference, recommend personalization, improve caching and pre-fetching of Web objects, build better adaptive user interfaces, and also to improve Web search for a search engine application. A query log contain data such as the client"s IP address, time and date of request, the resources or page requested, status of request HTTP method used and the type of browser and operating system. A query log can offer valuable insight into web site usage. A proper compilation and interpretation of query log can provide a baseline of statistics that indicate the usage levels of website and can be used as tool to assist decision making in management activities.
In this paper we want to discuss on the tasks performed of query logs in pre-processing of web usage mining. We will use query logs from an online newspaper company. The query logs will undergo pre-processing stage, in which the clickstream data is cleaned and partitioned into a set of user interactions which will represent the activities of each user during their visits to the site. The query logs will undergo essential task in pre-processing which are data cleaning and user identification.
In this paper we want to discuss on the tasks performed of query logs in pre-processing of web usage mining. We will use query logs from an online newspaper company. The query logs will undergo pre-processing stage, in which the clickstream data is cleaned and partitioned into a set of user interactions which will represent the activities of each user during their visits to the site. The query logs will undergo essential task in pre-processing which are data cleaning and user identification.
목차
ABSTRACT
1. INTRODUCTION
2. RELATED WORK
3. DATA PREPARATION AND PREPROCESSING
4. RESULTS
5. CONCLUSION
ACKNOWLEDGEMENT
REFERENCES
참고문헌 (18)
참고문헌 신청
1. [학술대회논문] - Batista, P. / 2002 / Mining On-line Newspaper Web Access Logs / Proceedings of the AH’'2002 Workshop on Recommendation and Personalization in eCommerce : 100 ~ 108
2. [학술지(정기간행물)] - 김성희 / 200210 / A personalized recommender system based on web usage mining and decision tree induction / EXPERT SYSTEMS WITH APPLICATIONS 23 (3) : 329 ~ 342
3. [학술지(정기간행물)] - Cooley, R. / 1999 / Data Preparation for Mining World Wide Web Browsing Patterns / Knowledge and Information Systems 1 (1) : 5 ~ 32
4. [학술지(정기간행물)] - Dixit, D. / 2010 / Automatic Recommendation for Online Users Using Web Usage Mining / International Journal of Managing Information Technology 2 : 33 ~ 42
5. [학술대회논문] - Elsheikh, S. / 2008 / Web Usage Data for Web Access Control (WUDWAC) / Proceedings of the World Congress on Engineering
2. [학술지(정기간행물)] - 김성희 / 200210 / A personalized recommender system based on web usage mining and decision tree induction / EXPERT SYSTEMS WITH APPLICATIONS 23 (3) : 329 ~ 342
3. [학술지(정기간행물)] - Cooley, R. / 1999 / Data Preparation for Mining World Wide Web Browsing Patterns / Knowledge and Information Systems 1 (1) : 5 ~ 32
4. [학술지(정기간행물)] - Dixit, D. / 2010 / Automatic Recommendation for Online Users Using Web Usage Mining / International Journal of Managing Information Technology 2 : 33 ~ 42
5. [학술대회논문] - Elsheikh, S. / 2008 / Web Usage Data for Web Access Control (WUDWAC) / Proceedings of the World Congress on Engineering
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UCI(KEPA) : I410-ECN-0101-2013-530-003612595