Article

Free access

Web mining in search engines

Author:

Ricardo Baeza-YatesAuthors Info & Claims

ACSC '04: Proceedings of the 27th Australasian conference on Computer science - Volume 26

Pages 3 - 4

Published: 01 January 2004 Publication History

PDF eReader

Abstract

Given the rate of growth of the Web, scalability of search engines is a key issue, as the amount of hardware and network resources needed is large, and expensive. In addition, search engines are popular tools, so they have heavy constraints on query answer time. So, the efficient use of resources can improve both scalability and answer time. One tool to achieve these goals is Web mining. Web mining has three branches: link mining, usage mining, and content mining. One important analysis in all these cases is the dynamic behavior. Here we give examples of link and usage mining related to search engines, as well as the related Web dynamics.

References

[1]

Ricardo Baeza-Yates and Carlos Castillo, Relating Web Characteristics with Link Based Web Page Raking, In Proceedings of SPIRE 2001, IEEE CS Press, Laguna San Rafael, Chile, pp. 21--32, 2001.

Google Scholar

[2]

Baeza-Yates, Felipe Saint-Jean, and Carlos Castillo. Web Dynamics, Age and Page Quality, In Proceedings of SPIRE 2002, LNCS, Springer, Lisbon, Portugal, 2002.

Digital Library

Google Scholar

[3]

Ricardo Baeza-Yates, and Felipe Saint-Jean. A Three Level Search Engine Index based in Query Log Distribution. SPIRE 2003, LNCS, Springer, Manaus, 2003.

Crossref

Google Scholar

[4]

Ricardo Baeza-Yates, and Barbara Poblete. Evolution of the Chilean Web Structure Composition. In First Latin-American Web Congress, Santiago, Chile, IEEE CS Press, 2003.

Digital Library

Google Scholar

[5]

Ricardo Baeza-Yates. Query Usage Mining in Search Engines. In Web Mining: Applications and Techniques, Anthony Scime, editor. Idea Group, 2004.

Digital Library

Google Scholar

[6]

Andrei Broder, Ravi, Kumar, Farzin Maghoul, Prabakhar Raghavan, Sridhar Rajagopalan, Raymie Stata, Andrew Tomkins, and Janet Wiener. Graph structure in the Web: Experiments and models. In 9th World Wide Web Conference, Amsterdam, 2000.

Digital Library

Google Scholar

[7]

Soumen Chakrabarti, Mining the Web: Discovering Knowledge from Hypertext Data. Morgan Kaufmann Publishers, San Francisco, CA, USA, 2002.

Digital Library

Google Scholar

[8]

Jon Kleinberg. Authoritative sources in a hyperlinked environment. Proc. 9th Symposium on Discrete Algorithms, 1998.

Digital Library

Google Scholar

[9]

Mark Levene and Alex Poulovassilis, editors. Web Dynamics, Springer, 2003.

Digital Library

Google Scholar

[10]

Larry Page, Sergei Brin, Rajeev Motwani, and Terry Winograd. The Pagerank citation algorithm: bringing order to the Web. Tech. rep., Dept. of Computer Science, Stanford University, 1999.

Google Scholar

[11]

Peter Pirolli. Computational Models of Information Scent-Following in a Very Large Browsable Text Collection, In Human Factors in Computing Systems: Proceedings of the CHI '97 Conference. ACM Press, New York, 3--10, 1997.

Digital Library

Google Scholar

[12]

Dell Zhang, and Yisheng Dong. A Novel Web Usage Mining Approach For Search Engine. Computer Networks 39(3): 303--310, 2002.

Google Scholar

Cited By

View all

Jiang DPei JLi H(2013)Mining search and browse logs for web searchACM Transactions on Intelligent Systems and Technology10.1145/2508037.25080384:4(1-37)Online publication date: 8-Oct-2013
https://dl.acm.org/doi/10.1145/2508037.2508038
Lu JCallan JEfthimiadis EDumais SHawking DJärvelin, K(2006)User modeling for full-text federated search in peer-to-peer networksProceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval10.1145/1148170.1148229(332-339)Online publication date: 6-Aug-2006
https://dl.acm.org/doi/10.1145/1148170.1148229
Dasgupta AKumar RRaghavan PTomkins AGrossman RBayardo RBennett K(2005)Variable latent semantic indexingProceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining10.1145/1081870.1081876(13-21)Online publication date: 21-Aug-2005
https://dl.acm.org/doi/10.1145/1081870.1081876
Show More Cited By

Index Terms

Web mining in search engines
1. Information systems

Recommendations

Web dynamics and their ramifications for the development of web search engines
Web dynamics

The World Wide Web has become the largest hypertext system in existence, providing an extremely rich collection of information resources. Compared with conventional information sources, the Web is highly dynamic in the following four factors: size (i.e.,...
Web Search Engines: Part 1

A behind-the-scenes look explores the data processing "miracle" that characterizes Web crawling and searching.
Mining test oracles of web search engines
ASE '11: Proceedings of the 26th IEEE/ACM International Conference on Automated Software Engineering

Web search engines have major impact in people's everyday life. It is of great importance to test the retrieval effectiveness of search engines. However, it is labor-intensive to judge the relevance of search results for a large number of queries, and ...

Comments

Information & Contributors

Information

Published In

ACSC '04: Proceedings of the 27th Australasian conference on Computer science - Volume 26

January 2004

367 pages

Editor:
Estivill-Castro

Publisher

Australian Computer Society, Inc.

Australia

Publication History

Published: 01 January 2004

Author Tags

Qualifiers

Article

Conference

ACSC '04

ACSC '04: Computer science

01 01 2004

Dunedin, New Zealand

Acceptance Rates

Overall Acceptance Rate 136 of 379 submissions, 36%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
1,709
Total Downloads

Downloads (Last 12 months)60
Downloads (Last 6 weeks)13

Reflects downloads up to 19 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Jiang DPei JLi H(2013)Mining search and browse logs for web searchACM Transactions on Intelligent Systems and Technology10.1145/2508037.25080384:4(1-37)Online publication date: 8-Oct-2013
https://dl.acm.org/doi/10.1145/2508037.2508038
Lu JCallan JEfthimiadis EDumais SHawking DJärvelin, K(2006)User modeling for full-text federated search in peer-to-peer networksProceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval10.1145/1148170.1148229(332-339)Online publication date: 6-Aug-2006
https://dl.acm.org/doi/10.1145/1148170.1148229
Dasgupta AKumar RRaghavan PTomkins AGrossman RBayardo RBennett K(2005)Variable latent semantic indexingProceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining10.1145/1081870.1081876(13-21)Online publication date: 21-Aug-2005
https://dl.acm.org/doi/10.1145/1081870.1081876
Baeza-Yates R(2005)Applications of web query miningProceedings of the 27th European conference on Advances in Information Retrieval Research10.1007/978-3-540-31865-1_2(7-22)Online publication date: 21-Mar-2005
https://dl.acm.org/doi/10.1007/978-3-540-31865-1_2
Baeza-Yates RSalinger A(2005)Experimental analysis of a fast intersection algorithm for sorted sequencesProceedings of the 12th international conference on String Processing and Information Retrieval10.1007/11575832_2(13-24)Online publication date: 2-Nov-2005
https://dl.acm.org/doi/10.1007/11575832_2
Baeza-Yates R(2004)Web mining in search enginesProceedings of the 27th Australasian conference on Computer science - Volume 2610.5555/979922.979923(3-4)Online publication date: 1-Jan-2004
https://dl.acm.org/doi/10.5555/979922.979923

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

References

Cited By

Index Terms

Recommendations

Web dynamics and their ramifications for the development of web search engines

Web Search Engines: Part 1

Mining test oracles of web search engines

Comments

Information

Published In

Publisher

Publication History

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

PDF

eReader

Login options

Full Access

Share

Share this Publication link

Share on social media

Affiliations