The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Estimating frequency of change
Abstract
Many online data sources are updated autonomously and independently. In this article, we make the case for estimating the change frequency of data to improve Web crawlers, Web caches and to help data mining. We first identify various scenarios, where different applications have different requirements on the accuracy of the estimated frequency. Then we develop several "frequency estimators" for the identified scenarios, showing analytically and experimentally how precise they are. In many cases, our proposed estimators predict change frequencies much more accurately and improve the effectiveness of applications. For example, a Web crawler could achieve 35% improvement in "freshness" simply by adopting our proposed estimator.
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Overall, the results show that the web is constantly changing mainly due to the creation of more and more new pages and, to a lesser extent, the modification of the content of existing ones.
“Overall, the results show that the web is constantly changing mainly due to the creation of more and more new pages and, to a lesser extent, the modification of the content of existing ones.”
From Survey of Temporal Information Retrieval and Related Applications (2015) · cites this paper as Cho and Garcia-Molina 2003
Citations
Cites
Bernardo, J. M., & Smith, A. F. M. (1994) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316870 · Cited by 2,178
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Research works printed but not linked: 23
- Baentsch M., 1997, World Wide Web caching: The application-level view of the internet, IEEE Commun., 35, 6, 10.1109/35.587725 · DOI
- Brewington B. E., Proceedings of the 9th World-Wide Web Conference.]]
- 10.1109/2.841784 · DOI
- Canavos G., 1972, A Bayesian approach to parameter and reliability estimation in the Poisson distribution, IEEE Trans. Reliab., 21, 52, 10.1109/TR.1972.5216172 · DOI
- Cho J., Proceedings of the 26th International Conference on Very Large Databases.]]
- Cho J., 2009, Proceedings of the 2000 ACM SIGMOD ACM
- Coffman Jr., E., 1998, Optimal robot scheduling for web search engines, J. Sched., 1, 1
- Courant R. and David H. 1989. Methods of mathematical physics 1st ed. Wiley New York.]] Courant R. and David H. 1989. Methods of mathematical physics 1st ed. Wiley New York.]] · DOI
- Douglis F., USENIX Symposium on Internetworking Technologies and Systems.]]
- Edwards J., 1920, Proceedings of the 10th World-Wide Web Conference
- Gwertzman J., Proceedings of USENIX 1996 Annual Technical Conference.]]
- Hammer J., 1995, The Stanford data warehousing project, IEEE Data Eng. Bull., 18, 2
- Harinarayan V., Proceedings of the 1996 ACM SIGMOD. ACM
- Lee P. M., 1997, Bayesian Statistics: An Introduction
- Misra P., 1975, Parameter estimation in Poisson processes, IEEE Trans. Inf. Theory IT-21, 87--90.]]
- Snyder D. L. 1975. Random Point Processes. Wiley New York.]] Snyder D. L. 1975. Random Point Processes. Wiley New York.]]
- Taylor H. M. and Karlin S. 1998. An Introduction to Stochastic Modeling 3rd ed. Academic Press Orlando Fla.]] Taylor H. M. and Karlin S. 1998. An Introduction to Stochastic Modeling 3rd ed. Academic Press Orlando Fla.]]
- Thomas Jr. G. B. 1969. Calculus and Analytic Geometry 4th ed. Addison-Wesley Reading Mass.]] Thomas Jr. G. B. 1969. Calculus and Analytic Geometry 4th ed. Addison-Wesley Reading Mass.]]
- Wills C. E., Proceedings of the 8th World-Wide Web Conference.]]
- Wolman A., 1915, Proceedings of the 17th ACM Symposium on Operating Systems Principles (SOSP '99)
- Yacout S., Proceedings of the 19th International Conference on Computers and Industrial Engineering.]]
- Yu H., Proceedings of the 1999 ACM SIGCOMM ACM
- Zhuge Y., Proceedings of the 1995 ACM SIGMOD ACM
Cited by
Sorted by citations
Survey of Temporal Information Retrieval and Related Applications
Campos, R., Dias, G., Jorge, A. M., & Jatowt, A. (2015) · ACM Computing Surveys · doi:10.1145/2619088 · Cited by 262
Metadata
- Authors
- Cho, J., & Garcia-Molina, H.
- Affiliations
- University of California, Los Angeles, CA
- Stanford University, Stanford, CA
- Venue
- ACM Transactions on Internet Technology
- Published
- 2003-08
- Volume
- 3
- Issue
- 3
- Pages
- 256-290
- ISSN
- 1533-5399
- 1557-6051
- Cited by
- 196
- References
- 1
- Language
- en
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