Academic literature on the topic 'Web Caching'

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Journal articles on the topic "Web Caching"

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Srinath, Harsha, and Shiva Shankar Ramanna. "Web caching." Resonance 7, no. 7 (July 2002): 54–62. http://dx.doi.org/10.1007/bf02836754.

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Cáceres, Ramón, Fred Douglis, Anja Feldmann, Gideon Glass, and Michael Rabinovich. "Web proxy caching." ACM SIGMETRICS Performance Evaluation Review 26, no. 3 (December 1998): 11–15. http://dx.doi.org/10.1145/306225.306230.

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Berghel, Hal. "Responsible web caching." Communications of the ACM 45, no. 9 (September 2002): 15–20. http://dx.doi.org/10.1145/567498.567514.

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Sathiyamoorthi, V. "A Novel Cache Replacement Policy for Web Proxy Caching System Using Web Usage Mining." International Journal of Information Technology and Web Engineering 11, no. 2 (April 2016): 1–13. http://dx.doi.org/10.4018/ijitwe.2016040101.

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Network congestion remains one of the main barriers to the continuing success of the internet and Web based services. In this background, proxy caching is one of the most successful solutions for civilizing the performance of Web since it reduce network traffic, Web server load and improves user perceived response time. Here, the most popular Web objects that are likely to be revisited in the near future are stored in the proxy server thereby it improves the Web response time and saves network bandwidth. The main component of Web caching is it cache replacement policy. It plays a key role in replacing existing objects when there is no room for new one especially when cache is full. Moreover, the conventional replacement policies are used in Web caching environments which provide poor network performance. These policies are suitable for memory caching since it involves fixed sized objects. But, Web caching which involves objects of varying size and hence there is a need for an efficient policy that works better in Web cache environment. Moreover, most of the existing Web caching policies have considered few factors and ignored the factors that have impact on the efficiency of Web proxy caching. Hence, it is decided to propose a novel policy for Web cache environment. The proposed policy includes size, cost, frequency, ageing, time of entry into the cache and popularity of Web objects in cache removal policy. It uses the Web usage mining as a technique to improve Web caching policy. Also, empirical analyses shows that proposed policy performs better than existing policies in terms of various performance metrics such as hit rate and byte hit rate.
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Sathiyamoorthi and Murali Bhaskaran. "Novel Approaches for Integrating MART1 Clustering Based Pre-Fetching Technique with Web Caching." International Journal of Information Technology and Web Engineering 8, no. 2 (April 2013): 18–32. http://dx.doi.org/10.4018/jitwe.2013040102.

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Web caching and Web pre-fetching are two important techniques for improving the performance of Web based information retrieval system. These two techniques would complement each other, since Web caching provides temporal locality whereas Web pre-fetching provides spatial locality of Web objects. However, if the web caching and pre-fetching are integrated inefficiently, this might cause increasing the network traffic as well as the Web server load. Conventional policies are most suitable only for memory caching since it involves fixed page size. But when one deals with web caching which involves pages of different size. Hence one need an efficient algorithm that works better in web cache environment. Moreover conventional replacement policies are not suitable in clustering based pre-fetching environment since multiple objects were pre-fetched. Hence, it cannot be handled by conventional algorithms. Therefore, care must be taken while integrating web caching with web pre-fetching technique in order to overcome these limitations. In this paper, novel algorithms have been proposed for integrating web caching with clustering based pre-fetching technique. Here Modified ART1 has been used for clustering based pre-fetching technique. The proposed algorithm outperforms the traditional algorithms in terms of hit rate and number of objects to be pre-fetched. Hence saves bandwidth.
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Zulfa, Mulki Indana, Rudy Hartanto, and Adhistya Erna Permanasari. "Caching strategy for Web application – a systematic literature review." International Journal of Web Information Systems 16, no. 5 (October 5, 2020): 545–69. http://dx.doi.org/10.1108/ijwis-06-2020-0032.

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Purpose Internet users and Web-based applications continue to grow every day. The response time on a Web application really determines the convenience of its users. Caching Web content is one strategy that can be used to speed up response time. This strategy is divided into three main techniques, namely, Web caching, Web prefetching and application-level caching. The purpose of this paper is to put forward a literature review of caching strategy research that can be used in Web-based applications. Design/methodology/approach The methods used in this paper were as follows: determined the review method, conducted a review process, pros and cons analysis and explained conclusions. The review method is carried out by searching literature from leading journals and conferences. The first search process starts by determining keywords related to caching strategies. To limit the latest literature in accordance with current developments in website technology, search results are limited to the past 10 years, in English only and related to computer science only. Findings Note in advance that Web caching and Web prefetching are slightly overlapping techniques because they have the same goal of reducing latency on the user’s side. But actually, the two techniques are motivated by different basic mechanisms. Web caching uses the basic mechanism of cache replacement or the algorithm to change cache objects in memory when the cache capacity is full, whereas Web prefetching uses the basic mechanism of predicting cache objects that can be accessed in the future. This paper also contributes practical guidelines for choosing the appropriate caching strategy for Web-based applications. Originality/value This paper conducts a state-of-the art review of caching strategies that can be used in Web applications. Exclusively, this paper presents taxonomy, pros and cons of selected research and discusses data sets that are often used in caching strategy research. This paper also provides another contribution, namely, practical instructions for Web developers to decide the caching strategy.
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Dr., H. B. Patelpaik. "Machine Learning-Based Optimization of Web Caching: A Support Vector Machine Model." International Journal of Advance and Applied Research S6, no. 18 (April 10, 2025): 282–88. https://doi.org/10.5281/zenodo.15259563.

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<em>In the era of information technology, the Internet serves as a critical medium for accessing information globally. The World Wide Web (WWW) facilitates a diverse range of Internet-based services, including e-commerce, online banking, entertainment, education, and e-governance. However, the exponential growth in web applications has led to a substantial increase in network traffic, causing congestion and elevating server loads. This, in turn, results in higher response times, thereby negatively impacting user experience. Web caching has emerged as an effective solution to mitigate latency issues by storing frequently accessed web objects closer to end users. Traditional caching strategies, such as Least Recently Used (LRU), Least Frequently Used (LFU), SIZE, GD-Size, and GDSF, have been widely implemented to enhance web system performance. However, recent advancements in machine learning have significantly improved conventional web proxy caching policies. Support Vector Machine (SVM), a robust supervised machine learning algorithm, is extensively utilized for both classification and regression tasks. By integrating conventional caching policies with SVM-based predictive models, intelligent caching approaches have been developed. These models are evaluated using trace-driven simulations, and their performance is systematically compared with traditional web proxy caching techniques. The empirical findings indicate that SVM-enhanced caching strategies yield substantial performance improvements, demonstrating the efficacy of machine learning in optimizing web caching systems.</em>
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Rajendran Baskaran, Kuttuva, and Chellan Kalaiarasan. "Improved Performance by Combining Web Pre-Fetching Using Clustering with Web Caching Based on SVM Learning Method." International Journal of Computers Communications & Control 11, no. 2 (January 26, 2016): 67. http://dx.doi.org/10.15837/ijccc.2016.2.897.

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Combining Web caching and Web pre-fetching results in improving the bandwidth utilization, reducing the load on the origin server and reducing the delay incurred in accessing information. Web pre-fetching is the process of fetching the Web objects from the origin server which has more likelihood of being used in future. The fetched contents are stored in the cache. Web caching is the process of storing the popular objects ”closer” to the user so that they can be retrieved faster. In the literature many interesting works have been carried out separately for Web caching and Web pre-fetching. In this work, clustering technique is used for pre-fetching and SVM-LRU technique forWeb caching and the performance is measured in terms of Hit Ratio (HR) and Byte Hit Ratio (BHR). With the help of real data, it is demonstrated that the above approach is superior to the method of combining clustering based prefetching technique with traditional LRU page replacement method for Web caching.
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Davison, B. D. "A Web caching primer." IEEE Internet Computing 5, no. 4 (2001): 38–45. http://dx.doi.org/10.1109/4236.939449.

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Chauhan, Shobhit. "Caching in Web Applications." International Journal of Computer Trends and Technology 68, no. 8 (August 25, 2020): 14–20. http://dx.doi.org/10.14445/22312803/ijctt-v68i8p102.

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Dissertations / Theses on the topic "Web Caching"

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Gu, Wenzheng. "Ubiquitous Web caching." [Gainesville, Fla.] : University of Florida, 2003. http://purl.fcla.edu/fcla/etd/UFE0002406.

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Mahdavi, Mehregan Computer Science &amp Engineering Faculty of Engineering UNSW. "Caching dynamic data for web applications." Awarded by:University of New South Wales. Computer Science and Engineering, 2006. http://handle.unsw.edu.au/1959.4/32316.

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Web portals are one of the rapidly growing applications, providing a single interface to access different sources (providers). The results from the providers are typically obtained by each provider querying a database and returning an HTML or XML document. Performance and in particular providing fast response time is one of the critical issues in such applications. Dissatisfaction of users dramatically increases with increasing response time, resulting in abandonment of Web sites, which in turn could result in loss of revenue by the providers and the portal. Caching is one of the key techniques that address the performance of such applications. In this work we focus on improving the performance of portal applications via caching. We discuss the limitations of existing caching solutions in such applications and introduce a caching strategy based on collaboration between the portal and its providers. Providers trace their logs, extract information to identify good candidates for caching and notify the portal. Caching at the portal is decided based on scores calculated by providers and associated with objects. We evaluate the performance of the collaborative caching strategy using simulation data. We show how providers can trace their logs and calculate cache-worthiness scores for their objects and notify the portal. We also address the issue of heterogeneous scoring policies by different providers and introduce mechanisms to regulate caching scores. We also show how portal and providers can synchronize their meta-data in order to minimize the overhead associated with collaboration for caching.
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Liang, Zhengang. "Transparent Web caching with load balancing." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ59383.pdf.

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Gupta, Priya S. M. Massachusetts Institute of Technology. "Providing caching abstractions for web applications." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62453.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 99-101).<br>Web-based applications are used by millions of users daily, and as a result a key challenge facing web application designers is scaling their applications to handle this load. A crucial component of this challenge is scaling the data storage layer, especially for the newer class of social networking applications that have huge amounts of shared data. Caching is an important scaling technique and is a critical part of the storage layer for such high-traffic web applications. Usually, building caching mechanisms involves significant effort from the application developer to maintain and invalidate data in the cache. In this work we present CacheGenie, a system which aims to make it easy for web application developers to build caching mechanisms in their applications. It achieves this by proposing high-level caching abstractions for frequently observed query patterns in web applications. These abstractions take the form of declarative query objects, and once the developer defines them, she does not have to worry about managing the cache (i.e., insertion and deletion) or maintaining consistency (e.g., invalidation or updates) when writing application code. We designed and implemented CacheGenie in the popular Django web application framework, with PostgreSQL as the database backend and memcached as the caching layer. We use triggers inside the database to automatically invalidate or keep the cache synchronized, as desired by the developer. We have not made any modifications to PostgreSQL or memcached. To evaluate our prototype, we ported several Pinax web applications to use our caching abstractions and performed several experiments. Our results show that it takes little effort for application developers to use CacheGenie, and that caching provides a throughput improvement by a factor of 2-2.5 for read-mostly workloads.<br>by Priya Gupta.<br>S.M.
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Chiang, Cho-Yu. "On building dynamic web caching hierarchies /." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488199501403111.

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Arshinov, Alex. "Building high-performance web-caching servers." Thesis, De Montfort University, 2004. http://hdl.handle.net/2086/13257.

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Logren, Dély Tobias. "Caching HTTP : A comparative study of caching reverse proxies Varnish and Nginx." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-9679.

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With the amount of users on the web steadily increasing websites must at times endure heavy loads and risk grinding to a halt beneath the flood of visitors. One solution to this problem is by using HTTP reverse proxy caching, which acts as an intermediate between web application and user. Content from the application is stored and passed on, avoiding the need for the application produce it anew for every request. One popular application designed solely for this task is Varnish; another interesting application for the task is Nginx which is primarily designed as a web server. This thesis compares the performance of the two applications in terms of number of requests served in relation to response time, as well as system load and free memory. With both applications using their default configuration, the experiments find that Nginx performs better in the majority of tests performed. The difference is however very slightly in tests with low request rate.
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Zou, Qing. "Transparent Web caching with minimum response time." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2002. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ65661.pdf.

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Sherman, Alexander 1975. "Distributed web caching system with consistent hashing." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80121.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.<br>Includes bibliographical references (p. 63-64).<br>by Alexander Sherman.<br>Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
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Acharjee, Utpal. "Personalized and artificial intelligence Web caching and prefetching." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27215.

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Web caching and prefetching are the most popular and widely used solutions to remedy Internet performance problems. Performance is increased if a combination of caching and prefetching systems is used rather than if these techniques are used individually. Web caching reduces the bandwidth consumption and network latency by serving the user's request from its own cache instead of the original Internet source. Prefetching is a technique that preloads and caches the web object that is not currently requested by the user but can be requested (expected) in the near future. It provides low retrieval latency for users and as well as high hit ratios. Existing methods for caching and prefetching are mostly traditional sharable Proxy cache servers. In our personalized caching and prefetching approach, the system builds up a user profile associated with a user's web behaviour by parsing the keywords from HTML pages that are browsed by the user. The keywords of a user profile are updated by adding a new keyword or incrementing its associated weight if it is already, in the profile. This user profile reflects users' web behaviour or interest. In this cache and prefetch prediction module we considered both static and dynamic users' web behaviour. We have designed and implemented an artificial intelligence multilayer neural network-based caching and prediction algorithm to personalize the Proteus Proxy server with this mechanism. Enhanced Proteus is a multilingual and internationally-supported Proxy system and can work with both mobile and traditional Proxy server-based sharable environments. In the prefetch option of Proteus, time also implemented a unique content filtering feature that blocks the downloading of unwanted web objects.
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Books on the topic "Web Caching"

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Oliver, Spatscheck, ed. Web caching and replication. Boston: Addison-Wesley, 2002.

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Nagaraj, S. V. Web caching and its applications. Boston: Kluwer Academic Publishers, 2004.

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Chi, Chi-Hung, Maarten van Steen, and Craig Wills, eds. Web Content Caching and Distribution. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b101692.

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Douglis, Fred, and Brian D. Davison, eds. Web Content Caching and Distribution. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/1-4020-2258-1.

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1961-, Bestavros Azer, and Rabinovich Michael, eds. Web caching and content delivery: Proceedings of the sixth International Web Content Caching and Distribution Workshop, Boston University, Boston, Massachusetts, USA, June 20-22, 2001. Amsterdam: Elsevier, 2001.

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International Web Content Caching and Distribution Workshop. Web content caching and distribution: Proceedings of the 8th International Workshop. Dordrecht: Kluwer Academic Publishers, 2004.

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Frederick, Douglis, and Davison Brian D, eds. Web content caching and distribution: Proceedings of the 8th International Workshop, IBM T.J. Watson Research Center, Hawthorne, New York, USA, September 29-October 1, 2003. Boston: Kluwer Academic Publishers, 2004.

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Vasiliev, Yuli. PHP Oracle web development: Data processing, security, caching, XML, web services and AJAX : a practical guide to combining the power, performance, scalability, and reliability of Oracle Database with the ease of use, short development time, and high performance of PHP. Birmingham, U.K: Packt Pub., 2007.

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Wessels, Duane. Web Caching. Tandem Library, 2001.

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Web caching. Sebastopol, CA: O'Reilly & Associates, 2001.

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Book chapters on the topic "Web Caching"

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Fox, Richard, and Wei Hao. "Web Caching." In Internet Infrastructure, 373–409. Boca Raton : Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa, plc, [2017]: CRC Press, 2017. http://dx.doi.org/10.1201/9781315175577-9.

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Wessler, Michael, Erin Mulder, Rob Harrop, and Jan Machacek. "Configuring Web Caching." In Oracle Application Server 10g, 389–421. Berkeley, CA: Apress, 2004. http://dx.doi.org/10.1007/978-1-4302-0744-3_19.

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Rojas, Carlos. "Caching Strategies." In Building Progressive Web Applications with Vue.js, 67–81. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5334-2_4.

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Buchholz, Sven, and Alexander Schill. "Adaptation-Aware Web Caching: Caching in the Future Pervasive Web." In Kommunikation in Verteilten Systemen (KiVS), 55–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55569-5_5.

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Kalyanasundaram, Bala, John Noga, Kirk Pruhs, and Gerhard Woeginger†. "Caching for Web Searching." In Algorithm Theory - SWAT 2000, 150–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44985-x_14.

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Bent, Leeann, Michael Rabinovich, Geoffrey M. Voelker, and Zhen Xiao. "Towards Informed Web Content Delivery." In Web Content Caching and Distribution, 232–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30471-5_18.

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Yang, Mengdong, and Gang Wu. "Semantic Caching for Semantic Web Applications." In The Semantic Web, 192–209. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29923-0_13.

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Seltzsam, Stefan, Roland Holzhauser, and Alfons Kemper. "Semantic Caching for Web Services." In Service-Oriented Computing – ICSOC 2007, 324–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596141_25.

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Sosa Sosa, Víctor J., Gabriel González S., Leandro Navarro, and Joaquín Pérez O. "A Resilient Web Caching Architecture." In Computational Science and Its Applications — ICCSA 2003, 160–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44839-x_18.

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Zhao, Weibin, and Henning Schulzrinne. "DotSlash: A Self-Configuring and Scalable Rescue System for Handling Web Hotspots Effectively." In Web Content Caching and Distribution, 1–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30471-5_1.

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Conference papers on the topic "Web Caching"

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Rhea, Sean C., Kevin Liang, and Eric Brewer. "Value-based web caching." In the twelfth international conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/775152.775239.

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Qian, Feng, Kee Shen Quah, Junxian Huang, Jeffrey Erman, Alexandre Gerber, Zhuoqing Mao, Subhabrata Sen, and Oliver Spatscheck. "Web caching on smartphones." In the 10th international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2307636.2307649.

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Turner, D. A. "Web page caching in Java Web applications." In International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II. IEEE, 2005. http://dx.doi.org/10.1109/itcc.2005.296.

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Raza, Ali, Yasir Zaki, Thomas Pötsch, Jay Chen, and Lakshmi Subramanian. "Extreme Web Caching for Faster Web Browsing." In SIGCOMM '15: ACM SIGCOMM 2015 Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2785956.2790032.

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Atassi, Mohamed R., Sherif G. Aly, and Amr El-Kadi. "Cooperative web caching of dynamic web content." In 2011 9th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA). IEEE, 2011. http://dx.doi.org/10.1109/aiccsa.2011.6126610.

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Hai Liu and Maobian Chen. "Evaluation of web caching consistency." In 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icacte.2010.5579113.

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Shi, Lei, and Yan Zhang. "Optimal Model of Web Caching." In 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.220.

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Katsaros, Dimitrios, and Yannis Manolopoulos. "Caching in Web memory hierarchies." In the 2004 ACM symposium. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/967900.968126.

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Liu, Zhen, Philippe Nain, Nicolas Niclausse, and Don Towsley. "Static caching of Web servers." In Photonics West '98 Electronic Imaging, edited by Kevin Jeffay, Dilip D. Kandlur, and Timothy Roscoe. SPIE, 1997. http://dx.doi.org/10.1117/12.298419.

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Zhang, Kaimin, Lu Wang, Aimin Pan, and Bin Benjamin Zhu. "Smart caching for web browsers." In the 19th international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1772690.1772741.

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Reports on the topic "Web Caching"

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Cooper, I., I. Melve, and G. Tomlinson. Internet Web Replication and Caching Taxonomy. RFC Editor, January 2001. http://dx.doi.org/10.17487/rfc3040.

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DeMarle, David, and Andrew Bauer. In situ visualization with temporal caching. Engineer Research and Development Center (U.S.), January 2022. http://dx.doi.org/10.21079/11681/43042.

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In situ visualization is a technique in which plots and other visual analyses are performed in tandem with numerical simulation processes in order to better utilize HPC machine resources. Especially with unattended exploratory engineering simulation analyses, events may occur during the run, which justify supplemental processing. Sometimes though, when the events do occur, the phenomena of interest includes the physics that precipitated the events and this may be the key insight into understanding the phenomena that is being simulated. In situ temporal caching is the temporary storing of produced data in memory for possible later analysis including time varying visualization. The later analysis and visualization still occurs during the simulation run but not until after the significant events have been detected. In this article, we demonstrate how temporal caching can be used with in-line in situ visualization to reduce simulation run-time while still capturing essential simulation results.
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Brandt, Sebastian, and Anni-Yasmin Turhan. An Approach for Optimizing ALE-Approximation of ALC-Concepts. Technische Universität Dresden, 2002. http://dx.doi.org/10.25368/2022.121.

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An approximation of an ALC-concept by an ALE-concept can be computed in double exponential time [4]. Consequently, one needs powerful optimization techniques for approximating an entire unfoldable TBox. Addressing this issue we identify a special form of ALC-concepts, which can be divided into parts s.t. each part can be approximated independently. This independent approximation in turn facilitates caching during the computation of approximation.
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Allegheny woodrat and eastern small-footed bat inventory: White Rocks ? Cumberland Gap National Historical Park. National Park Service, 2024. http://dx.doi.org/10.36967/2302513.

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Surveys were conducted for the presence of Allegheny woodrats, eastern small-footed bats, and their habitat within Sample Units surrounding potential climbing routes on the White Rocks cliff of Cumberland Gap National Historical Park. White Rocks is a 200-300ft south facing cliff along the Virginia- Kentucky border that contains the typical habitat requirements of both species: rock ledges, outcrops, or a network of fissures and crevices surrounded by forested habitat. The eastern small-footed bat uses this type of habitat primarily during the summer months for roosting while the Allegheny woodrat is present year-round building nests within deep crevices. The eastern small-footed bat is listed as threatened in Kentucky and both are considered Species of Greatest Conservation Need in Virginia. The rock-climbing community has approached the National Park Service with interest in opening the cliff to recreational climbing. The results of this survey will be used to assess impacts to the species and aid in the studies required for the potential development of a formal climbing management plan. Surveys were conducted from August 24 to September 3, 2021, and included searches of the cliff face via rappelling, ground searches of suitable habitat surrounding the rim and base of the climbing routes, and noninvasive woodrat camera trap and bat acoustic surveys. Suitable eastern small-footed bat and Allegheny woodrat habitat was documented along all eleven potential climbing routes, and results of the acoustic and camera trap surveys confirmed the presence of both species. Surveyors did not observe roosting bats during visual encounter surveys, so we cannot confirm they are roosting along the specific climbing routes. It is likely they are using or could use the habitat at some point given their presence at the site. Surveyors did not observe obvious sign of woodrat presence, perhaps due to the complexity of the cliff habitat, but woodrats were captured at 32 of 37 (86%) camera sites. Video of woodrat activity at sites F3-C3, MF-C3, and CC-C1 confirm that these cameras were placed at crevices actively used by woodrats for caching food. Woodrat activity was captured on the first night at nearly half of the camera sites (40%) and within all Sample Units except SU-4. This suggests that the rim and/or base of most climbing routes are at or near core activity centers. Detection data was fitted to occupancy models to estimate probabilities of site occupancy and detection. Consistent with observations of woodrat activity detection probabilities and na?ve estimates of occupancy indicate woodrats are active at both the rim and base throughout the white rocks area. Design constraints, however, make it difficult to make strong inferences about factors affecting occupancy at the site or to predict occupancy at unsampled areas of the White Rocks cliff. Detection probabilities were high in all Sample Units especially after the first night of detection and were positively associated with habitat. However, these variables had little to do with site occupancy rates and are not very informative for predicting the influence of climbing activities on Allegheny woodrats. Research on the effects of rock climbing to roosting bats and Allegheny woodrats is lacking, but it has the potential to negatively impact these species both directly and indirectly. Directly through disturbance or harm to roosting bats or woodrats during climbing activities and indirectly through degradation of the cliff face and loss of suitable habitat. Whether climbing activities negatively affect the population of either species may depend on the amount of suitable habitat in areas where climbing does not occur and the extent of climbing activities. Suitable habitat does not appear to be a limiting factor at the site and the White Rocks cliff area extends beyond the potential climbing areas. These and other factors such as the presence of other species of concern should be considered when evaluating alternatives to continue climbing closures or consider opening the cliff to recreational climbing with protection measures. Measures that may reduce the impact of climbing activities on these species include additional surveys to determine exact roosting or nesting areas, restrictions on the amount and timing of climbing activities, restrictions on actions that degrade or remove vegetation from the rock surface, and education about the presence of these species at the site.
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