Relevant bibliographies by topics / Web-logs

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Web-logs'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Web-logs"

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Reddy, K. Sudheer, G. Partha Saradhi Varma, and I. Ramesh Babu. "Preprocessing the web server logs." ACM SIGSOFT Software Engineering Notes 37, no. 3 (2012): 1–5. http://dx.doi.org/10.1145/2180921.2180940.

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M., B., and Haseena Begum. "An Efficient Web Recommender System for Web Logs." International Journal of Computer Applications 152, no. 3 (2016): 9–12. http://dx.doi.org/10.5120/ijca2016911795.

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Song, Bo, and Sheng Bo Chen. "Reorganization of Web Site Structure Using Web Logs." Advanced Materials Research 756-759 (September 2013): 1828–34. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1828.

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With the rapid development and evolution of Internet, Web applications play a significant role in people's daily life and daily work. Usually, Web developers design the structure of the web application according to their experiences. But as the evolution of web applications, the existing structure is not enough to meet the needs of the users. This paper proposes an approach to reorganizing the structure of web applications dynamically based on Web logs. Obtained structure of the web application is more reasonable, more convenient to provide services for the users.
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Joshila Grace, L. K., V. Maheswari, and Dhinaharan Nagamalai. "Analysis of Web Logs And Web User In Web Mining." International Journal of Network Security & Its Applications 3, no. 1 (2011): 99–110. http://dx.doi.org/10.5121/ijnsa.2011.3107.

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Ingram, Albert L. "Using Web Server Logs in Evaluating Instructional Web Sites." Journal of Educational Technology Systems 28, no. 2 (1999): 137–57. http://dx.doi.org/10.2190/r3ae-ucry-njvr-ly6f.

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Manchanda, Mahesh. "Web Usage Mining: Dynamic Methodology to Preprocessing Web Logs." HELIX 8, no. 5 (2018): 3810–15. http://dx.doi.org/10.29042/2018-3810-3815.

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Masseglia, F., P. Poncelet, M. Teisseire, and A. Marascu. "Web usage mining: extracting unexpected periods from web logs." Data Mining and Knowledge Discovery 16, no. 1 (2007): 39–65. http://dx.doi.org/10.1007/s10618-007-0080-z.

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Anandha, Jothi, and P. Gayathri. "A Survey on Clustering Algorithms in Web Usage Mining." International Journal of Trend in Scientific Research and Development 2, no. 4 (2018): 249–51. https://doi.org/10.31142/ijtsrd12742.

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Clustering an information is gathering an data together as indicated by their likeness. There are numerous clustering algorithms are availble here to cluster web logs. In this paper, it mainly focusing on clustering algorithm that is used for clustering web logs. Also this survey is about an algorithm which is used to cluster an web log or web usage data. Anandha Jothi | Gayathri. P "A Survey on: Clustering Algorithms in Web Usage Mining" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL:
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Chang, Chih-Kai, Gwo-Dong Chen, and Kou-Liang Ou. "Student Portfolio Analysis by Data Cube Technology for Decision Support of Web-Based Classroom Teacher." Journal of Educational Computing Research 19, no. 3 (1998): 307–28. http://dx.doi.org/10.2190/k6x6-9fmd-yeen-kn42.

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As learners increasingly use Web-based distance learning systems over years, large amounts of learning logs are generated. An instructor needs analysis tools to manage the logs and discover patterns within them to help improve instruction. A variety of analysis tools, including descriptive statistics, statistical inference, prediction, etc., can be utilized to analyze the effects of a teaching strategy from the logs. However, logs of a Web server, as learners' portfolios, cannot satisfy the requirements of these analysis tools. To resolve this problem, a data cube model is proposed as the infr
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B. Raut, Aditi. "Web Logs Analysis for Finding Brand Status." IOSR Journal of Computer Engineering 16, no. 4 (2014): 78–85. http://dx.doi.org/10.9790/0661-16467885.

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Dissertations / Theses on the topic "Web-logs"

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Rao, Rashmi Jayathirtha. "Modeling learning behaviour and cognitive bias from web logs." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492560600002105.

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Lam, Yin-wan, and 林燕雲. "Senior secondary students use of web-logs in writing Chinese." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B37198361.

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Chiara, Ramon. ""Aplicação de técnicas de data mining em logs de servidores web"." Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-19012004-093205/.

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Com o advento da Internet, as empresas puderam mostrar-se para o mundo. A possibilidade de colocar um negócio na World Wide Web (WWW) criou um novo tipo de dado que as empresas podem utilizar para melhorar ainda mais seu conhecimento sobre o mercado: a sequência de cliques que um usuário efetua em um site. Esse dado pode ser armazenado em uma espécie de Data Warehouse para ser analisado com técnicas de descoberta de conhecimento em bases de dados. Assim, há a necessidade de se realizar pesquisas para mostrar como retirar conhecimento a partir dessas sequências de cliques. Neste trabalho
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Holmes, Ashley Joyce. "Web logs in the Post-Secondary Writing Classroom: A Study of Purposes." NCSU, 2005. http://www.lib.ncsu.edu/theses/available/etd-03222005-205901/.

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In the past few decades, education research has been thriving in the areas of computers and new technologies. Often, teachers turn to what is popular in the technological world for new ideas to use in their classrooms. One such technology that has become extremely popular in Web culture is Web logs, now most often referred to as ?weblogs,? or simply ?blogs.? The present work seeks to further research on weblogs in education by identifying the various ways in which current post-secondary writing course teachers are using them in their courses. This definitional study attempts to answer the ques
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Villalobos, Luengo César Alexis. "Análisis de archivos Logs semi-estructurados de ambientes Web usando tecnologías Big-Data." Tesis, Universidad de Chile, 2016. http://repositorio.uchile.cl/handle/2250/140417.

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Magíster en Tecnologías de la Información<br>Actualmente el volumen de datos que las empresas generan es mucho más grande del que realmente pueden procesar, por ende existe un gran universo de información que se pierde implícito en estos datos. Este proyecto de tesis logró implementar tecnologías Big Data capaces de extraer información de estos grandes volúmenes de datos existentes en la organización y que no eran utilizados, de tal forma de transformarlos en valor para el negocio. La empresa elegida para este proyecto se dedicada al pago de cotizaciones previsionales de forma electróni
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Vasconcelos, Leandro Guarino de. "Uma abordagem para mineração de logs para apoiar a construção de aplicações web adaptativas." Instituto Nacional de Pesquisas Espaciais (INPE), 2017. http://urlib.net/sid.inpe.br/mtc-m21b/2017/07.24.15.06.

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Atualmente, há mais de 1 bilhão de web sites disponíveis. Neste enorme hiperespaço, há muitos web sites que fornecem o mesmo conteúdo ou serviço. Portanto, quando um usuário não encontra o que está procurando ou enfrenta dificuldades na interação, ele tende a procurar em outro web site. Para suprir as necessidades dos usuários atuais da Web, web sites adaptativos têm sido propostos. As abordagens de adaptação existentes geralmente adaptam o conteúdo das páginas de acordo com o interesse do usuário. Entretanto, a adaptação da estrutura da interface para atender às necessidades do usuário ainda
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Tanasa, Doru. "Web usage mining : contributions to intersites logs preprocessing and sequential pattern extraction with low support." Nice, 2005. http://www.theses.fr/2005NICE4019.

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Le Web Usage Mining (WUM), domaine de recherche assez récent, correspond au processus d’extraction des connaissances à partir des données (ECD) appliquées aux données d’usage sur le Web. Il comporte trois étapes principales : le prétraitement des données, la découverte des schémas et l’analyse des résultats. La quantité des données d’usage à analyser ainsi que leur faible qualité (en particulier l’absence de structuration) sont les principaux problèmes en WUM. Les algorithmes classiques de fouille de données appliquées sur ces données donnent généralement des résultats décevants en termes de p
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Allam, Amir Ali. "Measuring the use of online corporate annual reports through the analysis of web server logs." Thesis, University of Birmingham, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.633067.

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The current study investigates a novel area of accounting research; the use of online annual reports by corporate Websites' visitors. This study is of a cross-disciplinary nature as it involves knowledge from different fields including accounting, mass communication and computer science. It is argued that the examination of communication theory may provide insights into how best to enhance the value relevance of accounting information. The Internet, as a new means of communication, has many possible effects on the way accounting information is disseminated and the way its usage can be investig
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Tanasa, Doru. "Fouille de données d'usage du Web : Contributions au prétraitement de logs Web Intersites et à l'extraction des motifs séquentiels avec un faible support." Phd thesis, Université de Nice Sophia-Antipolis, 2005. http://tel.archives-ouvertes.fr/tel-00178870.

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Les quinze dernières années ont été marquées par une croissance exponentielle du domaine du Web tant dans le nombre de sites Web disponibles que dans le nombre d'utilisateurs de ces sites. Cette croissance a généré de très grandes masses de données relatives aux traces d'usage duWeb par les internautes, celles-ci enregistrées dans des fichiers logs Web. De plus, les propriétaires de ces sites ont exprimé le besoin de mieux comprendre leurs visiteurs afin de mieux répondre à leurs attentes. Le Web Usage Mining (WUM), domaine de recherche assez récent, correspond justement au processus d'extract
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Mantella, Dana G. ""Pro-ana" Web-log uses and gratifications towards understanding the pro-anorexia paradox." unrestricted, 2007. http://etd.gsu.edu/theses/available/etd-04182007-194043/.

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Thesis (M.A.)--Georgia State University, 2007.<br>Cynthia Hoffner, committee chair; Jaye Atkinson, Mary Ann Romski, committee members. Electronic text (90 p.) : digital, PDF file. Title from file title page. Description based on contents viewed Dec. 14, 2007. Includes bibliographical references (p. 67-74).
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Books on the topic "Web-logs"

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Kline, David. Blog!: How the newest media revolution is changing politics, business, and culture. CDS Books, 2005.

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Eisenberg, Bryan, and Jim Novo. The marketer's common sense guide to e-metrics: 22 benchmarks to understand the major trends, key opportunities, and hidden hazards your web logs uncover. Future Now, Inc., 2002.

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Pfundstein, Jens. Face - Deconstruction in Web Logs. GRIN Verlag GmbH, 2009.

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Stauffer, Todd. Blog on: Building Online Communities with Web Logs. Tandem Library, 2002.

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Stauffer, Todd. Blog On: Building Online Communities with Web Logs. McGraw-Hill/OsborneMedia, 2002.

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Stauffer, Todd. Blog On: Building Online Communities with Web Logs. McGraw-Hill/OsborneMedia, 2002.

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Christl, Alexander. Datenschutz Im Internet: Cookies, Web-Logs, Location Based Services, EMail, Webbugs, Spyware. Disserta Verlag. ein Imprint der Diplomica Verlag GmbH, 2014.

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Christl, Alexander. Cookies, Web-Logs, Location Based Services, eMail, Webbugs, Spyware - Datenschutz im Internet. GRIN Verlag GmbH, 2009.

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Jose, Jeeva. Gaining Insight into User and Search Engine Behaviour by Analyzing Web Logs. Anchor Academic Publishing. ein Imprint der Diplomica Verlag GmbH, 2016.

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Book chapters on the topic "Web-logs"

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Fei, Bennie, Jan Eloff, Martin Olivier, and Hein Venter. "Analysis of Web Proxy Logs." In IFIP Advances in Information and Communication Technology. Springer New York, 2006. http://dx.doi.org/10.1007/0-387-36891-4_20.

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Schmitz, Andreas, and Olga Yanenko. "Web Server Logs und Logfiles." In Handbuch Methoden der empirischen Sozialforschung. Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-531-18939-0_65.

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Schmitz, Andreas, and Olga Yanenko. "Web Server Logs und Logfiles." In Handbuch Methoden der empirischen Sozialforschung. Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-21308-4_70.

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Schmitz, Andreas, and Olga Yanenko. "Web Server Logs und Logfiles." In Handbuch Methoden der empirischen Sozialforschung. Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-37985-8_87.

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Yang, Qiang, Charles X. Ling, and Jianfeng Gao. "Mining Web Logs for Actionable Knowledge." In Intelligent Technologies for Information Analysis. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07952-2_8.

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Labbaci, Hamza, Brahim Medjahed, and Youcef Aklouf. "Learning Interactions from Web Service Logs." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64471-4_22.

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Yang, Qiang, Henry Haining Zhang, Ian T. Y. Li, and Ye Lu. "Mining Web Logs to Improve Web Caching and Prefetching." In Web Intelligence: Research and Development. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45490-x_62.

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Gu, Yingqin, Jianwei Cui, Hongyan Liu, et al. "Detecting Hot Events from Web Search Logs." In Web-Age Information Management. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14246-8_41.

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Sun, Liping, and Xiuzhen Zhang. "Efficient Frequent Pattern Mining on Web Logs." In Advanced Web Technologies and Applications. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24655-8_58.

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Morzy, Tadeusz, Marek Wojciechowski, and Maciej Zakrzewicz. "Optimizing Pattern Queries for Web Access Logs." In Advances in Databases and Information Systems. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44803-9_12.

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Conference papers on the topic "Web-logs"

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Kumar, Ravi. "Mining web logs." In the 15th ACM SIGKDD international conference. ACM Press, 2009. http://dx.doi.org/10.1145/1557019.1557022.

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Sudhamathy, G. "Mining web logs." In the 1st Amrita ACM-W Celebration. ACM Press, 2010. http://dx.doi.org/10.1145/1858378.1858435.

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Hassan, Muhammad Umair, Kamran Shaukat, Dongmie Niu, et al. "Web-Logs Prediction with Web Mining." In 2018 2nd IEEE Advanced Information Management,Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2018. http://dx.doi.org/10.1109/imcec.2018.8469256.

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Joshi, Karuna P., Anupam Joshi, Yelena Yesha, and Raghu Krishnapuram. "Warehousing and mining Web logs." In the second international workshop. ACM Press, 1999. http://dx.doi.org/10.1145/319759.319792.

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Zhou, Jin, Chen Ding, and Dimitrios Androutsos. "Improving web site search using web server logs." In the 2006 conference of the Center for Advanced Studies. ACM Press, 2006. http://dx.doi.org/10.1145/1188966.1188996.

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Højgaard, Christian, Joachim Sejr, and Yun-Gyung Cheong. "Query Disambiguation from Web Search Logs." In Information Technology and Computer Science 2016. Science & Engineering Research Support soCiety, 2016. http://dx.doi.org/10.14257/astl.2016.133.17.

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Sisodia, Dilip Singh, and Shrish Verma. "Web usage pattern analysis through web logs: A review." In 2012 International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE, 2012. http://dx.doi.org/10.1109/jcsse.2012.6261924.

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Malik, S. K., N. Prakash, and S. A. M. Rizvi. "Ontology and Web Usage Mining towards an Intelligent Web Focusing Web Logs." In 2010 International Conference on Computational Intelligence and Communication Networks (CICN 2010). IEEE, 2010. http://dx.doi.org/10.1109/cicn.2010.90.

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Rožanc, Igor, and Marko Poženel. "Reconstruction of the Web Application Hypertext Model using Web Logs." In Software Engineering / 811: Parallel and Distributed Computing and Networks / 816: Artificial Intelligence and Applications. ACTAPRESS, 2014. http://dx.doi.org/10.2316/p.2014.810-032.

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Eltahir, Mirghani A., and Anour F. A. Dafa-Alla. "Extracting knowledge from web server logs using web usage mining." In 2013 International Conference on Computing, Electrical and Electronics Engineering (ICCEEE). IEEE, 2013. http://dx.doi.org/10.1109/icceee.2013.6633973.

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Reports on the topic "Web-logs"

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Joshi, Anupam, and Raghu Krishnapuram. On Mining Web Access Logs. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada461525.

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Strauss, Ilan, Jangho Yang, Tim O’Reilly, Sruly Rosenblat, and Isobel Moure. The Attribution Crisis in LLM Search Results: Estimating Ecosystem Exploitation. AI Disclosures Project, Social Science Research Council, 2025. https://doi.org/10.35650/aidp.4114.d.2025.

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Web-enabled LLMs routinely answer queries without crediting the pages they consume, creating an “attribution gap” – the difference between relevant URLs visited and those cited. Using 14,000 real-world LMArena logs from search-enabled LLM systems, we highlight three modes of exploitation online: 1) Skipping web search altogether (34% of Google Gemini model answers, 24% of OpenAI’s GPT-4o model answers); 2) Omitting citations entirely – Gemini supplies no clickable citations in 92% of responses; 3) High-volume exploitation: Perplexity’s Sonar visits approximately a dozen relevant pages when sea
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Gaskin, Tosin, Matthew Balazik, Catherine Thomas, et al. Living shoreline in USACE projects : a review. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49678.

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The term living shoreline (LS) refers to the practice of shoreline stabilization using natural elements (e.g., vegetation, oysters, logs, etc.) in a way that maintains continuity and connectivity between terrestrial and aquatic habitats. This report provides a review of LS practices to assess the applicability of these engineering techniques for US Army Corps of Engineers (USACE) projects. Specifically, this review examines the current state of knowledge regarding LS efforts through evaluation of peer-reviewed literature, agency reports, web tools, applications, and relevant guidance. It is im
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