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Relevant bibliographies by topics / Web-logs

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Academic literature on the topic 'Web-logs'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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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 (May 16, 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 (October 17, 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 (January 28, 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 (December 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 (August 31, 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 (September 15, 2007): 39–65. http://dx.doi.org/10.1007/s10618-007-0080-z.

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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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Harika, B., and T. Sudha. "Extraction of Knowledge from Web Server Logs Using Web Usage Mining." Asian Journal of Computer Science and Technology 8, S3 (June 5, 2019): 12–15. http://dx.doi.org/10.51983/ajcst-2019.8.s3.2113.

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Information on internet increases rapidly from day to day and the usage of the web also increases, thus there is the need to discover interesting patterns from web. The process used to extract and mine useful information from web documents by using Data Mining Techniques is called Web Mining. Web Mining is broadly classified in to three types namely Web Content Mining, Web Structure Mining and Web Usage Mining. In this paper our focus is mainly on Web Usage Mining, where we are applying the data mining techniques to analyse and discover interesting knowledge from the Web Usage data. The activities of the user are captured and stored at different levels such as server level, proxy level and user level called as Web Usage Data and the usage data stored at server side is Web Server Log, where it records the browsing behavior of users and their requests based on the user clicks. Web server Log is a primary source to perform Web Usage Mining. This paper also brings in to discussion of various existing pre-processing techniques and analysis of web log files and how clustering is applied to group the users based on the browsing behavior of users on their interested contents.

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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 (October 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 infrastructure to store learning logs for analysis. We also describe the method of using query language to retrieve information from a database to construct the data cube. Furthermore, user-friendly operations for manipulating a data cube can retrieve statistical information from the data cube. Although statistical tools for managing Web logs exist, none specifically address the needs of the distance learning instructor. This article uses data cubes and database technology as fundamental analysis tools to satisfy a distance learning instructor's requirements for managing and analyzing learning logs.

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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 são discutidas e analisadas algumas das técnicas utilizadas para atingir esse objetivo. é proposta uma ferramenta onde os dados dessas sequências de cliques são mapeadas para o formato atributo-valor utilizado pelo Sistema Discover, um sistema sendo desenvolvindo em nosso Laboratório para o planejamento e execução de experimentos relacionados aos algoritmos de aprendizado utilizados durante a fase de Mineração de Dados do processo de descoberta de conhecimento em bases de dados. Ainda, é proposta a utilização do sistema de Programação Lógica Indutiva chamado Progol para extrair conhecimento relacional das sessões de sequências de cliques que caracterizam a interação de usuários com as páginas visitadas no site. Experimentos iniciais com a utilização de uma sequência de cliques real foram realizados usando Progol e algumas das facilidades já implementadas pelo Sistema Discover.

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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 question: for what educational, or non-educational, purposes are weblogs in post-secondary writing courses being used? The study looks at the way educators claim to be using weblogs in their courses based on how they explain their blog assignments to students (either on a course syllabus or course blog posting). Adding depth to the analysis, the study also explores survey responses from thirty-two college writing teachers across the country. The eleven main uses for weblogs in writing courses that this study identifies are as follows: 1) as a public space with a broad audience, 2) to post student work, 3) as a journal, 4) to reflect on course-related assignments, 5) for student discussion and interaction, 6) to explore and share ideas, as well as brainstorm, 7) to engage with and respond to assigned readings, 8) for collaborative projects, 9) to link to Web materials, 10) to ask and answer questions related to the course, and 11) to discuss topics not necessarily related to the course. After compiling data as to these current uses of weblogs in college writing courses, this researcher explores the implications of these uses, offering suggestions and drawing conclusions as to how the new technology of weblogs has impacted and will impact college level writing courses.

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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
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 electrónica por internet. Su función es ser el medio por el cual se recaudan las cotizaciones de los trabajadores del país. Cada una de estas cotizaciones es informada, rendida y publicada a las instituciones previsionales correspondientes (Mutuales, Cajas de Compensación, AFPs, etc.). Para realizar su función, la organización ha implementado a lo largo de sus 15 años una gran infraestructura de alto rendimiento orientada a servicios web. Actualmente esta arquitectura de servicios genera una gran cantidad de archivos logs que registran los sucesos de las distintas aplicaciones y portales web. Los archivos logs tienen la característica de poseer un gran tamaño y a la vez no tener una estructura rigurosamente definida. Esto ha causado que la organización no realice un eficiente procesamiento de estos datos, ya que las actuales tecnologías de bases de datos relaciones que posee no lo permiten. Por consiguiente, en este proyecto de tesis se buscó diseñar, desarrollar, implementar y validar métodos que sean capaces de procesar eficientemente estos archivos de logs con el objetivo de responder preguntas de negocio que entreguen valor a la compañía. La tecnología Big Data utilizada fue Cloudera, la que se encuentra en el marco que la organización exige, como por ejemplo: Que tenga soporte en el país, que esté dentro de presupuesto del año, etc. De igual forma, Cloudera es líder en el mercado de soluciones Big Data de código abierto, lo cual entrega seguridad y confianza de estar trabajando sobre una herramienta de calidad. Los métodos desarrollados dentro de esta tecnología se basan en el framework de procesamiento MapReduce sobre un sistema de archivos distribuido HDFS. Este proyecto de tesis probó que los métodos implementados tienen la capacidad de escalar horizontalmente a medida que se le agregan nodos de procesamiento a la arquitectura, de forma que la organización tenga la seguridad que en el futuro, cuando los archivos de logs tengan un mayor volumen o una mayor velocidad de generación, la arquitectura seguirá entregando el mismo o mejor rendimiento de procesamiento, todo dependerá del número de nodos que se decidan incorporar.

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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 necessita ser explorada. Nesta tese, uma abordagem é proposta para analisar o comportamento do usuário de aplicações Web durante a navegação, explorando a mineração de logs de cliente, chamada RUM (em inglês, Real-time Usage Mining). Nesta abordagem, as ações do usuário são coletadas na interface da aplicação e processadas de forma síncrona. Assim, a RUM é capaz de detectar problemas de usabilidade e padrões de comportamento para o usuário ativo, enquanto ele navega na aplicação. A fim de facilitar a implantação, a RUM fornece um toolkit que permite à aplicação consumir informações sobre o comportamento do usuário. Usando o toolkit, os desenvolvedores podem codificar adaptações que são automaticamente disparadas em resposta aos dados fornecidos pelo toolkit. Experimentos foram realizados em diferentes web sites para demonstrar a eficiência da abordagem em apoiar adaptações na interface que aprimoram a experiência do usuário.
Currently, there are more than 1 billion websites available. In this huge hyperspace, there are many websites that provide exactly the same content or service. Therefore, when the user does not find what she is looking for easily or she faces difficulties during the interaction, she tends to search for another website. In order to fullfil the needs and preferences of todays web users, adaptive websites have been proposed. Existing adaptation approaches usually adapt the content of pages according to the user interest. However, the adaptation of the interface structure in order to meet user needs and preferences is still incipient. In this thesis, an approach is proposed to analyze the user behavior of Web applications during navigation, exploring the mining of client logs, called RUM (Real-time Usage Mining). In this approach, user actions are collected in the applications interface and processed synchronously. Thus, RUM is able to detect usability problems and behavioral patterns for the current application user, while she is browsing the application. In order to facilitate its deployment, RUM provides a toolkit which allows the application to consume information about the user behavior. By using this toolkit, developers are able to code adaptations that are automatically triggered in response to the data provided by the toolkit. Experiments were conducted on different websites to demonstrate the efficiency of the approach in order to support interface adaptations that improve the user experience.

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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 pratiques des internautes. Dans cette thèse, nous apportons deux contributions importantes pour un processus WUM, implémentées dans notre boîte à outils Axislogminer. D’abord, nous proposons une méthodologie générale de prétraitement des logs Web dont l’originalité consiste dans le fait qu’elle prend en compte l’aspect multi-sites du WUM. Nous proposons dans notre méthodologie quatre étapes distinctes : la fusion des fichiers logs, le nettoyage, la structuration et l’agrégation des données. Notre deuxième contribution vise à la découverte à partir d’un fichier log prétraité de grande taille, des comportements minoritaires correspondant à des motifs séquentiels de très faible support. Pour cela, nous proposons une méthodologie générale visant à diviser le fichier log prétraité en sous-logs, se déclinant selon trois approches d’extraction de motifs séquentiels au support faible (séquentielle, itérative et hiérarchique). Celles-ci ont été implémentées dans des méthodes concrètes hybrides mettant en jeu des algorithmes de classification et d’extraction de motifs séquentiels
The Web use mining (WUM) is a rather research field and it corresponds to the process of knowledge discovery from databases (KDD) applied to the Web usage data. It comprises three main stages : the pre-processing of raw data, the discovery of schemas and the analysis (or interpretation) of results. The quantity of the web usage data to be analysed and its low quality (in particular the absence of structure) are the principal problems in WUM. When applied to these data, the classic algorithms of data mining, generally, give disappointing results in terms of behaviours of the Web sites users (E. G. Obvious sequential patterns, stripped of interest). In this thesis, we bring two significant contributions for a WUM process, both implemented in our toolbox, the Axislogminer. First, we propose a complete methodology for pre-processing the Web logs whose originality consists in its intersites aspect. We propose in our methodology four distinct steps : the data fusion, data cleaning, data structuration and data summarization. Our second contribution aims at discovering from a large pre-processed log file the minority behaviours corresponding to the sequential patterns with low support. For that, we propose a general methodology aiming at dividing the pre-processed log file into a series of sub-logs. Based on this methodology, we designed three approaches for extracting sequential patterns with low support (the sequential, iterative and hierarchical approaches). These approaches we implemented in hybrid concrete methods using algorithms of clustering and sequential pattern mining

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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 investigated. Traditional methods, including questionnaires and interviews, have been used to study the usage of annual reports. This study explores and applies a novel method, Web Server Log Analysis, to study how online annual reports are accessed by users. The study investigated the Web Server Log Files of six companies and found that the most accessed sections of online annual reports are the Notes to the Financial Statements, the Chairman Statement, and the Profit and Loss Account. Narrative sections were accessed more frequently compared to the financial sections of the annual report. In addition, the non-statutory sections were found to be more accessed by users compared to the statutory ones.

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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'extraction des connaissances à partir des données (ECD) appliqué aux données d'usage sur le Web. Il comporte trois étapes principales : le prétraitement des données, la découverte des schémas et l'analyse (ou l'interprétation) des résultats. Un processus WUM extrait des patrons de comportement à partir des données d'usage et, éventuellement, à partir d'informations sur le site (structure et contenu) et sur les utilisateurs du site (profils). 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 appliqués sur ces données donnent généralement des résultats décevants en termes de pratiques des internautes (par exemple des patrons séquentiels évidents, dénués d'intérêt). Dans cette thèse, nous apportons deux contributions importantes pour un processus WUM, implémentées dans notre bo^³te à outils AxisLogMiner. Nous proposons une méthodologie générale de prétraitement des logs Web et une méthodologie générale divisive avec trois approches (ainsi que des méthodes concrètes associées) pour la découverte des motifs séquentiels ayant un faible support. Notre première contribution concerne le prétraitement des données d'usage Web, domaine encore très peu abordé dans la littérature. L'originalité de la méthodologie de prétraitement proposée consiste dans le fait qu'elle prend en compte l'aspect multi-sites du WUM, indispensable pour appréhender les pratiques des internautes qui naviguent de fa»con transparente, par exemple, sur plusieurs sites Web d'une même organisation. Outre l'intégration des principaux travaux existants sur ce thème, nous proposons dans notre méthodologie quatre étapes distinctes : la fusion des fichiers logs, le nettoyage, la structuration et l'agrégation des données. En particulier, nous proposons plusieurs heuristiques pour le nettoyage des robots Web, des variables agrégées décrivant les sessions et les visites, ainsi que l'enregistrement de ces données dans un modèle relationnel. Plusieurs expérimentations ont été réalisées, montrant que notre méthodologie permet une forte réduction (jusqu'à 10 fois) du nombre des requêtes initiales et offre des logs structurés plus riches pour l'étape suivante de fouille de données. Notre deuxième contribution vise la découverte à partir d'un fichier log prétraité de grande taille, des comportements minoritaires correspondant à des motifs séquentiels de très faible support. Pour cela, nous proposons une méthodologie générale visant à diviser le fichier log prétraité en sous-logs, se déclinant selon trois approches d'extraction de motifs séquentiels au support faible (Séquentielle, Itérative et Hiérarchique). Celles-ci ont été implémentées dans des méthodes concrètes hybrides mettant en jeu des algorithmes de classification et d'extraction de motifs séquentiels. Plusieurs expérimentations, réalisées sur des logs issus de sites académiques, nous ont permis de découvrir des motifs séquentiels intéressants ayant un support très faible, dont la découverte par un algorithme classique de type Apriori était impossible. Enfin, nous proposons une boite à outils appelée AxisLogMiner, qui supporte notre méthodologie de prétraitement et, actuellement, deux méthodes concrètes hybrides pour la découverte des motifs séquentiels en WUM. Cette boite à outils a donné lieu à de nombreux prétraitements de fichiers logs et aussi à des expérimentations avec nos méthodes implémentées.

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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.
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. New York, NY: 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. [S.l.]: Future Now, Inc., 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. 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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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, 247–58. Boston, MA: 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, 847–54. Wiesbaden: 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, 991–99. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-21308-4_70.

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Labbaci, Hamza, Brahim Medjahed, and Youcef Aklouf. "Learning Interactions from Web Service Logs." In Lecture Notes in Computer Science, 275–89. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64471-4_22.

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Yang, Qiang, Charles X. Ling, and Jianfeng Gao. "Mining Web Logs for Actionable Knowledge." In Intelligent Technologies for Information Analysis, 169–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07952-2_8.

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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, 483–92. Berlin, Heidelberg: 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, Xuan Jiang, Jun He, Xiaoyong Du, and Zhixu Li. "Detecting Hot Events from Web Search Logs." In Web-Age Information Management, 417–28. Berlin, Heidelberg: 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, 533–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24655-8_58.

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Sun, Hui, Jianhua Sun, and Hao Chen. "Mining Frequent Attack Sequence in Web Logs." In Green, Pervasive, and Cloud Computing, 243–60. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39077-2_16.

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Billerbeck, Bodo, Gianluca Demartini, Claudiu S. Firan, Tereza Iofciu, and Ralf Krestel. "Ranking Entities Using Web Search Query Logs." In Research and Advanced Technology for Digital Libraries, 273–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15464-5_28.

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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. New York, New York, USA: 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. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1858378.1858435.

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Hassan, Muhammad Umair, Kamran Shaukat, Dongmie Niu, Sundas Mahreen, Yingjun Ma, Xiuyang Zhao, and Muhammad Ahmad Shabir. "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. New York, New York, USA: 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. New York, New York, USA: 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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Goel, Neha, and C. K. Jha. "Preprocessing web logs: A critical phase in web usage mining." In 2015 International Conference on Advances in Computer Engineering and Applications (ICACEA). IEEE, 2015. http://dx.doi.org/10.1109/icacea.2015.7164776.

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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. Calgary,AB,Canada: ACTAPRESS, 2014. http://dx.doi.org/10.2316/p.2014.810-032.

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Reports on the topic "Web-logs"

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

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