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Academic literature on the topic 'Search engines'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Search engines"

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Saraygord Afshari, Sajad, Bhargava Jonnadula, Xiangyang Xu, Xihui Liang, and Zhaohui Yang. "Jet Engine Optimal Preventive Maintenance Scheduling Using Golden Section Search and Genetic Algorithm." Journal of Prognostics and Health Management 2, no. 1 (July 13, 2022): 45–71. http://dx.doi.org/10.22215/jphm.v2i1.3321.

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Jet engines are critical assets in aircraft, and their availability is crucial in the modern aircraft industry. Therefore, their maintenance scheduling is one of the major tasks an airline has to make during an engine’s lifetime. A proper engine maintenance schedule can significantly reduce maintenance costs without compromising the aircraft's reliability and safety. Different maintenance scheduling approaches have been used for jet engines, such as corrective, preventive, and predictive maintenance strategies. Regarding the safety demands in aircraft industries, preventive maintenance is a frequent maintenance method for jet engines. However, preventive maintenance schedules are often use fixed maintenance intervals, which is usually suboptimal. This paper focuses on minimizing a jet engine's overall maintenance cost by optimizing its preventive maintenance schedule based on an engine’s comprehensive reliability model. A hierarchical optimization framework including the golden section search and genetic algorithms is applied to find the optimal set of preventive maintenance number and their times and the components to be replaced at those times during the jet engine's overall lifetime. The Monte Carlo simulation is used to estimate the engine’s failure times using their lifetime distributions from the reliability model. The estimated failure times are then used to determine the engine's overall corrective and preventive maintenance costs during its lifetime. Finally, an optimal preventive maintenance schedule is proposed for an RB 211 jet engine using the presented method. In the end, comparing the proposed method's overall maintenance cost with two other maintenance methods demonstrates the proposed schedule's effectiveness. The method presented in this paper is generic, and it can be used for other similar engineering systems.
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Seymour, Tom, Dean Frantsvog, and Satheesh Kumar. "History Of Search Engines." International Journal of Management & Information Systems (IJMIS) 15, no. 4 (September 12, 2011): 47. http://dx.doi.org/10.19030/ijmis.v15i4.5799.

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As the number of sites on the Web increased in the mid-to-late 90s, search engines started appearing to help people find information quickly. Search engines developed business models to finance their services, such as pay per click programs offered by Open Text in 1996 and then Goto.com in 1998. Goto.com later changed its name to Overture in 2001, and was purchased by Yahoo! in 2003, and now offers paid search opportunities for advertisers through Yahoo! Search Marketing. Google also began to offer advertisements on search results pages in 2000 through the Google Ad Words program. By 2007, pay-per-click programs proved to be primary money-makers for search engines. In a market dominated by Google, in 2009 Yahoo! and Microsoft announced the intention to forge an alliance. The Yahoo! & Microsoft Search Alliance eventually received approval from regulators in the US and Europe in February 2010. Search engine optimization consultants expanded their offerings to help businesses learn about and use the advertising opportunities offered by search engines, and new agencies focusing primarily upon marketing and advertising through search engines emerged. The term "Search Engine Marketing" was proposed by Danny Sullivan in 2001 to cover the spectrum of activities involved in performing SEO, managing paid listings at the search engines, submitting sites to directories, and developing online marketing strategies for businesses, organizations, and individuals. Some of the latest theoretical advances include Search Engine Marketing Management (SEMM). SEMM relates to activities including SEO but focuses on return on investment (ROI) management instead of relevant traffic building (as is the case of mainstream SEO). SEMM also integrates organic SEO, trying to achieve top ranking without using paid means of achieving top in search engines, and PayPerClick SEO. For example some of the attention is placed on the web page layout design and how content and information is displayed to the website visitor.
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Kumar, Kailash, and Abdulaziz Al-Besher. "Critical Analysis of Major Search Engines." Journal of Computational and Theoretical Nanoscience 16, no. 9 (September 1, 2019): 3712–16. http://dx.doi.org/10.1166/jctn.2019.8239.

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This paper examines the overlapping of the results retrieved between three major search engines namely Google, Yahoo and Bing. A rigorous analysis of overlap among these search engines was conducted on 100 random queries. The overlap of first ten web page results, i.e., hundred results from each search engine and only non-sponsored results from these above major search engines were taken into consideration. Search engines have their own frequency of updates and ranking of results based on their relevance. Moreover, sponsored search advertisers are different for different search engines. Single search engine cannot index all Web pages. In this research paper, the overlapping analysis of the results were carried out between October 1, 2018 to October 31, 2018 among these major search engines namely, Google, Yahoo and Bing. A framework is built in Java to analyze the overlap among these search engines. This framework eliminates the common results and merges them in a unified list. It also uses the ranking algorithm to re-rank the search engine results and displays it back to the user.
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de Cornière, Alexandre. "Search Advertising." American Economic Journal: Microeconomics 8, no. 3 (August 1, 2016): 156–88. http://dx.doi.org/10.1257/mic.20130138.

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Search engines enable advertisers to target consumers based on the query they have entered. In a framework in which consumers search sequentially after having entered a query, I show that such targeting reduces search costs, improves matches and intensifies price competition. However, a profit-maximizing monopolistic search engine imposes a distortion by charging too high an advertising fee, which may negate the benefits of targeting. The search engine also has incentives to provide a suboptimal quality of sponsored links. Competition among search engines can increase or decrease welfare, depending on the extent of multi-homing by advertisers. (JEL D43, D83, L13, L86, M37)
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Zuo, Xiang Lin, Wen Bo Wang, Ying Wang, and Wan Li Zuo. "Research and Implementation of Improved Real-Time Crawler Modeling." Applied Mechanics and Materials 312 (February 2013): 791–95. http://dx.doi.org/10.4028/www.scientific.net/amm.312.791.

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The past decade has witnessed the rapid development of search engines, which has become an indispensable part of everyday life. However, people are no longer satisfied with accessing to ordinary information, and they may instead pay more attention to fresh information. This demand poses challenges to traditional search engines, which concern more about relevance and importance of web pages. A search engine compresses three modules: crawler, indexer and searcher. Changes are needed for all these three parts to improve search engine's freshness. This paper investigates the first part of search engine crawler, we analyze the requirements for real-time crawler, and propose a novel real-time crawler based on more accurate estimation of refresh time. Experimental results demonstrate that the proposed real-time crawler can help search engine improve its freshness.
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MARCHIORI, MASSIMO. "SOCIAL SEARCH ENGINES." International Journal of Bifurcation and Chaos 17, no. 07 (July 2007): 2355–61. http://dx.doi.org/10.1142/s0218127407018440.

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The web landscape has undergone massive changes in the past years. On the other hand, search engine technology has not quite kept the same pace. In this article we look at the current scenarios, and argue how social flows can be used to make up for a better generation of search engines. We consider how society and technological progress somehow changed the rules of the game, introducing good but also bad components, and see how this situation could be modeled by search engines. Along this line of thinking, we show how the real components of interest are not just web pages, but flows of information of any kind, that need to be merged: this opens up for a wide range of improvements and far-looking developments, towards a new horizon of social search.
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Dayanand, Dr C. D., and Syed M. S. Zain. "Search Engines in Biomedical Research." JOURNAL OF CLINICAL AND BIOMEDICAL SCIENCES 04, no. 1 (March 15, 2014): 259–60. http://dx.doi.org/10.58739/jcbs/v04i1.7.

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Bukhor, Saiful i. "Analisis Perbandingan Fitur Search Engine." INFORMAL: Informatics Journal 3, no. 1 (February 25, 2019): 17. http://dx.doi.org/10.19184/isj.v3i1.9850.

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Search engines are used in the web as a tool for information retrieval. Web Server is a large warehouse of heterogeneous and unstructured data so that to filter out relevant information from people, a search engine is needed. Search engines usually consist of page repositories, indexing modules, query modules and ranking modules. Search engines do not work alone, besides that there is a web browser that supports the work of this search engine to be more optimal. A browser is software that is run on a user's computer (user) that displays web documents or information taken from a web server [1]. A browser is the type of intermediary the user uses most often. This paper aims to analyze three search engines namely Google, Yahoo, and Bing based on existing features. These features include web search, image search, video search, news search, route search, book search, change search settings, display number of views, shopping, language translator. Google stands as the best search engine among all search engines, which works using the Page Rank algorithm. Page Rank is a numerical value that determines the importance of a web page by calculating the number of backlinks.
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Garner, Wendy. "Search engines." Nursing Standard 20, no. 22 (February 8, 2006): 67–68. http://dx.doi.org/10.7748/ns.20.22.67.s53.

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Al-Shahi, Rustam. "Search Engines." Practical Neurology 1, no. 1 (October 2001): 60–61. http://dx.doi.org/10.1046/j.1474-7766.2001.01012.x.

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Dissertations / Theses on the topic "Search engines"

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Blaauw, Pieter. "Search engine poisoning and its prevalence in modern search engines." Thesis, Rhodes University, 2013. http://hdl.handle.net/10962/d1002037.

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The prevalence of Search Engine Poisoning in trending topics and popular search terms on the web within search engines is investigated. Search Engine Poisoning is the act of manipulating search engines in order to display search results from websites infected with malware. Research done between February and August 2012, using both manual and automated techniques, shows us how easily the criminal element manages to insert malicious content into web pages related to popular search terms within search engines. In order to provide the reader with a clear overview and understanding of the motives and the methods of the operators of Search Engine Poisoning campaigns, an in-depth review of automated and semi-automated web exploit kits is done, as well as looking into the motives for running these campaigns. Three high profile case studies are examined, and the various Search Engine Poisoning campaigns associated with these case studies are discussed in detail to the reader. From February to August 2012, data was collected from the top trending topics on Google’s search engine along with the top listed sites related to these topics, and then passed through various automated tools to discover if these results have been infiltrated by the operators of Search Engine Poisoning campaings, and the results of these automated scans are then discussed in detail. During the research period, manual searching for Search Engine Poisoning campaigns was also done, using high profile news events and popular search terms. These results are analysed in detail to determine the methods of attack, the purpose of the attack and the parties behind it
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Shen, Yipeng. "Meta-search and distributed search systems /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?COMP%202002%20SHEN.

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Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2002.
Includes bibliographical references (leaves 138-144). Also available in electronic version. Access restricted to campus users.
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Natvig, Ola. "Compression in XML search engines." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9119.

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The structure of XML documents can be used by search engines to answer structured queries or to provide better relevancy. Several index structures exist for search in XML data. This study focuses on inverted lists with dictionary coded path types and dewey coded path instances. The dewey coded path index is large, but could be compressed. This study examines query processing with indexes encoded using well known integer coding methods VByte and PFor(delta) and methods tailored for the dewey index. Intersection queries and structural queries are evaluated. In addition to standard document level skipping, skip operations for path types are implemented and evaluated. Four extensions over plain PFor methods are proposed and tested. Path type sorting sorts dewey codes on their path types and store all deweys from one path type together. Column wise dewey storage stores the deweys in columns instead of rows. Prefix coding a well known method, is adapted to the column wise dewey storage, and dynamic column wise method chooses between row wise and column wise storage based on the compressed data. Experiments are performed on a XML collection based on Wikipedia. Queries are generated from the TREC 06 efficiency task query trace. Several different types of structural queries have been executed. Experiments show that column wise methods perform good on both intersection and structural queries. The dynamic column wise scheme is in most cases the best, and creates the smallest index. Special purpose skipping for path types makes some queries extremely fast and can be implemented with only limited storage footprint. The performance of in-memory search with multi-threaded query execution is limited by memory bandwidth.

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Mtshontshi, Lindiwe. "Evaluation and comparison of search engines." Thesis, Stellenbosch : Stellenbosch University, 2004. http://hdl.handle.net/10019.1/49955.

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Thesis (MPhil)--Stellenbosch University, 2004.
ENGLISH ABSTRACT: A growing body of studies is developing approaches to evaluate human interaction with Web search engines. Measuring the information retrieval effectiveness of World Wide Web search engines is costly because of the human relevance judgements involved. However, both for business enterprises and people it is important to know the most effective Web search engine, since such search engines help their users find a higher number of relevant Web pages with less effort. Furthermore, this information can be used for several practical purposes. This study does not attempt to describe all the currently available search engines, but provides a comparison of some, which are deemed to be among the most useful. It concentrates on search engines and their characteristics only. The goal is to help a new user get the most useful "hits" when using the various tools.
AFRIKAANSE OPSOMMING: Al hoe meer studies word gedoen om benaderings te ontwikkel vir die evaluasie van menslike interaksie met Web-soekenjins. Om te meet hoe effektief 'n soekenjin inligting op die Wêreldwye Web kan opspoor, is duur vanweë die mens se relevansiebeoordeling wat daarby betrokke is. Dit is egter belangrik dat die bestuurders van sake-ondememings en ander mense sal weet watter die mees doeltreffende soekenjins is, aangesien sulke soekenjins hulle gebruikers help om 'n hoër aantal relevante Webblaaie met minder inspanning te vind. Hierdie inligting kan ook gebruik word om 'n paar praktiese doelwitte te verwesenlik. Daar word nie gepoog om al die soekenjins wat tans beskikbaar is, te beskryf nie, maar sommige van die soekenjins wat as die nuttigste beskou word, word vergelyk. Daar word alleenlik op soekenjins en hulle kenmerke gekonsentreer. Die doel is om die nuwe gebruiker te help om die nuttigste inligting te verkry deur gebruik te maak van verskeie hulpmiddels.
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Zheng, Li. "Towards Next Generation Vertical Search Engines." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1517.

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As the Web evolves unexpectedly fast, information grows explosively. Useful resources become more and more difficult to find because of their dynamic and unstructured characteristics. A vertical search engine is designed and implemented towards a specific domain. Instead of processing the giant volume of miscellaneous information distributed in the Web, a vertical search engine targets at identifying relevant information in specific domains or topics and eventually provides users with up-to-date information, highly focused insights and actionable knowledge representation. As the mobile device gets more popular, the nature of the search is changing. So, acquiring information on a mobile device poses unique requirements on traditional search engines, which will potentially change every feature they used to have. To summarize, users are strongly expecting search engines that can satisfy their individual information needs, adapt their current situation, and present highly personalized search results. In my research, the next generation vertical search engine means to utilize and enrich existing domain information to close the loop of vertical search engine's system that mutually facilitate knowledge discovering, actionable information extraction, and user interests modeling and recommendation. I investigate three problems in which domain taxonomy plays an important role, including taxonomy generation using a vertical search engine, actionable information extraction based on domain taxonomy, and the use of ensemble taxonomy to catch user's interests. As the fundamental theory, ultra-metric, dendrogram, and hierarchical clustering are intensively discussed. Methods on taxonomy generation using my research on hierarchical clustering are developed. The related vertical search engine techniques are practically used in Disaster Management Domain. Especially, three disaster information management systems are developed and represented as real use cases of my research work.
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Mendoza, Rocha Marcelo Gabriel. "Query log mining in search engines." Tesis, Universidad de Chile, 2007. http://www.repositorio.uchile.cl/handle/2250/102877.

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Doctor en Ciencias, Mención Computación
La Web es un gran espacio de información donde muchos recursos como documentos, imágenes u otros contenidos multimediales pueden ser accesados. En este contexto, varias tecnologías de la información han sido desarrolladas para ayudar a los usuarios a satisfacer sus necesidades de búsqueda en la Web, y las más usadas de estas son los motores de búsqueda. Los motores de búsqueda permiten a los usuarios encontrar recursos formulando consultas y revisando una lista de respuestas. Uno de los principales desafíos para la comunidad de la Web es diseñar motores de búsqueda que permitan a los usuarios encontrar recursos semánticamente conectados con sus consultas. El gran tamaño de la Web y la vaguedad de los términos más comúnmente usados en la formulación de consultas es un gran obstáculo para lograr este objetivo. En esta tesis proponemos explorar las selecciones de los usuarios registradas en los logs de los motores de búsqueda para aprender cómo los usuarios buscan y también para diseñar algoritmos que permitan mejorar la precisión de las respuestas recomendadas a los usuarios. Comenzaremos explorando las propiedades de estos datos. Esta exploración nos permitirá determinar la naturaleza dispersa de estos datos. Además presentaremos modelos que nos ayudarán a entender cómo los usuarios buscan en los motores de búsqueda. Luego, exploraremos las selecciones de los usuarios para encontrar asociaciones útiles entre consultas registradas en los logs. Concentraremos los esfuerzos en el diseño de técnicas que permitirán a los usuarios encontrar mejores consultas que la consulta original. Como una aplicación, diseñaremos métodos de reformulación de consultas que ayudarán a los usuarios a encontrar términos más útiles mejorando la representación de sus necesidades. Usando términos de documentos construiremos representaciones vectoriales para consultas. Aplicando técnicas de clustering podremos determinar grupos de consultas similares. Usando estos grupos de consultas, introduciremos métodos para recomendación de consultas y documentos que nos permitirán mejorar la precisión de las recomendaciones. Finalmente, diseñaremos técnicas de clasificación de consultas que nos permitirán encontrar conceptos semánticamente relacionados con la consulta original. Para lograr esto, clasificaremos las consultas de los usuarios en directorios Web. Como una aplicación, introduciremos métodos para la manutención automática de los directorios.
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Zamurenko. "WEBSITE PROMOTION TECHNIQUES IN SEARCH ENGINES." Thesis, Київ 2018, 2018. http://er.nau.edu.ua/handle/NAU/33932.

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SHARMA, AMARENDRA KUMAR. "ESSAYS IN INTERNET ECONOMICS." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1028832870.

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Henriksson, Adam. "Alternative Search : From efficiency to experience." Thesis, Umeå universitet, Institutionen Designhögskolan, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-97836.

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Search engines of today are focusing on efficiently and accurately generating search results.Yet, there is much to be explored in the way people interact with the applications and relate to the content. Individuals are commonly unique, with complex preferences, motives and expectations. Not only is it important to be sensitive to these differences, but to accommodate the extremes. Enhancing a search engine does not only rely on technological development, but to explore potential user experiences in broader perspectives - which not only gratifies the needs for information, but supports a diversity of journeys. The aim of the project is to develop an alternate search engine with different functionality based on new values that reflects contemporary needs. The result, Exposeek, is an experiential prototype supporting exploratory browsing based on principles of distributed infrastructure, transparent computation and serendipitous information. Suggestive queries, legible algorithms and augmented results provide additional insights and present an alternative way to seek and peruse the Web.
Search Engines, Interaction Design
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Скиданенко, Максим Сергійович, Максим Сергеевич Скиданенко, Maksym Serhiiovych Skydanenko, and A. S. Skidanenko. "Visual search engines as a search tool in the learning process." Thesis, Сумський державний університет, 2012. http://essuir.sumdu.edu.ua/handle/123456789/29424.

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The main objective of the University is the promotion of successful professionals who have practical skills, can predict, model, process information, and integrate the knowledge obtained in a higher educational establishment. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/29424
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Books on the topic "Search engines"

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Search engines. London: Cassell, 2000.

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Haider, Jutta, and Olof Sundin. Invisible Search and Online Search Engines. Milton Park, Abingdon, Oxon ; New York : Routledge, 2019.: Routledge, 2019. http://dx.doi.org/10.4324/9780429448546.

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White, Martin. Enterprise search guidebook. Ashford, Middlesex, England: Free Pint, 2005.

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Gossen, Tatiana. Search Engines for Children. Wiesbaden: Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-658-12069-6.

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Search engine optimization. [Calif.]: O'Reilly, 2006.

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Odom, Sean. SEO for 2010: Search engine optimization secrets. [Tigard, OR ]: MediaWork Pub., 2010.

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Gibbon, David C. Introduction to video search engines. Berlin: Springer, 2008.

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Chang, Yi, and Hongbo Deng, eds. Query Understanding for Search Engines. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58334-7.

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Google and other search engines. Berkeley, CA: Peachpit Press, 2004.

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Chaters, Brent. Mastering search analytics. Sebastopol, CA: O'Reilly Media, 2012.

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Book chapters on the topic "Search engines"

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Shenoy, Aravind, and Anirudh Prabhu. "Search Engines." In Introducing SEO, 9–20. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1854-9_2.

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Ceri, Stefano, Alessandro Bozzon, Marco Brambilla, Emanuele Della Valle, Piero Fraternali, and Silvia Quarteroni. "Search Engines." In Web Information Retrieval, 71–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39314-3_6.

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Ferragina, Paolo, and Fabrizio Luccio. "Search Engines." In Computational Thinking, 111–27. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97940-3_9.

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Leung, Billy Tak Hoi, Jingzhen Xie, Linlin Geng, and Priscilla Nga Ian Pun. "Search Engines." In Transferring Information Literacy Practices, 163–74. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7743-3_7.

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Comer, Douglas E. "Internet Search (Search Engines)." In The Internet Book, 257–66. Fifth edition. | Boca Raton : Taylor & Francis, CRC Press, 2018.: Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9780429447358-25.

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Chang, George, Marcus J. Healey, James A. M. McHugh, and Jason T. L. Wang. "Multimedia Search Engines." In Mining the World Wide Web, 51–63. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1639-2_4.

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Thouvenin, Florent, Peter Hettich, Herbert Burkert, and Urs Gasser. "2 Search Engines." In Law, Governance and Technology Series, 59–71. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90230-2_4.

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Calsavara, Alcides, and Glauco Schmidt. "Semantic Search Engines." In Advanced Distributed Systems, 145–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25958-9_14.

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Ali-Ahmed, Syed Toufeeq. "Search Engines with Faceted Search." In Encyclopedia of Systems Biology, 1902–3. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_170.

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Hendry, D. G., and E. N. Efthimiadis. "Conceptual Models for Search Engines." In Web Search, 277–307. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75829-7_15.

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Conference papers on the topic "Search engines"

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Seyedarabi, Faezeh, and Rocio Calvo. "Search Engines." In DSAI 2016: 7th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/3019943.3019952.

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Dery, Hanan, Berkehan Ciftcioglu, Yang Song, Hui Wu, Michael Huang, Roland Kawakami, Jing Shi, Ilya Krivorotov, Igor Zutic, and Lu J. Sham. "Spintronics search engines." In 2011 69th Annual Device Research Conference (DRC). IEEE, 2011. http://dx.doi.org/10.1109/drc.2011.5994459.

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Snoek, Cees G. M., and Arnold W. M. Smeulders. "Video search engines." In the international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1873951.1874366.

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Pedersen, Jan O. "Internet search engines." In Special interest tracks and posters of the 14th international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1062745.1062770.

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Guha, Neel, and Matt Wytock. "Course-specific search engines." In the 22nd International Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2487788.2488158.

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Guha, Neel. "Course specific search engines." In the sixth international workshop. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2513204.2513216.

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Chang, Yun-Ke, Miguel A. Morales Arroyo, Amanda Spink, Myat Thu Aung, Kyaw Thu, Ya Lwin, and Zaw Win Htike. "Search Engines' Help Systems." In 2008 Fifth International Conference on Information Technology: New Generations (ITNG). IEEE, 2008. http://dx.doi.org/10.1109/itng.2008.219.

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Wu, Sun, Hsien-Tsung Chang, Ting-Chao Hsu, and Pei-Shin Liu. "Approximate Keyword Search in Web Search Engines." In 2006 1st International Conference on Digital Information Management. IEEE, 2007. http://dx.doi.org/10.1109/icdim.2007.369229.

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Khan, Javed Ahmad, Deepak Sangroha, Masroor Ahmad, and Md Tanzillur Rahman. "A performance evaluation of semantic based search engines and keyword based search engines." In 2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom). IEEE, 2014. http://dx.doi.org/10.1109/medcom.2014.7005997.

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Agrawal, Sanjay, Kaushik Chakrabarti, Surajit Chaudhuri, Venkatesh Ganti, Arnd Christian Konig, and Dong Xin. "Exploiting web search engines to search structured databases." In the 18th international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1526709.1526777.

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Reports on the topic "Search engines"

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Mager, Astrid, ed. Algorithmic Ideology. How capitalist society shapes search engines. Vienna: self, 2014. http://dx.doi.org/10.1553/ita-pa-am-12-3.

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Chiou, Lesley, and Catherine Tucker. Search Engines and Data Retention: Implications for Privacy and Antitrust. Cambridge, MA: National Bureau of Economic Research, September 2017. http://dx.doi.org/10.3386/w23815.

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Mager, Astrid, ed. In search of ideology. Socio-cultural dimensions of Google and alternative search engines (ITA-manu:script--02). Vienna: self, 2014. http://dx.doi.org/10.1553/ita-ms-13-02.

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Melnyk, Yuriy. IJSA IS PRESENTED IN THE INTERNATIONAL SCIENTOMETRIC DATABASES, REPOSITORIES AND SEARCH ENGINES. KRPOCH, 2018. http://dx.doi.org/10.26697/ijsa.1.3.

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Aldrich, Susan. Using Search Engines to Find New Customers at the American Institute of Physics. Boston, MA: Patricia Seybold Group, March 2004. http://dx.doi.org/10.1571/bp3-4-04cc.

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Mager, Astrid, ed. Search Engines Matter: From Educating Users Towards Engaging with Online Health Information Practices. Vienna: self, 2014. http://dx.doi.org/10.1553/ita-pa-am-12-1.

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Mager, Astrid, ed. Internet governance as joint effort - (Re)Ordering search engines at the intersection of global and local cultures. Vienna: self, 2018. http://dx.doi.org/10.1553/ita-pa-am-2018.

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Hernández-López, Luis Pablo, Miriam Romero-López, and Guillermo García-Quirante. Humor and social competence in High School and University education: a systematic review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2021. http://dx.doi.org/10.37766/inplasy2021.11.0033.

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Review question / Objective: Research question: What type of relationship exists between the use of humor and social competence, or any of their respective components, in post-compulsory education students? The aim of this paper was to conduct a systematic theoretical review of the relationship between humor and social competence in post-compulsory education students. Information sources: Electronic bibliographic databases of Psychology, Biomedical and Multidisciplinary Sciences, as well as the ProQuest search platform and the SCOPUS and Web of Science meta-search engines. The electronic databases used were MEDLINE, ProQuest Dissertations & Theses Global, Psychology Database, APA PsycArticles, APA PsycInfo, SCOPUS and Web of Science Core Collection. Platforms: ProQuest and the meta-search engines SCOPUS and Web of Science.
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MOSKALENKO, O. L., R. A. YASKEVICH, and E. V. KASPAROV. METABOLIC SYNDROME: PREVALENCE, DIAGNOSIS CRITERIA. Science and Innovation Center Publishing House, April 2022. http://dx.doi.org/10.12731/978-0-615-67340-0-3.

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This article presents a review of the literature and analyzes scientific research on the metabolic syndrome and diagnostic criteria. The authors conducted a scientific search using the relevant keywords in the PubMed and Google Scholar search engines, in the Scopus, Web of Science, MedLine, The Cochrane Library, EMBASE, Global Health, CyberLeninka, RSCI and others databases.
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MOSKALENKO, O. L., S. YU TERESHCHENKO, and E. V. KASPAROV. INTERNET ADDICTION: DIAGNOSIS CRITERIA AND METHODS. Science and Innovation Center Publishing House, April 2022. http://dx.doi.org/10.12731/978-0-615-67340-0-2.

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This article presents a review of the literature and analyzes scientific studies on the prevalence of Internet addiction in different countries. The authors conducted a scientific search using the relevant keywords in the PubMed and Google Scholar search engines, in the Scopus, Web of Science, MedLine, The Cochrane Library, EMBASE, Global Health, CyberLeninka, RSCI and others databases.
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