Bibliografie tematiche / Search engine

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Letteratura scientifica selezionata sul tema "Search engine"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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Articoli di riviste sul tema "Search engine"

MINASYAN, Vladimir. "Search Engine Friendliness of Search Engine Marketing Methods." Journal of Business 3, no. 1 (2014): 47–51. http://dx.doi.org/10.31578/job.v3i1.80.

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The article examines search engine friendliness of the most popular and widely used search engine marketing (SEM) methods. In the first part, SEM methods and approaches that are in line with search engines’ official guidelines are examined and analyzed. The second part deals with popular methods which are not welcomed by search engines and are implemented by companies solely for search engine rankings manipulation purposes. Finally, the last section describes several experiments conducted on live websites in order to figure out modern search engines abilities to identify widely used deceptive

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D.Minnie and S.Srinivasan. "META SEARCH ENGINE WITH AN INTELLIGENT INTERFACE FOR INFORMATION RETRIEVAL ON MULTIPLE DOMAINS." International Journal of Computer Science, Engineering and Information Technology (IJCSEIT) 1, no. 4 (2011): 37–45. https://doi.org/10.5281/zenodo.3542608.

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This paper analyses the features of Web Search Engines, Vertical Search Engines, Meta Search Engines, and proposes a Meta Search Engine for searching and retrieving documents on Multiple Domains in the World Wide Web (WWW). A web search engine searches for information in WWW. A Vertical Search provides the user with results for queries on that domain. Meta Search Engines send the user’s search queries to various search engines and combine the search results. This paper introduces intelligent user interfaces for selecting domain, category and search engines for the proposed Multi-Domain M

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Manjula, D., and T. V. Geetha. "Semantic Search Engine." Journal of Information & Knowledge Management 03, no. 01 (2004): 107–17. http://dx.doi.org/10.1142/s0219649204000729.

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Currently existing search engines index documents only by words and as a result, when a query can be interpreted in different senses, the irrelevant results are obtained in the midst of relevant results. A semantic search engine is proposed here which indexes documents both by words and senses and as a result tries to avoid the irrelevant results. The "crawler" traverses the worldwide web and the normalized documents are sent to the disambiguator module, which identifies the top few sense(s) of ambiguous words by employing a weighted disambiguation algorithm. The documents are then indexed by

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Jha, Radhika. "Xperia Search Engine." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 6331–40. http://dx.doi.org/10.22214/ijraset.2023.52996.

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Abstract: This research paper delves into the innerworkings of search engines and introduces Xperia, a personalized search engine aimed at enhancing information retrieval. The paper explores the fundamentaltechnologies employed by search engines, tracing their evolution and growth over time. It presents the development and implementation of Xperia, highlighting its unique feature set that goes beyond traditional search engines by providing users with not only relevant resourcelinks but also extracted information from various web sources. The paper begins with an introduction, providing the bac

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Lee, Sungin, Wonhong Jang, Eunsol Lee, and Sam G. Oh. "Search engine optimization." Library Hi Tech 34, no. 2 (2016): 197–206. http://dx.doi.org/10.1108/lht-02-2016-0014.

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Purpose – The purpose of this paper is to examine the effect of, and identify core techniques of, search engine optimization (SEO) techniques applied to the web (http://lg-sl.net) and mobile (http//m.lg-sl.net) Science Land content and services at LG Sangnam Library in Korea. Design/methodology/approach – In accordance with three major SEO guidelines, ten SEO techniques were identified and applied, and their implications were extracted on three areas: improved search engine accessibility, increased relevance between site content and search engine keywords, and improved site credibility. The ef

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Li, Yi, Zhihui Yuan, Yujie Li, and Jing Liu. "Factors influencing search engine usage behavior." Social Behavior and Personality: an international journal 46, no. 1 (2018): 1–10. http://dx.doi.org/10.2224/sbp.6211.

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We analyzed the effect of individual factors, contextual factors, and perception of search engine advertising on users' search engine usage behavior. The sample comprised 404 Chinese who used search engines in the context of their paid employment. Results showed that (a) perceived search skills and perceived search engine reliance significantly and positively impacted users' general search engine usage, (b) perceived advertising clutter reduced the beneficial effects of perceived search skills on users' general search engine usage, (c) users with higher perceived search engine reliance preferr

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Goyal, Harsh, and Komal kapoor. "Search Engine Optimization with Google." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 496–501. http://dx.doi.org/10.22214/ijraset.2022.47904.

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Abstract: This paper is a finished survey that how various strategies of Search Engine Optimization (SEO) can help to improve product marketing. Search engines have now become an important channel for increasing SMEs’ global reach and helps companies to compete with other and large companies. SMEs are using search engine optimization to improve their online visibility as a result (SEO). With the help of Search Engine Optimization small companies can actually now compete with the large companies and can appear ahead of large. The main objective of every website to list at the top of all thelink

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Takama, Yasufumi, Yanjun Zhu, Shogo Kori, Koichi Yamaguchi, Lieu-Hen Chen, and Hiroshi Ishikawa. "Design of Context Search Engine Based on Analysis of User’s Search Intentions." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 6 (2016): 910–18. http://dx.doi.org/10.20965/jaciii.2016.p0910.

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The context search engine has been studied for answering trend-related queries. As trend information is obtained from temporal data, which is common in many applications, the context engine is expected to be available regardless of domains. When using existing search engines, it is supposed that users submit a series of queries based on search intention. Therefore, search functions of the context search engine should be designed based on the user’s potential search intention. To analyze user’s behavior in information retrieval, this paper conducted experiments using existing Web search engines

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de Cornière, Alexandre. "Search Advertising." American Economic Journal: Microeconomics 8, no. 3 (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, depe

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Stevenson, Valerie. "Search Engine Update." Legal Information Management 1, no. 3 (2001): 28–31. http://dx.doi.org/10.1017/s1472669600000566.

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Looking back to 1999, there were a number of search engines which performed equally well. I recommended defining the search strategy very carefully, using Boolean logic and field search techniques, and always running the search in more than one search engine. Numerous articles and Web columns comparing the performance of different search engines came to different conclusions on the ‘best’ search engines. Over the last year, however, all the speakers at conferences and seminars I have attended have recommended Google as their preferred tool for locating all kinds of information on the Web. I co

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Tesi sul tema "Search engine"

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 a

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Fahlström, Kamilla, and Caroline Jensen. "Search Engine Marketing in SMEs : The motivations behind using search engine marketing." Thesis, Högskolan i Gävle, Avdelningen för ekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-21116.

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Abstract Title: Search Engine Marketing in SMEs Level: Final assignment for Bachelor Degree in Business Administration Authors: Kamilla Fahlström & Caroline Jensen Supervisor: Jens Eklinder Frick Date: 2016 January Purpose: The purpose of this study is to use Expectancy theory to describe and analyze small company owners’ motivations for their usage of Search Engine Marketing, in terms of their perceived Valence, Expectancy and Instrumentality. Method: To research the aim of this study a qualitative research approach was used. The empirical data was compiled through ten semi-structured i

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Hurlock, Jonathan. "Twitter search : building a useful search engine." Thesis, Swansea University, 2015. https://cronfa.swan.ac.uk/Record/cronfa43037.

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Millions of digital communications are posted over social media every day. Whilst some state that a large proportion of these posts are considered to be babble, we know that some of these posts actually contain useful information. In this thesis we specifically look at how we can identify reasons as to what makes some of these communications useful or not useful to someone searching for information over social media. In particular we look at what makes messages (tweets) from the social network Twitter useful or not useful users performing search over a corpus of tweets. We identify 16 features

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Narayan, Nitesh. "Advanced Intranet Search Engine." Thesis, Mälardalen University, School of Innovation, Design and Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-9408.

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<p>Information retrieval has been a prevasive part of human society since its existence.With the advent of internet and World wide Web it became an extensive area of researchand major foucs, which lead to development of various search engines to locate the de-sired information, mostly for globally connected computer networks viz. internet.Butthere is another major part of computer network viz. intranet, which has not seen muchof advancement in information retrieval approaches, in spite of being a major source ofinformation within a large number of organizations.Most common technique for intran

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King, John D. "Search engine content analysis." Queensland University of Technology, 2008. http://eprints.qut.edu.au/26241/.

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Search engines have forever changed the way people access and discover knowledge, allowing information about almost any subject to be quickly and easily retrieved within seconds. As increasingly more material becomes available electronically the influence of search engines on our lives will continue to grow. This presents the problem of how to find what information is contained in each search engine, what bias a search engine may have, and how to select the best search engine for a particular information need. This research introduces a new method, search engine content analysis, in order to s

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Edlund, Joakim. "Cognitive Search Engine Optimization." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281882.

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The use of search engines is a common way to navigate through information today. The field of information retrieval is the field of finding documents in large unstructured collections. Within this field there are widely researched baseline solutions to solve this problem. There are also more advanced techniques (often based on machine learning) to improve relevant results further. However, picking the right algorithm or technique when implementing a search engine is no trivial task and deciding which performs better might seem hard. This project takes a commonly used baseline search engine imp

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King, John Douglas. "Search engine content analysis." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/26241/1/John_King_Thesis.pdf.

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Khan, Saiful. "Visualization assisted enterprise search engine." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:d1790b99-c30e-487b-b87e-98d4e3a8b2bb.

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In most organizations, the number of files increases at a rate similar to the growth of data. As one of the big data challenges, many enterprises encounter a common difficulty in a routine operation, that is, finding files in a large-scale file system typically distributed across several physical sites and accessed by thousands of users. This thesis addresses a central question: whether or not visualization techniques can be used to improve the effectiveness and efficiency in performing numerous file searching operations at an industrial scale. All work conducted in this research was done in p

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Slavík, Michal. "Search Engine Marketing neziskových organizací." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-124602.

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The goal of this thesis is to design methodics for Search Engine Marketing (SEM) in nonprofit organizations (NPOs) which takes advantage of their specifics. Other goals include practical evaluation of the methodics and analysis of the current state of NPOs websites. Determined goals are reached by merging theoretical background from relevant literature with the knowledge gained during field research and with author's experience. Designed methodics is built on the following hypotheses: NPOs are able to negotiate better trade terms than trading companies, NPOs can delegate their volunteers to do

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FISTER, JUSTIN M. "CORRELATION ANALYSIS OF ON-PAGE ATTRIBUTES AND SEARCH ENGINE RANKINGS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1178730597.

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Più fonti

Libri sul tema "Search engine"

Grappone, Jennifer. Search Engine Optimization. John Wiley & Sons, Ltd., 2008.

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Ramos, Andreas. Search Engine Marketing. McGraw-Hill, 2008.

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Susan, Esparza, and Clay, eds. Search Engine Optimization. For Dummies [Imprint], 2009.

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Stephanie, Cota, ed. Search engine marketing. McGraw-Hill, 2009.

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com, Did-it. 101 search engine secrets: "your guide to search engine placement". did-it.com, Inc., 1998.

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Kennedy, Anne. Global search engine marketing: Fine-tuning your international search engine results. Que, 2012.

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Kent, Peter. Search engine optimization for dummies. 4th ed. Wiley Publishing, Inc., 2011.

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Odom, Sean. SEO for 2010: Search engine optimization secrets. MediaWork Pub., 2010.

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Kent, Peter. Search Engine Optimization For Dummies. John Wiley & Sons, Ltd., 2004.

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Kelsey, Todd. Introduction to Search Engine Optimization. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2851-7.

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Capitoli di libri sul tema "Search engine"

Rennie, Frank, and Keith Smyth. "Search engine." In Digital Learning: The Key Concepts, 2nd ed. Routledge, 2019. http://dx.doi.org/10.4324/9780429425240-167.

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Weik, Martin H. "search engine." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_16727.

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Das, Subhankar. "Search Engine Algorithm and Search Engine Marketing." In Search Engine Optimization and Marketing. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429298509-6.

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Thelwall, Michael. "Automatic Search Engine Searches: LexiURL Searcher." In Introduction to Webometrics: Quantitative Web Research for the Social Sciences. Springer International Publishing, 2009. http://dx.doi.org/10.1007/978-3-031-02261-6_5.

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Goldman, E. "Search Engine Bias and the Demise of Search Engine Utopianism." In Web Search. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75829-7_8.

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Lieberam-Schmidt, Sönke. "Search Engine Optimization." In Analyzing and Influencing Search Engine Results. Gabler, 2010. http://dx.doi.org/10.1007/978-3-8349-8915-4_5.

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Carterette, Ben. "Search Engine Metrics." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_325-2.

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Van Looy, Amy. "Search Engine Optimization." In Social Media Management. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-21990-5_6.

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Li, Mingjing, and Wei-Ying Ma. "Image Search Engine." In Encyclopedia of Multimedia. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-78414-4_81.

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West, Adrian W. "Search Engine Optimization." In Practical Web Design for Absolute Beginners. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1993-5_19.

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Atti di convegni sul tema "Search engine"

Modi, Sangita S., and Sujata R. Shahabade. "Search Engine Based Recommendation Engine Using Hybrid Model." In 2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA). IEEE, 2024. https://doi.org/10.1109/icisaa62385.2024.10828885.

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Kumar, JMSV Ravi, Bellaganti Dheeraj, Geddada Gowtham, Dokala Vivek Babu, Bonthu Asha Kiran, and M. Srikanth. "Image Search Engine with Recognition." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933116.

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Arkhipova, Olga, Lidia Grauer, Igor Kuralenok, and Pavel Serdyukov. "Search Engine Evaluation based on Search Engine Switching Prediction." In SIGIR '15: The 38th International ACM SIGIR conference on research and development in Information Retrieval. ACM, 2015. http://dx.doi.org/10.1145/2766462.2767786.

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Knok, Zeljko, and Mark Marcec. "Universtiy search engine." In 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). IEEE, 2016. http://dx.doi.org/10.1109/mipro.2016.7522267.

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Zhang, Jie, Xiong Zhang, and Wei Zhang. "Microseismic search engine." In SEG Technical Program Expanded Abstracts 2013. Society of Exploration Geophysicists, 2013. http://dx.doi.org/10.1190/segam2013-1277.1.

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Aldweik, Ghadeer, Saadia Malik, Abrar Almuhammidi, Wejdan Alyoubi, Ahad Alsulami, and Hind Al-Oufi. "PHARMACEUTICAL SEARCH ENGINE." In International Conference on e-Society 2020. IADIS Press, 2020. http://dx.doi.org/10.33965/es2020_202005l011.

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Kamath, Abhirup, Siddhesh Menon, Archita Poddar, Vijay Katkar, and Savita Lohiya. "Programmer's Search Engine." In 2019 International Conference on Advances in Computing, Communication and Control (ICAC3). IEEE, 2019. http://dx.doi.org/10.1109/icac347590.2019.9036840.

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Rajkumar, N., B. Gohin, and V. Vinod. "Search engine: intelligent web service search." In IET Chennai 3rd International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2012). Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/cp.2012.2183.

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Fagroud, Fatima Zahra, El Habib Ben Lahmar, Mohamed Amine, Hicham Toumi, and Sanaa El Filali. "What does mean search engine for IOT or IOT search engine." In BDIoT'19: The 4th International Conference On Big Data and Internet of Things. ACM, 2019. http://dx.doi.org/10.1145/3372938.3372958.

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Kumar, Rajesh, Sunil Kumar Singh, and Virendra Kumar. "A heuristic approach for search engine selection in meta-search engine." In 2015 International Conference on Computing, Communication & Automation (ICCCA). IEEE, 2015. http://dx.doi.org/10.1109/ccaa.2015.7148496.

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Rapporti di organizzazioni sul tema "Search engine"

Aldrich, Susan. Northern Light Enterprise Search Engine V.3.0. Patricia Seybold Group, 2005. http://dx.doi.org/10.1571/pr6-23-05cc.

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Aldrich, Susan. Who Needs a Premium Product Search Engine? Patricia Seybold Group, 2004. http://dx.doi.org/10.1571/psgp1-9-04cc.

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Aldrich, Susan. Five Steps in Selecting a Search Engine. Patricia Seybold Group, 2008. http://dx.doi.org/10.1571/psgp10-23-08cc.

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Seacord, Robert C., Scott A. Hissam, and Kurt C. Wallnau. Agora: A Search Engine for Software Components. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada351653.

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Gagarin, A. YU, S. I. Kazakov, and V. E. Ovsyannikov. Information search engine «Technology of mechanical engineering». OFERNIO, 2023. http://dx.doi.org/10.12731/ofernio.2023.25142.

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Ingram, William, and Evelyn Gray. A Search Engine for Federal Standard 1037C. Institute for Telecommunication Sciences, 1999. https://doi.org/10.70220/5gs1g1mx.

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Aldrich, Susan. Search Engine Marketing and Optimization Wisdom V.2003. Patricia Seybold Group, 2003. http://dx.doi.org/10.1571/psgp12-19-03cc.

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Adams, Paige, Pranav Anand, Grant Gehrke, et al. ReSEARCH: A Requirements Search Engine: Progress Report 2. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada529465.

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Ramos, Lionel R. Hardware Realization of an Ethernet Packet Analyzer Search Engine. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada392128.

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Mager, Astrid, ed. Search engine imaginary - Visions and values in the co-production of search technology and Europe. Self, 2018. http://dx.doi.org/10.1553/ita-pa-am-2017.

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