Relevant bibliographies by topics / „Google” search engine

Contents

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

Academic literature on the topic '„Google” search engine'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 April 2022

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Journal articles on the topic "„Google” search engine"

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Roberston, L. "Anecdotes [Google search engine]." IEEE Annals of the History of Computing 27, no. 3 (July 2005): 96–97. http://dx.doi.org/10.1109/mahc.2005.33.

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Gould, Paula. "Google adds scholarly search engine." Physics World 18, no. 1 (January 2005): 6. http://dx.doi.org/10.1088/2058-7058/18/1/9.

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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 confess that I have now abandoned most of my carefully worked out search strategies and comparison tests, and use Google for most of my own Web searches.
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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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Strzelecki, Artur. "Google Web and Image Search Visibility Data for Online Store." Data 4, no. 3 (August 22, 2019): 125. http://dx.doi.org/10.3390/data4030125.

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This data descriptor describes Google search engine visibility data. The visibility of a domain name in a search engine comes from search engine optimization and can be evaluated based on four data metrics and five data dimensions. The data metrics are the following: Clicks volume (1), impressions volume (2), click-through ratio (3), and ranking position (4). Data dimensions are as follows: queries that are entered into search engines that trigger results with the researched domain name (1), page URLs from research domains which are available in the search engine results page (2), country of origin of search engine visitors (3), type of device used for the search (4), and date of the search (5). Search engine visibility data were obtained from the Google search console for the international online store, which is visible in 240 countries and territories for a period of 15 months. The data contain 123 K clicks and 4.86 M impressions for the web search and 22 K clicks and 9.07 M impressions for the image search. The proposed method for obtaining data can be applied in any other area, not only in the e-commerce industry.
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Loan, Fayaz Ahmad, Umer Yousuf Parray, and Aasif Mohammad Khan. "Performance of search engines in harvesting grey literature." Library Hi Tech News 39, no. 2 (February 8, 2022): 21–23. http://dx.doi.org/10.1108/lhtn-12-2021-0094.

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Purpose The purpose of the study is to examine how far search engines have succeeded in retrieving electronic theses and dissertations (ETDs) using different search techniques and approaches. This study also compared the search results of search engines to identify the most suitable search engine for searching and retrieving the ETDs. Design/methodology/approach Fifty ETDs have been selected using systematic random sampling techniques from Shodhganga (a reservoir of Indian theses). These ETDs have been searched through prominent search engines such as Google, Google Scholar, Yahoo and Open Access Theses and Dissertation (OATD) using different search approaches and techniques. Findings The overall performance of the search engines revealed that Google retrieved most of the ETDs (89%) followed by OATD (88.5%), Yahoo (78%) and Google Scholar (76%), respectively. Originality/value The results have brought into light interesting findings regarding search engines in retrieving grey literature.
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Manjula, D., and T. V. Geetha. "Semantic Search Engine." Journal of Information & Knowledge Management 03, no. 01 (March 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 the words and the senses. The query is also disambiguated in a similar manner and retrieval is performed by matching both the sense and the word. The performance of the semantic search engine is compared against traditional word based indexing and also against the commercial search engines like Google, Yahoo, Hotbot and Lycos. The results show an impressive precision for the semantic search engine compared to other engines, particularly for ambiguous queries.
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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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Zhao, Yiming, Jin Zhang, Xue Xia, and Taowen Le. "Evaluation of Google question-answering quality." Library Hi Tech 37, no. 2 (June 17, 2019): 312–28. http://dx.doi.org/10.1108/lht-10-2017-0218.

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Purpose The purpose of this paper is to evaluate Google question-answering (QA) quality. Design/methodology/approach Given the large variety and complexity of Google answer boxes in search result pages, existing evaluation criteria for both search engines and QA systems seemed unsuitable. This study developed an evaluation criteria system for the evaluation of Google QA quality by coding and analyzing search results of questions from a representative question set. The study then evaluated Google’s overall QA quality as well as QA quality across four target types and across six question types, using the newly developed criteria system. ANOVA and Tukey tests were used to compare QA quality among different target types and question types. Findings It was found that Google provided significantly higher-quality answers to person-related questions than to thing-related, event-related and organization-related questions. Google also provided significantly higher-quality answers to where- questions than to who-, what- and how-questions. The more specific a question is, the higher the QA quality would be. Research limitations/implications Suggestions for both search engine users and designers are presented to help enhance user experience and QA quality. Originality/value Particularly suitable for search engine QA quality analysis, the newly developed evaluation criteria system expanded and enriched assessment metrics of both search engines and QA systems.
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Solodka, N., D. Sorokin, and O. Liashenko. "SEO-OPTIMIZATION OF WEBSITE ON GOOGLE SEARCH ENGINE." Transactions of Kremenchuk Mykhailo Ostrohradskyi National University 1 (February 27, 2020): 107–12. http://dx.doi.org/10.30929/1995-0519.2020.1.107-112.

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Dissertations / Theses on the topic "„Google” search engine"

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Nilsson, Rebecca, and Christa Alanko. "STREAMLINE THE SEARCH ENGINE MARKETING STRATEGY : Generational Driven Search Behavior on Google." Thesis, Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-70149.

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The expanded internet usage has resulted in an increased activity at web-based search engines. Companies are therefore devoting a large portion of their online marketing budget on Search Engine Marketing (abbreviated SEM) in order to reach potential online consumers searching for products. SEM comprises Search Engine Advertising (SEA) and Search Engine Optimization (SEO) which are two dissimilar marketing tools companies can invest in to reach the desired customer segments. It is therefore of great interest for companies in different product markets to have knowledge of which SEM strategy to utilize. The statement leads to the purpose of the thesis which is to investigate which SEM strategy is the most suitable for companies in different markets, SEA or SEO?. The purpose of the thesis is derived to the research problem: How does the search behavior of consumers differ between the two SEM tools, SEO and SEA?. Initially, in order to answer the research problem, a theoretical framework was conducted consisting of theories from previous research. To collect primary data observations of 60 test subjects was performed in accordance with the Experimental Vignette Methodology. The analysis consists of a comparison between the collected data and the theories included in the frame of reference, to identify similarities and differences. The SPSS analysis of the result revealed numerous findings such as the two-way interactions of the factors degree of involvement and the click rate of SEM, as well as the choice of either a head or a tail keyword and the degree of involvement. The analysis further revealed a three-way interaction which suggests that the degree of involvement, and the use of either a head or tail keyword affects the choice of SEM. Additionally, the result shows that customers using brands as keywords are more likely to click on an organic link rather than on a paid ad. However, when adding the factor age to the analysis the results turn insignificant. As the area of search behavior of customers using search engines is relatively scientifically unexplored, the thesis has contributed with knowledge useful for companies, marketing agencies, among others. However, due to the ongoing expansion of search engine usage, it is of great interest to conduct further research in the area to reveal additional findings.
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Pohjanen, R. (Riikka). "The benefits of search engine optimization in Google for businesses." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201910112963.

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Abstract. This study concentrates on search engine visibility and the benefits of search engine optimization (SEO) in Google for businesses. It seems that search engine visibility has undeniably an important role in business. Most of the people use internet, more than half of Internet traffic begins with search engines and majority of users search for information about goods and services on a regular basis. Google has the largest market share of all search engines. SEO has been studied since 1998 when Google was founded. In earlier studies the topics varied from the basics of SEO to the SEO process in practice and finally to the benefits of SEO. Only a limited number of information systems theory (IS) studies focus on the role of organic listing in search engine visibility. That is why some of the references in this literature review represent also general business research. The research question is “What are the benefits of Search Engine Optimization (SEO) in Google for businesses?” and the sub questions are: “Why search engine visibility is important for businesses?”, “How can business achieve organic search engine visibility in Google?” and “What is the future of Search Engine Optimization (SEO)?” This is a qualitative empirical study which utilizes eight semi-structured interviews on the matter. The goal of this study is to create a comprehensive understanding about SEO and the benefits businesses can get from it. Another ambitious attempt is to try to forecast the future role of search engines and SEO for business operations. The main benefits of SEO for businesses are better search engine visibility, more visitors and better-quality visitors on website and more conversions. It has been noted in many articles, that SEO alone will not provide us the desired return on investment (ROI). In the empirical results of this study, the interviewees also mentioned better understanding of search engine users, website programming and business as one of the benefits.
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Marshall, Oliver. "Search Engine Optimization and the connection with Knowledge Graphs." Thesis, Högskolan i Gävle, Företagsekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-35165.

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Aim: The aim of this study is to analyze the usage of Search Engine Optimization and Knowledge Graphs and the connection between them to achieve profitable business visibility and reach. Methods: Following a qualitative method together with an inductive approach, ten marketing professionals were interviewed via an online questionnaire. To conduct this study both primary and secondary data was utilized. Scientific theory together with empirical findings were linked and discussed in the analysis chapter. Findings: This study establishes current Search Engine Optimization utilization by businesses regarding common techniques and methods. We demonstrate their effectiveness on the Google Knowledge Graph, Google My Business and resulting positive business impact for increased visibility and reach. Difficulties remain in accurate tracking procedures to analyze quantifiable results. Contribution of the thesis: This study contributes to the literature of both Search Engine Optimization and Knowledge Graphs by providing a new perspective on how these subjects have been utilized in modern marketing. In addition, this study provides an understanding of the benefits of SEO utilization on Knowledge Graphs. Suggestions for further research: We suggest more extensive investigation on the elements and utilization of Knowledge Graphs; how the structure can be affected; which techniques are most effective on a bigger scale and how effectively the benefits can be measured. Key Words: Search Engine, Search Engine Optimization, SEO, Knowledge Graphs, Google My Business, Google Search Engine, Online Marketing.
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Dennis, Johansson. "Search Engine Optimization and the Long Tail of Web Search." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-296388.

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In the subject of search engine optimization, many methods exist and many aspects are important to keep in mind. This thesis studies the relation between keywords and website ranking in Google Search, and how one can create the biggest positive impact. Keywords with smaller search volume are called "long tail" keywords, and they bear the potential to expand visibility of the website to a larger crowd by increasing the rank of the website for the large fraction of keywords that might not be as common on their own, but together make up for a large amount of the total web searches. This thesis will analyze where on the web page these keywords should be placed, and a case study will be performed in which the goal is to increase the rank of a website with knowledge from previous tests in mind.
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Jonsson, Gustav, and Linn Ståhl. "Users’ Perception and Evaluation of a Search Engine Result Page." Thesis, Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-9916.

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In order to find information before an e-commerce purchase, Swedish young adults almost exclusively uses the Google search engine. Previous research contains quantitative data, which showcases how the users utilize search engines as online tools. However, qualitative data regarding the user behaviour on generic search engines were lacking and a gap in the literature was found. In order to collect empirical data to try to fill the gap, six participants were interviewed about their behaviour when using Google. The interviews aimed to discover how the user chooses one result over another at the search engine result page and why. The empirical data showed that the users used the search engine in order to obtain quick and accurate information, in the most convenient process possible. Two major factors were shown to highly contribute to the choice of a user’s perception of the search engine result page. Previous experience and the trustworthiness of a result, strongly affected if a user would choose to click on that result. As a support to both these factors, the user used the technical components: title, URL and snippet. We believe that our findings will contribute to a deeper understanding within search engine user behaviour.
För att hitta information inför ett köp av produkter över Internet, används sökmotorn Google av merparten av svenska unga vuxna. Tidigare forskning inom området innehåller uteslutande ett kvantitativt undersökningsperspektiv, som belyser hur användarna utnyttjar sökmotorerna som ett verktyg. Dock saknas det kvalitativ forskning som utförligt beskriver användarbeteendet. Med målet att hitta empirisk data för att fylla tomrummet, intervjuades sex deltagare om deras beteende och användning av sökmotorn Google. Intervjuerna syftade till att undersöka hur och varför användaren väljer ett resultat framför ett annat. Den empiriska undersökningen visade att användarna nyttjade sökmotorn för att få snabb och korrekt information, på ett så bekvämt sätt som möjligt. Framförallt två faktorer visade sig vara starkt bidragande till varför användarna väljer ett resultat på sökresultat sidan. Tidigare erfarenhet och tillförlitligheten av ett resultat, var båda starkt bidragande orsaker till om användaren skulle välja ett specifikt resultat. Som ett stöd till båda dessa faktorer, använde sig användaren av tekniska funktioner såsom resultats titel, URL och beskrivningen, för att bilda sig en egen uppfattning. Vi tror att vårt resultat kommer att bidra till en djupare förståelse för användarbeteendet inom sökmotorer.
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Claesson, Jennifer, and Henrik Gedda. "Google Ads: Understanding millennials' search behavior on mobile devices." Thesis, Linnéuniversitetet, Institutionen för marknadsföring (MF), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-75731.

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Purpose: The purpose of this study is to understand millennials search behavior on mobile devices. Research Questions: How do millennials value organic and sponsored search results on mobile devices? What are the Web advertising variables that affect millennials attitudes towards sponsored search ads on mobile devices? Methodology: Data was collected from 103 Swedish millennials through an experiment and survey. Conclusion: The findings of this research supports the variables of entertainment and incentives to have a positive association with millennials attitudes towards mobile search ads while irritation, informativeness and credibility were only partially supported when testing independently with attitudes. An overall negative attitude could be seen toward sponsored links when participants motivated their action to click. Moreover, the results illustrated a higher attitude value towards mobile search ads to reflect an increased click behavior on sponsored search results.
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Fransson, Johan Hansson Anders. "Primär eller sekundär söktjänst? : en effektivitetsstudie av söktjänsten Google och metasöktjänsten Dogpile = [Primary or secondary search engine?] : [a study of the retrieval performance of the search engine Google and the metasearch engine Dogpile] /." Borås : Högsk. i Borås, Bibliotekshögskolan/Biblioteks- och informationsvetenskap, 2004. http://www.hb.se/bhs/slutversioner/2004/04-51.pdf.

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Carter, Kristen J. "Credibility by Google: Do search engine cues influence website credibility and relevance assessments?" The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1308237330.

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Lee, Szeyin. "Designing A Better Internet Search Engine Based On Information Foraging Theory." Scholarship @ Claremont, 2014. http://scholarship.claremont.edu/scripps_theses/456.

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The first part of the thesis focuses on Information Foraging Theory which was developed by Peter Pirolli, a cognitive scientist from Intelligent Systems Lab at Palo Alto Research Center, to understand how human search in an information environment (Pirolli 1995). The theory builds upon the optimal foraging theory in behavioral ecology, which assumes that people adapt and optimize their information seeking behavior to maximize the success of accomplishing the task goals by selectively choosing paths based on the expected utility from the information cues. The expected utility in Information Foraging Theory is called Information Scent. The second part is to design and build a new way to visualize search engine results in a graphical way that incorporates the concept of information scent to make search experience more efficient for users. The end result of the project will be an improved visualization of search results, obtained by using Google’s Application programming interface (API), latent semantic analysis, and data visualization methods to present a semantics-based visualization of the search results. The proposed design is developed to increase information scent for relevant results and shorten the foraging path to reach the search goal by presenting users with fewer but more valuable proximal cues, thus making search a more human-centered experience.
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Allard, Sebastian, and Björn Nilsson. "Sökmotoroptimering : Metoder för att förbättra sin placering i Googles sökresultat." Thesis, Uppsala University, Computer Systems Sciences, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-126487.

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This paper is a literature study on search engine optimization (SEO) considering the leader of the search engine market: Google. There´s an introductory background description of Google and its methods of  crawling the Internet and indexing the web pages, along with a brief review of the famous PageRank algorithm. The purpose of this paper is to describe the major important methods for improved rankings on Google´s result lists. These methods could be categorized as on-page methods tied to the website to be optimized or off-page methods that are external to the website such as link development. Furthermore the most common unethical methods are described, known as “black hat”, which is the secondary purpose of the text. The discussion that follows concerns the practical implications of SEO and personal reflections of the matter. Finally  there´s a quick view of the expanding market of handheld devices connected to the Internet and mobile search as an initial area of research.


Denna uppsats är en litteraturstudie om ämnet sökmotoroptimering (SEO) som behandlar ledaren bland sökmotorer: Google. Det finns en introducerande bakgrund som beskriver Google och dess metoder för ”crawling” och indexering av webbplatser, tillsammans med en kort genomgång av den  välkända  PageRank-algoritmen. Syftet med denna uppsats är att beskriva de centrala metoderna för förbättrad rankning i Googles träffresultat. Dessa metoder kan kategoriseras som ”on-page”-metoder, som är knutna till webbplatsen, eller ”off-page”-metoder, som är externa, exempelvis  länkförvärvning. Vidare kommer de vanligaste oetiska metoderna att beskrivas, kända som ”black hat”, som är det andra syftet med denna text. Diskussionen som följer behandlar de praktiska implikationerna av SEO och personliga reflektioner i frågan. Avslutningsvis  berör vi  den expanderade marknaden av handhållen utrustning med Internetuppkoppling och mobil sökning som ett kommande forskningsområde.

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Books on the topic "„Google” search engine"

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Steve Johnston & Liam McGee. 50 Ways to Make Google Love Your Website. London: Random House Group Limited, 2010.

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Fox, Vanessa. Marketing in the age of Google: A non-technical guide to search engine strategy. Hoboken, N.J: John Wiley & Sons, 2010.

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Mordkovich, Boris. Pay-per-click search engine marketing handbook: Low cost strategies to attracting new customers using Google, Yahoo & other search engines. Brooklyn, N.Y: MordComm, 2005.

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Mordkovich, Boris. Pay-per-click search engine marketing handbook: Low cost strategies to attracting new customers using Google, Yahoo & other search engines. Brooklyn, N.J: MordComm, Inc., 2005.

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Fox, Vanessa. Marketing in the age of Google: A non-technical guide to search engine strategy. Hoboken, N.J: John Wiley & Sons, 2010.

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Fox, Vanessa. Marketing in the age of Google: A non-technical guide to search engine strategy. Hoboken, N.J: John Wiley & Sons, 2010.

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Smith, Jon. Grow your business with Google Adwords: 7 quick and easy secrets for reaching more customers with the world's #1 search engine. New York: McGraw-Hill, 2010.

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The small business owner's handbook to search engine optimization: Increase your Google rankings, double your site traffic-- in just 15 steps--guaranteed. Ocala, FL: Atlantic Pub. Group, 2009.

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Woessner, Stephen. The small business owner's handbook to search engine optimization: Increase your Google rankings, double your site traffic-- in just 15 steps--guaranteed. Ocala, FL: Atlantic Pub. Group, 2009.

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Be #1 on Google: 52 fast and easy search engine optimization tools to drive customers to your web site. New York: McGraw-Hill, 2010.

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Book chapters on the topic "„Google” search engine"

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Cassel, John B. "Wolfram|Alpha: A Computational Knowledge “Search” Engine." In Google It, 267–99. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6415-4_11.

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Buddenbrock, Frank. "Search Engine Optimization: Getting to Google’s First Page." In Google It, 195–204. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6415-4_5.

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Shamaeva, Irina, and David Galley. "What Is a Google Custom Search Engine?" In Custom Search – Discover more:, 29–32. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003100133-3.

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Diaz, A. "Through the Google Goggles: Sociopolitical Bias in Search Engine Design." In Web Search, 11–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75829-7_2.

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Voigt, Kai-Ingo, Oana Buliga, and Kathrin Michl. "Beyond the Search Engine: The Case of Google." In Management for Professionals, 81–94. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38845-8_8.

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Zimmer, M. "The Gaze of the Perfect Search Engine: Google as an Infrastructure of Dataveillance." In Web Search, 77–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75829-7_6.

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Bilal, Dania, and Yan Zhang. "Teens’ Conceptual Understanding of Web Search Engines: The Case of Google Search Engine Result Pages (SERPs)." In Lecture Notes in Computer Science, 253–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78468-3_18.

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Khoo, Michael, and Catherine Hall. "What Would ‘Google’ Do? Users’ Mental Models of a Digital Library Search Engine." In Theory and Practice of Digital Libraries, 1–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33290-6_1.

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Mraček, David. "Uncovering the Potential of the Google Search Engine for L2 Learning and L2 Translator Training." In Emerging Technologies for Education, 109–21. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38778-5_14.

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Martínez-Santiago, Fernando, Arturo Montejo-Ráez, Miguel Á. García-Cumbreras, and L. Alfonso Ureña-López. "SINAI at CLEF 2006 Ad Hoc Robust Multilingual Track: Query Expansion Using the Google Search Engine." In Evaluation of Multilingual and Multi-modal Information Retrieval, 119–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74999-8_16.

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Conference papers on the topic "„Google” search engine"

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Silva, Leandro Rodrigues Manso, Carlos Augusto Duque, and Paulo Fernando Ribeiro. "Power Quality waveform recognition using Google Image Search Engine (iPQ-Google)." In 2016 17th International Conference on Harmonics and Quality of Power (ICHQP). IEEE, 2016. http://dx.doi.org/10.1109/ichqp.2016.7783320.

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Meng, Zhaoli, Meiyun Zuo, and Jina Fu. "Why Google Cannot Beat Baidu in China Search Engine Market." In 2008 International Symposium on Ubiquitous Multimedia Computing (UMC). IEEE, 2008. http://dx.doi.org/10.1109/umc.2008.45.

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Raval, Vishwas, and Padam Kumar. "EGG (Enhanced Guided Google) — A meta search engine for combinatorial keyword search." In 2011 Nirma University International Conference on Engineering (NUiCONE). IEEE, 2011. http://dx.doi.org/10.1109/nuicone.2011.6153251.

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Brickley, Dan, Matthew Burgess, and Natasha Noy. "Google Dataset Search: Building a search engine for datasets in an open Web ecosystem." In WWW '19: The Web Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3308558.3313685.

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Mracek, David. "The Google Search Engine: A Blended-Learning Tool for Student Empowerment." In 2019 International Symposium on Educational Technology (ISET). IEEE, 2019. http://dx.doi.org/10.1109/iset.2019.00054.

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Al-Duwairi, B., and G. Manimaran. "JUST-Google: A Search Engine-Based Defense Against Botnet-Based DDoS Attacks." In ICC 2009 - 2009 IEEE International Conference on Communications. IEEE, 2009. http://dx.doi.org/10.1109/icc.2009.5199127.

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Juhari, Suriati, Jasmee Jaafar, Roslina Idris, and Mohammad Yaziz Ahmad. "My Map Portal Engine Search (MMPes) utilizing spatial dataset with Google Earth." In 2013 IEEE 3rd International Conference on System Engineering and Technology (ICSET). IEEE, 2013. http://dx.doi.org/10.1109/icsengt.2013.6650163.

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Patil, Akshita, Jayesh Pamnani, and Dipti Pawade. "Comparative Study Of Google Search Engine Optimization Algorithms: Panda, Penguin and Hummingbird." In 2021 6th International Conference for Convergence in Technology (I2CT). IEEE, 2021. http://dx.doi.org/10.1109/i2ct51068.2021.9418074.

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Lin, Tsung Fu, and Yan Ping Chi. "Application of Webpage Optimization for Clustering System on Search Engine V Google Study." In 2014 International Symposium on Computer, Consumer and Control (IS3C). IEEE, 2014. http://dx.doi.org/10.1109/is3c.2014.186.

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Kalyana Chakravarthy, Katuru SM. "Openness of Search Engine: A Critical Flaw in Search Systems; A Case Study on Google, Yahoo and Bing." In The First International Conference on Information Technology Convergence and Services. Academy & Industry Research Collaboration Center (AIRCC), 2012. http://dx.doi.org/10.5121/csit.2012.2128.

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Reports on the topic "„Google” search engine"

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Frisancho, Verónica, and Eric Parrado. International Remittances to Latin America and the Caribbean amid the COVID-19 Crisis: A Push for Digitalization? Inter-American Development Bank, December 2021. http://dx.doi.org/10.18235/0003874.

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Remittances constitute a significant safety net for millions of households in Latin America and the Caribbean (LAC). Consequently, changes in international transfers can be a crucial agent of transmission of the COVID-19 induced economic crisis from richer to poorer nations and from urban to rural areas. Relying on data on queries to the search engine Google between December 2018 and July 2021, this study looks at the evolution of demand for in-person versus digital international transfer services and evaluates if take-up rates of different types of service providers trace the initial drop and subsequent rebound of remittances. The recovery of remittances was accompanied by a modest and temporary increase in the interest in digital mechanisms for sending money to home countries, which is accompanied by lower demand for brick-and-mortar service providers.
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Molla-Esparza, Cristian, Natalia Gandía Carbonell, María Isabel Gómez Núñez, and Laura Badenes Ribera. Prevalence of sex under the influence of drugs among young adults: a systematic review and meta-analysis protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, October 2021. http://dx.doi.org/10.37766/inplasy2021.10.0077.

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Review question / Objective: The objective of this systematic review and meta-analysis is to examine the prevalence of engaging in sex under the influence of drugs among young adults. Condition being studied: The prevalence of sex under the influence of drugs in young adults. Information sources: Published and unpublished literature will be systematically searched via three electronic databases: ISI Web of Science (WoS Core Collection); Scopus; and Psychological Information (PsycInfo). A grey literature search will also be performed via the Google and Google Scholar search engines, in order to obtain other potential relevant studies. Reference lists of relevant published studies will also be examined to obtain additional eligible reports. Additionally, emails will be sent to the research groups that have published the most about SDU, with the aim of identifying unpublished studies.
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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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