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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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Salamun, Salamun, Yessi Jusman, and Widdya Rahmalina. "POWER SEARCH DALAM MENGGUNAKAN SEARCH ENGINE." Jurnal Pengabdian Masyarakat Multidisiplin 1, no. 1 (January 11, 2018): 65–71. http://dx.doi.org/10.36341/jpm.v1i1.398.
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Mesin pencari web / web search engine adalah program komputer yang dirancang untuk melakukan pencarian atas berkas-berkas yang tersimpan dalam layanan www, ftp, publikasi milis, ataupun news group dalam sebuah ataupun sejumlah komputer dalam suatu jaringan. Dalam menggunakan fasilitas mesin pencarian seperti google harus dengan beberapa cara agar bisa langsung mendapatkan informasi atau file yang di inginkan. Dalam kegiatan ini siswa/I akan di ajarkan bagaimana cara mengguanakan mesin pencarian raksasa saat ini seperti google secara efektif dan terarah. Metode yang digunakan untuk melakukan pelatihan yaitu memberikan langsung ke pada SMK dan SMA pengetahuan dengan menggunakan perangkat komputer/laptop dan jaringan internet. Untuk pencapain tujuan tersebut maka kegiatan pengabdian akan dilakukan secara langsung dalam ruangan yang diikuti oleh 35 orang siswa. Adapun untuk menggunakan mesin pencarian yang efektif dan efisien dengan cara menggunakan kata kunci yang tepat agar apa yang tampilkan sesuai dengan yang di cari seperti, mencari file dengan type tertentu maka gunakan kata kunci yang memiliki type dari file tersebut seperti doc, pdf, ppt dan type file lainnya.
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Castelvecchi, Davide. "Google unveils search engine for open data." Nature 561, no. 7722 (September 2018): 161–62. http://dx.doi.org/10.1038/d41586-018-06201-x.
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Saputri, Ekawati. "Strategi Penelusuran Informasi Melalui Search Engine (Google)." Jurnal Adabiya 23, no. 2 (August 28, 2021): 232. http://dx.doi.org/10.22373/adabiya.v23i2.10137.
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Strategi penelusuran adalah taktik atau cara yang dilakukan secara sistematis berdasarkan kata kunci (keyword). Penelusuran ini memudahkan pengguna internet dalam mencari informasi. strategi penelusuran informasi apa saja yang bisa digunakan oleh pengguna internet pada search engine google dalam pencarian informasi. Strategi penelusuran yang paling sederhana, yaitu hanya memasukkan istilah tanpa taktik atau belum dilakukan secara sistematis, sehingga mesin pencari memberikan hasil penelusuran tanpa ada batasan, dan membanjirnya hasil penelusuran pun terjadi. Pengetahuan tentang strategi penelusuran informasi melalui search engine sudah seharusnya dimiliki oleh user, siswa dan para pencari informasi yang menjadikan internet sebagai sumber informasi. Adapun peran perpustakaan sebagai wadah informasi yang dapat memberikan referensi tentang strategi penelusuran informasi melalui media internet seperti search engine dan menyelenggarakan pelatihan tentang penelusuran informasi melalui media internet dengan efektif dan efisien.
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Loda, Teresa, Rebecca Erschens, Florian Junne, Andreas Stengel, Stephan Zipfel, and Anne Herrmann-Werner. "Undergraduate Medical Students’ Search for Health Information Online: Explanatory Cross-Sectional Study." JMIR Medical Informatics 8, no. 3 (March 2, 2020): e16279. http://dx.doi.org/10.2196/16279.
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Background Previous research shows that being a “digital native” or growing up in a digital environment does not necessarily lead to increased digital competencies, such as digital health literacy or evaluation of webpage quality. Objective This study showed how medical students searched for health information online, specifically the recommended testing for histamine intolerance, by comparing the use of various search engines (Google, Medisuch, and a website of the student’s choice) to find out more about search strategies in future health professionals. As Medisuch presents a qualitatively better search engine, we assumed that medical students using this search engine might find valid information faster on more reliable webpages, and might recommend the correct diagnostic steps for histamine intolerance to their patients more often than students using a generic search engine like Google. Methods Medical students in their third year of study were asked to find the relevant diagnostic steps of histamine intolerance online. They were randomly assigned to use one search engine: Google, their personal choice, or Medisuch. Their process of seeking information online was video recorded. Results In total, 140 medical students participated in this study. The total number of webpages found did not differ among the groups (P=.52). Students using Medisuch (P=.02) correctly identified the elimination diet as a relevant diagnostic step more frequently. The provocation test was reported by almost half of the students independent of the search engine used. In general, medical students commonly identified trustworthy webpages in all three groups (Google: 36/44, 82%; free choice: 31/36; 86%; and Medisuch: 35/45, 78%). Conclusions The results indicate that medical students were able to find trustworthy health-related information online independent of the search engine used. Medical students that are digital natives seem to have proper internet skills and a knowledge of how to use them. They entered specific medical terms (evidence-based diagnostic steps) or names of reliable webpages (DocCheck) in the search engines to gain correct information. However, it remains to be seen if this behavior can be called true “digital literacy”.
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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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Yamin, Fadhilah Mat. "INTERFACING GOOGLE SEARCH ENGINE TO CAPTURE USER WEB SEARCH BEHAVIOR." International Journal of Electronic Commerce Studies 4, no. 1 (June 2013): 47–62. http://dx.doi.org/10.7903/ijecs.1111.
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Zhang, Jin, Xin Cai, Taowen Le, Wei Fei, and Feicheng Ma. "A Study on Effective Measurement of Search Results from Search Engines." Journal of Global Information Management 27, no. 1 (January 2019): 196–221. http://dx.doi.org/10.4018/jgim.2019010110.
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This article describes how as internet technology continues to change and improve lives and societies worldwide, effective global information management becomes increasingly critical, and effective Internet information retrieval systems become more and more significant in providing Internet users worldwide with accurate and complete information. Search engine evaluation is an important research field as search engines directly determine the quality of information users' Internet searches. Relevance-decrease pattern/model plays an important role in search engine result evaluation. This research studies effective measurement of search results through investigating relevance-decrease patterns of search results from two popular search engines: Google and Bing. The findings can be applied to relevance-evaluation of search results from other information retrieval systems such as OPAC, can help make search engine evaluations more accurate and sound, and can provide global information management personnel with valuable insights.
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FAN, XIUYI, and THOMAS C. HENDERSON. "ROBOTSHARE: A GOOGLE FOR ROBOTS." International Journal of Humanoid Robotics 05, no. 02 (June 2008): 311–29. http://dx.doi.org/10.1142/s0219843608001388.
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Knowledge representation is a traditional field in artificial intelligence. Researchers have developed various ways to represent and share information among intelligent agents. Agents that share resources, data, information, and knowledge perform better than agents working alone. However, previous research has also revealed that sharing knowledge among a large number of entities in an open environment is a problem yet to be solved. Intelligent robots are designed and produced by different manufacturers. They have various physical attributes and employ different knowledge representations. Therefore, any nonstandard or non-widely-adopted technology is unsuitable for providing a satisfactory solution to the knowledge sharing problem. In this research, we pose robot knowledge sharing as an activity to be developed in an open environment — the World Wide Web. Just as search engines like Google provide enormous power for information exchange and sharing for humans, we believe that a searching mechanism designed for intelligent agents can provide a robust approach to sharing knowledge among robots. We have developed: (1) a knowledge representation for robots that allows Internet access, (2) a knowledge organization and search indexing engine, and (3) a query/reply mechanism between robots and the search engine.
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Martín, Bartolomé. "Google v. CNIL and the Right to Be Forgotten: A Judgment of Solomon." Global Privacy Law Review 1, Issue 1 (March 1, 2020): 61–62. http://dx.doi.org/10.54648/gplr2020008.
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The EUCJ ruling on Case C-507/17 provides further clarity on the territorial scope of the right to be forgotten. The court interprets that the EU law does not require a search engine operator to attend the right to de-referencing on all the versions of its search engine globally, but only on those corresponding to all the Member States, but at the same time it also clarifies that a supervisory or judicial authority of a Member State remains competent to, after weighing up the legally protected interests, order (where appropriate) that the de-referencing is carried out on all versions of the relevant search engine. A decision that likely does not please either the search engines or the data subjects, and which drops a certain dose of uncertainty on the system. GDPR, right to be forgotten, right to de-referencing, search engines, territorial scope
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Пестерев and Pavel Pesterev. "ANALYSIS OF IMPROVEMENTS IN THE SEARCH ENGINE ALGORITHMS OF YANDEX AND GOOGLE." Bulletin of Belgorod State Technological University named after. V. G. Shukhov 2, no. 3 (April 4, 2017): 145–47. http://dx.doi.org/10.12737/24888.
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The article describes improvements in the search engine algorithms of Yandex and Google. It is analyzed and presented the main directions of improving the search engine algorithms. Based on the present principles of algorithms developers can raise their sites in the search engine results page (SERPs). The article can be useful to designers and web developers, students, undergraduates and postgraduates in the fields of information technology.
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Oh, Se Eun, Shuai Li, and Nicholas Hopper. "Fingerprinting Keywords in Search Queries over Tor." Proceedings on Privacy Enhancing Technologies 2017, no. 4 (October 1, 2017): 251–70. http://dx.doi.org/10.1515/popets-2017-0048.
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AbstractSearch engine queries contain a great deal of private and potentially compromising information about users. One technique to prevent search engines from identifying the source of a query, and Internet service providers (ISPs) from identifying the contents of queries is to query the search engine over an anonymous network such as Tor.In this paper, we study the extent to which Website Fingerprinting can be extended to fingerprint individual queries or keywords to web applications, a task we call Keyword Fingerprinting (KF). We show that by augmenting traffic analysis using a two-stage approach with new task-specific feature sets, a passive network adversary can in many cases defeat the use of Tor to protect search engine queries.We explore three popular search engines, Google, Bing, and Duckduckgo, and several machine learning techniques with various experimental scenarios. Our experimental results show that KF can identify Google queries containing one of 300 targeted keywords with recall of 80% and precision of 91%, while identifying the specific monitored keyword among 300 search keywords with accuracy 48%. We also further investigate the factors that contribute to keyword fingerprintability to understand how search engines and users might protect against KF.
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Sihotang, Hengki Tamando. "OPTIMASI RISET KEYWORD DENGAN TEKNIK ALLINTITLLE PADA MESIN PENCARI GOOGLE." Journal Of Computer Networks, Architecture and High Performance Computing 1, no. 1 (December 18, 2018): 1–5. http://dx.doi.org/10.47709/cnapc.v1i1.1.
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Online information needs have evolved in the real direction. These needs include the latest information, government services, and commercial products. The research question is how to describe and optimize keyword research with the allintitle technique on the google search engine. The development method used in this research is the prototype method because it is considered able to be evaluated directly on the user. The system testing is done for 3 months by placing keywords on several websites on Google. The conclusion that can be taken is to use the allintitle technique, the search results for the web are easier to find. And this web-based allintitle technique can overcome the challenges of captcha verification from the Google search engine.
Keywords: Allintitle, Google's Search Engine, Keyword competition.
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Wani, Zahid Ashraf, and Adil Ahmad Sofi. "Retrieval efficiency of select search engines vis-à-vis diverse open courseware formats." Electronic Library 34, no. 3 (June 6, 2016): 457–70. http://dx.doi.org/10.1108/el-08-2014-0132.
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Purpose This paper aims to gauge the visibility of open content available in different formats of select open courseware (OCW) repositories through prominent search engines. Design/methodology/approach Open content in three formats (pdf, audio and video) from four OCW repositories listed in the OCW consortium under the science and technology subject heading were searched through seven select search engines. Findings None of the selected OCW repositories are fully visible on the selected search engines. Visibility of OCW content varied from one search engine to the other and was affected by the format in which it is available. Google is the best search engine for retrieving OCW content, whereas OCWfinder – a specialized search engine for retrieving OCW – has performed dismally. Research limitations/implications The study demonstrates the need for enhancing the visibility of open content through using search engine optimization techniques. Originality/value The study intends to supply findings that could be used by stakeholders to improve the visibility of OCW repositories. It is an attempt to draw a comparison between search engines for their ability to index different formats of OCW in the selected repositories. Findings can be used by information professionals to brush their information hunting skills.
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Ziakis, Christos, Maro Vlachopoulou, Theodosios Kyrkoudis, and Makrina Karagkiozidou. "Important Factors for Improving Google Search Rank." Future Internet 11, no. 2 (January 30, 2019): 32. http://dx.doi.org/10.3390/fi11020032.
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The World Wide Web has become an essential modern tool for people’s daily routine. The fact that it is a convenient means for communication and information search has made it extremely popular. This fact led companies to start using online advertising by creating corporate websites. With the rapid increase in the number of websites, search engines had to come up with a solution of algorithms and programs to qualify the results of a search and provide the users with relevant content to their search. On the other side, developers, in pursuit of the highest rankings in the search engine result pages (SERPs), began to study and observe how search engines work and which factors contribute to higher rankings. The knowledge that has been extracted constituted the base for the creation of the profession of Search Engine Optimization (SEO). This paper consists of two parts. The first part aims to perform a literature review of the factors that affect the ranking of websites in the SERPs and to highlight the top factors that contribute to better ranking. To achieve this goal, a collection and analysis of academic papers was conducted. According to our research, 24 website characteristics came up as factors affecting any website’s ranking, with the most references mentioning quality and quantity of backlinks, social media support, keyword in title tag, website structure, website size, loading time, domain age, and keyword density. The second part consists of our research which was conducted manually using the phrases “hotel Athens”, “email marketing”, and “casual shoes”. For each one of these keywords, the first 15 Google results were examined considering the factors found in the literature review. For the measurement of the significance of each factor, the Spearman correlation was calculated and every factor was compared with the ranking of the results individually. The findings of the research showed us that the top factors that contribute to higher rankings are the existence of website SSL certificate as well as keyword in URL, the quantity of backlinks pointing to a website, the text length, and the domain age, which is not perfectly aligned with what the literature review showed us.
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Mears, Derrick, Joanne M. Leight, and Matthew Cummiskey. "Google—So Much More than a Search Engine!" Journal of Physical Education, Recreation & Dance 83, no. 1 (January 2012): 9–12. http://dx.doi.org/10.1080/07303084.2012.10598702.
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Felkey, Bill G., and Brent I. Fox. "Google on Steroids: An Advanced Search Engine Primer." Hospital Pharmacy 46, no. 8 (August 2011): 618–20. http://dx.doi.org/10.1310/hpj4608-618.
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Marks, Paul. "The people's search engine gangs up on Google." New Scientist 194, no. 2606 (June 2007): 30–31. http://dx.doi.org/10.1016/s0262-4079(07)61377-3.
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Bar‐Ilan, Judit. "Manipulating search engine algorithms: the case of Google." Journal of Information, Communication and Ethics in Society 5, no. 2/3 (October 19, 2007): 155–66. http://dx.doi.org/10.1108/14779960710837623.
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Evans, Michael P. "Analysing Google rankings through search engine optimization data." Internet Research 17, no. 1 (February 6, 2007): 21–37. http://dx.doi.org/10.1108/10662240710730470.
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Shama Rani and Jaiteg Singh. "Getting Bulk Data Through Google: An empirical study." Journal of Technology Management for Growing Economies 7, no. 2 (October 24, 2016): 39–48. http://dx.doi.org/10.15415/jtmge.2016.72002.
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To store the information in a database is one of the major tasks. The efficient storage of data is important for future use. Information retrieval is a method of gathering information related to input queries from the various sources or stored databases. To retrieve the information, a search engine plays an important role. A web search engine creates an index to match queries. The quality of information is improved with the help of search engine. For retrieving the information, a search engine comprises some modules such as query processor, a searching and matching function, document processor and page rank capability. This paper focuses on the retrieval of web documents against input queries and stores them in to database. A Google search API can be used to fetch the results. It analyses the data by processing through these modules and downloads the content available in different formats.
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Sadeghi, Mohammad, and Jesús Vegas. "How well does Google work with Persian documents?" Journal of Information Science 43, no. 3 (March 1, 2016): 316–27. http://dx.doi.org/10.1177/0165551516640437.
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The performance evaluation of an information retrieval system is a decisive aspect of the measure of the improvements in search technology. The Google search engine, as a tool for retrieving information on the Web, is used by almost 92% of Iranian users. The purpose of this paper is to study Google’s performance in retrieving relevant information from Persian documents. The information retrieval effectiveness is based on the precision measures of the search results done to a website that we have built with the documents of a TREC standard corpus. We asked Google for 100 topics available on the corpus and we compared the retrieved webpages with the relevant documents. The obtained results indicated that the morphological analysis of the Persian language is not fully taken into account by the Google search engine. The incorrect text tokenisation, considering the stop words as the content keywords of a document and the wrong ‘variants encountered’ of words found by Google are the main reasons that affect the relevance of the Persian information retrieval on the Web for this search engine.
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Rovira, Cristòfol, Lluís Codina, and Carlos Lopezosa. "Language Bias in the Google Scholar Ranking Algorithm." Future Internet 13, no. 2 (January 27, 2021): 31. http://dx.doi.org/10.3390/fi13020031.
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The visibility of academic articles or conference papers depends on their being easily found in academic search engines, above all in Google Scholar. To enhance this visibility, search engine optimization (SEO) has been applied in recent years to academic search engines in order to optimize documents and, thereby, ensure they are better ranked in search pages (i.e., academic search engine optimization or ASEO). To achieve this degree of optimization, we first need to further our understanding of Google Scholar’s relevance ranking algorithm, so that, based on this knowledge, we can highlight or improve those characteristics that academic documents already present and which are taken into account by the algorithm. This study seeks to advance our knowledge in this line of research by determining whether the language in which a document is published is a positioning factor in the Google Scholar relevance ranking algorithm. Here, we employ a reverse engineering research methodology based on a statistical analysis that uses Spearman’s correlation coefficient. The results obtained point to a bias in multilingual searches conducted in Google Scholar with documents published in languages other than in English being systematically relegated to positions that make them virtually invisible. This finding has important repercussions, both for conducting searches and for optimizing positioning in Google Scholar, being especially critical for articles on subjects that are expressed in the same way in English and other languages, the case, for example, of trademarks, chemical compounds, industrial products, acronyms, drugs, diseases, etc.
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Luh, Cheng-Jye, Sheng-An Yang, and Ting-Li Dean Huang. "Estimating Google’s search engine ranking function from a search engine optimization perspective." Online Information Review 40, no. 2 (April 11, 2016): 239–55. http://dx.doi.org/10.1108/oir-04-2015-0112.
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Purpose – The purpose of this paper is to estimate Google search engine’s ranking function from a search engine optimization (SEO) perspective. Design/methodology/approach – The paper proposed an estimation function that defines the query match score of a search result as the weighted sum of scores from a limited set of factors. The search results for a query are re-ranked according to the query match scores. The effectiveness was measured by comparing the new ranks with the original ranks of search results. Findings – The proposed method achieved the best SEO effectiveness when using the top 20 search results for a query. The empirical results reveal that PageRank (PR) is the dominant factor in Google ranking function. The title follows as the second most important, and the snippet and the URL have roughly equal importance with variations among queries. Research limitations/implications – This study considered a limited set of ranking factors. The empirical results reveal that SEO effectiveness can be assessed by a simple estimation of ranking function even when the ranks of the new and original result sets are quite dissimilar. Practical implications – The findings indicate that web marketers should pay particular attention to a webpage’s PR, and then place the keyword in URL, the page title, and snippet. Originality/value – There have been ongoing concerns about how to formulate a simple strategy that can help a website get ranked higher in search engines. This study provides web marketers much needed empirical evidence about a simple way to foresee the ranking success of an SEO effort.
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Coates, Mildred. "Search engine queries used to locate electronic theses and dissertations." Library Hi Tech 32, no. 4 (November 11, 2014): 667–86. http://dx.doi.org/10.1108/lht-02-2014-0022.
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Purpose – The purpose of this paper is to examine two research questions: What search engine queries lead users to the Auburn University electronic theses and dissertations (AUETDs) collection? Do these queries vary for users in different locations and, if so, how? Design/methodology/approach – Search engine queries used to locate the AUETDs collection were obtained from Google Analytics and were separated into groups based on user location. These queries were assigned to empirically derived categories based on their content. Findings – Most local users’ queries contained person names, variants for thesis or dissertation, and variants for Auburn University. Over a third were queries for the AUETDs collection, while the remainder were seeking theses and dissertations from specific Auburn researchers. Most out-of-state users’ queries contained title and subject keywords and appeared to be seeking specific research studies. Queries from users located within the state but outside of the local area were intermediate between these groups. Practical implications – Over two-thirds of visits to the AUETDs collection were made by search engine users which reinforces the importance of having repository content indexed by search engines such as Google. The specificity of their queries indicates that full-text indexing will be more helpful to users than metadata indexing alone. Originality/value – This is the first detailed analysis of search engine queries used to locate an ETDs collection. It may also be the last, as query content for the major search engines is no longer available from Google Analytics.
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Majid, Ismail. "Analisis Penerapan Google Custom Search API pada Sistem Pencarian Data." Journal of Computer and Information System ( J-CIS ) 1, no. 1 (August 10, 2021): 29–35. http://dx.doi.org/10.31605/jcis.v1i1.626.
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Abstrak
Sistem Pencarian merupakan aplikasi penting diterapkan pada sebuah media informasi online, namun sejak hadirnya mesin pencari seperti Google orang lebih suka menggunakan alat ini untuk menemukan informasi. Karena metode pencarian yang digunakan terbukti keandalannya. Apakah kita mampu seperti itu? Penelitian ini membuktikan bahwa dengan menerapkan metode Google Custom Search API, kita dapat membangun sistem pencarian layaknya seperti mesin pencari Google, hasil pengujian menunjukkan hasil pencarian yang ditampilkan sangat relevan dan rata-rata berada pada peringkat pertama. Keuntungan lainnya metode ini dilengkapi koreksi ejaan salah untuk menyempurnakan kata kunci sebenarnya.
Abstract
Search system is an important application applied to an online information media, but since the presence of search engines like Google, people prefer to use this tool to find information. Because the search method used is proven to be reliable. Are we able to be like that? This research proves that by implementing the Google Custom Search API method, we can build a search system like Google's search engine, the test results show that the search results displayed are very relevant and on average are ranked first. Another advantage of this method is that it includes incorrect spelling corrections to perfect the actual keywords.
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Sari, Wasvita. "Literasi Digital pada Masalah Pencarian Informasi Dengan Google." Jurnal Penelitian Pers dan Komunikasi Pembangunan 22, no. 2 (October 17, 2018): 135–47. http://dx.doi.org/10.46426/jp2kp.v22i2.96.
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As we know that Google is the most popular search engine in the world. We are can get so much about the information and believe in the Google's result. So, the information is fake information, hoax or wrong information, that's all appears on Google. This paper aims to explore the negative side of Google search engine and give a solution with a digital literature study. Qualitative method with reference approach will use in this paper. The result is need two skills when we search the information from Google. there are consumming skills to and preconsumming skills. The consuming skill is an skill to analize, evaluate, make critical and give some synthesis of the information we get. The preconsumming skill is a product to be distributed and product information from Google. Finally, this paper will prevent the people from entering the Google game and work carefully to read the search engine information especially in Google.
Keywords: Digital Literacy, Search Engine, Google, Literacy
ABSTRAK
Seperti yang kita ketahui bahwa Google adalah mesin pencari paling populer di dunia. Kita bisa mendapatkan begitu banyak informasi dan percaya pada hasil Google. Jadi, informasi itu informasi palsu, tipuan atau informasi salah, itu semua muncul di Google. Tulisan ini bertujuan untuk mengeksplorasi sisi negatif dari mesin pencari Google dan memberikan solusi dengan studi literatur digital. Metode kualitatif dengan pendekatan referensi akan digunakan dalam makalah ini. Hasilnya adalah perlu dua keterampilan ketika kita mencari informasi dari Google. ada keterampilan yang menyita keterampilan dan keterampilan prakonsepsi. Keterampilan mengkonsumsi adalah keterampilan untuk menganalisis, mengevaluasi, membuat kritis dan memberikan beberapa sintesis dari informasi yang kami dapatkan. Keterampilan preconsumming adalah produk yang akan didistribusikan dan informasi produk dari Google. Akhirnya, tulisan ini akan mencegah orang-orang memasuki gim Google dan bekerja dengan hati-hati untuk membaca informasi mesin pencari terutama di Google.
Kata kunci: literasi dijital, mesin pencari, Google, literasi
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Murdiyanto, Aris Wahyu, and Adri Priadana. "Analysis of Web Scraping Techniques to Get Keywords Suggestion and Allintitle Automatically from Google Search Engines." Compiler 10, no. 2 (November 30, 2021): 71. http://dx.doi.org/10.28989/compiler.v10i2.1064.
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Keyword research is one of the essential activities in Search Engine Optimization (SEO). One of the techniques in doing keyword research is to find out how many articles titles on a website indexed by the Google search engine contain a particular keyword or so-called "allintitle". Moreover, search engines are also able to provide keywords suggestion. Getting keywords suggestions and allintitle will not be effective, efficient, and economical if done manually for relatively extensive keyword research. It will take a long time to decide whether a keyword is needed to be optimized. Based on these problems, this study aimed to analyze the implementation of the web scraping technique to get relevant keyword suggestions from the Google search engine and the number of "allintitle" that are owned automatically. The data used as an experiment in this test consists of ten keywords, which each keyword would generate a maximum of ten keywords suggestion. Therefore, from ten keywords, it will produce at most 100 keywords suggestions and the number of allintitles. Based on the evaluation result, we got an accuracy of 100%. It indicated that the technique could be applied to get keywords suggestions and allintitle from Google search engines with outstanding accuracy values.
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Lewandowski, Dirk, and Sebastian Sünkler. "What does Google recommend when you want to compare insurance offerings?" Aslib Journal of Information Management 71, no. 3 (May 20, 2019): 310–24. http://dx.doi.org/10.1108/ajim-07-2018-0172.
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Purpose The purpose of this paper is to describe a new method to improve the analysis of search engine results by considering the provider level as well as the domain level. This approach is tested by conducting a study using queries on the topic of insurance comparisons. Design/methodology/approach The authors conducted an empirical study that analyses the results of search queries aimed at comparing insurance companies. The authors used a self-developed software system that automatically queries commercial search engines and automatically extracts the content of the returned result pages for further data analysis. The data analysis was carried out using the KNIME Analytics Platform. Findings Google’s top search results are served by only a few providers that frequently appear in these results. The authors show that some providers operate several domains on the same topic and that these domains appear for the same queries in the result lists. Research limitations/implications The authors demonstrate the feasibility of this approach and draw conclusions for further investigations from the empirical study. However, the study is a limited use case based on a limited number of search queries. Originality/value The proposed method allows large-scale analysis of the composition of the top results from commercial search engines. It allows using valid empirical data to determine what users actually see on the search engine result pages.
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Hinman, Lawrence M. "Esse est indicato in Google: Ethical and Political Issues in Search Engines." International Review of Information Ethics 3 (June 1, 2005): 19–25. http://dx.doi.org/10.29173/irie345.
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Search engines play an increasingly pivotal role in the distribution and eventual construction of knowledge, yet they are largely unnoticed, their procedures are opaque, and they are almost completely devoid of independent oversight. In this paper the author examines three areas in which we encounter difficult and persistent ethical issues in search engine technology: The problem of algorithm and the lack of transparency of the search process, the problem of privacy with regards of the possibility to monitor search histories, and the problem of local censorship. The given findings lead to the conclusion that the development of structures of accountability for search engines is an important task for the near future.
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Šimek, Pavel, Jiří Vaněk, and Jan Jarolímek. "Quantification of competitive value of documents." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 57, no. 5 (2009): 285–90. http://dx.doi.org/10.11118/actaun200957050285.
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The majority of Internet users use the global network to search for different information using fulltext search engines such as Google, Yahoo!, or Seznam. The web presentation operators are trying, with the help of different optimization techniques, to get to the top places in the results of fulltext search engines. Right there is a great importance of Search Engine Optimization and Search Engine Marketing, because normal users usually try links only on the first few pages of the fulltext search engines results on certain keywords and in catalogs they use primarily hierarchically higher placed links in each category. Key to success is the application of optimization methods which deal with the issue of keywords, structure and quality of content, domain names, individual sites and quantity and reliability of backward links. The process is demanding, long-lasting and without a guaranteed outcome. A website operator without advanced analytical tools do not identify the contribution of individual documents from which the entire web site consists. If the web presentation operators want to have an overview of their documents and web site in global, it is appropriate to quantify these positions in a specific way, depending on specific key words. For this purpose serves the quantification of competitive value of documents, which consequently sets global competitive value of a web site. Quantification of competitive values is performed on a specific full-text search engine. For each full-text search engine can be and often are, different results. According to published reports of ClickZ agency or Market Share is according to the number of searches by English-speaking users most widely used Google search engine, which has a market share of more than 80%. The whole procedure of quantification of competitive values is common, however, the initial step which is the analysis of keywords depends on a choice of the fulltext search engine.
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Azizan, Azilawati, Zainab Abu Bakar, Nurazzah Abd Rahman, Suraya Masrom, and Nurkhairizan Khairuddin. "A Comparative Evaluation of Search Engines on Finding Specific Domain Information on the Web." International Journal of Engineering & Technology 7, no. 4.33 (December 9, 2018): 1. http://dx.doi.org/10.14419/ijet.v7i4.33.23471.
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Recently search engines have provided a truly amazing search service, especially in finding general information on the Web. However, the question arises, does search engine perform the same when seeking domain specific information such as medical, geographical or agriculture information? Along with that issue, an experiment has been conducted to test the effectiveness of today’s search engines from the aspect of information searching in a specific domain. There were four search engines have been selected namely Google, Bing, Yahoo and DuckDuckGo for the experiment. While for the domain specific, we chose to test information about the popular fruit in Southeast Asia that is durian. Precision metric has been used to evaluate the retrieval effectiveness. The findings show that Google has outperformed the other three search engines. Nevertheless, the mean average precision value 0.51 given by Google is still low to be satisfied neither by the researcher nor the information seekers.
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Lukito, Rony Baskoro, Cahya Lukito, and Deddy Arifin. "Penerapan Teknik Seo (Search Engine Optimization) pada Website dalam Strategi Pemasaran melalui Internet." ComTech: Computer, Mathematics and Engineering Applications 5, no. 2 (December 1, 2014): 1050. http://dx.doi.org/10.21512/comtech.v5i2.2363.
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The purpose of this research is how to optimize a web design that can increase the number of visitors. The number of Internet users in the world continues to grow in line with advances in information technology. Products and services marketing media do not just use the printed and electronic media. Moreover, the cost of using the Internet as a medium of marketing is relatively inexpensive when compared to the use of television as a marketing medium. The penetration of the internet as a marketing medium lasted for 24 hours in different parts of the world. But to make an internet site into a site that is visited by many internet users, the site is not only good from the outside view only. Web sites that serve as a medium for marketing must be built with the correct rules, so that the Web site be optimal marketing media. One of the good rules in building the internet site as a marketing medium is how the content of such web sites indexed well in search engines like google. Search engine optimization in the index will be focused on the search engine Google for 83% of internet users across the world using Google as a search engine. Search engine optimization commonly known as SEO (Search Engine Optimization) is an important rule that the internet site is easier to find a user with the desired keywords.
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Nasomyont, Tamrerk, and Nawaporn Wisitpongphan. "A Study on the Relationship between Search Engine Optimization Factors and Rank on Google Search Result Page." Advanced Materials Research 931-932 (May 2014): 1462–66. http://dx.doi.org/10.4028/www.scientific.net/amr.931-932.1462.
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Nowadays, Google has taken a role as a major platform for the Internet search; its search algorithm has been changed periodically, hence being positioned on the first page of the Google search result has a tremendous impact on the online marketing strategy of the organization. Search Engine Optimization (SEO) has been considered as one of the most effective strategies to place the webpage at the top rank of the search engine result. This paper presents the relationship between various SEO factors and the rank in the Search Engine Result Page. The results unravel the key SEO factors, e.g. Backlinks, Keyword, Site structure, that have high degree of relationship with rank in the search engine result page (SERPs).
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Foer, A. A., and S. Vaheesan. "Google: The Unique Case of the Monopolistic Search Engine." Journal of European Competition Law & Practice 4, no. 3 (May 12, 2013): 199–200. http://dx.doi.org/10.1093/jeclap/lpt025.
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Jain, Vivekanand, and Sanjiv Saraf. "Google Search Engine and its Usefulness to Library Professionals." DESIDOC Bulletin of Information Technology 26, no. 5 (September 1, 2006): 23–28. http://dx.doi.org/10.14429/dbit.26.3.3693.
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Yuan, Fong-Ching, and Chao-Hui Lee. "Intelligent sales volume forecasting using Google search engine data." Soft Computing 24, no. 3 (May 8, 2019): 2033–47. http://dx.doi.org/10.1007/s00500-019-04036-w.
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Touzani, Mourad, and Salim Moussa. "Ranking Marketing Journals Using the Search Engine Google Scholar." Marketing Education Review 20, no. 3 (October 2010): 229–47. http://dx.doi.org/10.2753/mer1052-8008200304.
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Vaughan, Liwen. "Discovering business information from search engine query data." Online Information Review 38, no. 4 (June 12, 2014): 562–74. http://dx.doi.org/10.1108/oir-08-2013-0190.
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Purpose – The purpose of this paper is to examine the feasibility of discovering business information from search engine query data. Specifically the study tried to determine whether search volumes of company names are correlated with the companies’ business performance and position data. Design/methodology/approach – The top 50 US companies in the 2012 Fortune 500 list were included in the study. The following business performance and position data were collected: revenues, profits, assets, stockholders’ equity, profits as a percentage of revenues, and profits as a percentage of assets. Data on the search volumes of the company names were collected from Google Trends, which is based on search queries users enter into Google. Google Trends data were collected in the two scenarios of worldwide searches and US searches. Findings – The study found significant correlations between search volume data and business performance and position data, suggesting that search engine query data can be used to discover business information. Google Trends’ worldwide search data were better than the US domestic search data for this purpose. Research limitations/implications – The study is limited to only one country and to one year of data. Practical implications – Publicly available search engine query data such as those from Google Trends can be used to estimate business performance and position data which are not always publicly available. Search engine query data are timelier than business data. Originality/value – This is the first study to establish a relationship between search engine query data and business performance and position data.
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LeVan, Ralph. "OpenSearch and SRU: A Continuum of Searching." Information Technology and Libraries 25, no. 3 (September 1, 2006): 151. http://dx.doi.org/10.6017/ital.v25i3.3346.
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Not all library content can be exposed as HTML pages for harvesting by search engines such as Google and Yahoo!. If a library instead exposes its content through a local search interface, that content can then be found by users of metasearch engines such as A9 and Vivísimo. The functionality provided by the local search engine will affect the functionality of the metasearch engine and the findability of the library’s content. This paper describes that situation and some emerging standards in the metasearch arena that choose different balance points between functionality and ease of implementation.
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Hussain, Aabid, Sumeer Gul, Tariq Ahmad Shah, and Sheikh Shueb. "Retrieval effectiveness of image search engines." Electronic Library 37, no. 1 (February 4, 2019): 173–84. http://dx.doi.org/10.1108/el-07-2018-0142.
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Purpose The purpose of this study is to explore the retrieval effectiveness of three image search engines (ISE) – Google Images, Yahoo Image Search and Picsearch in terms of their image retrieval capability. It is an effort to carry out a Cranfield experiment to know how efficient the commercial giants in the image search are and how efficient an image specific search engine is. Design/methodology/approach The keyword search feature of three ISEs – Google images, Yahoo Image Search and Picsearch – was exploited to make search with keyword captions of photos as query terms. Selected top ten images were used to act as a testbed for the study, as images were searched in accordance with features of the test bed. Features to be looked for included size (1200 × 800), format of images (JPEG/JPG) and the rank of the original image retrieved by ISEs under study. To gauge the overall retrieval effectiveness in terms of set standards, only first 50 result hits were checked. Retrieval efficiency of select ISEs were examined with respect to their precision and relative recall. Findings Yahoo Image Search outscores Google Images and Picsearch both in terms of precision and relative recall. Regarding other criteria – image size, image format and image rank in search results, Google Images is ahead of others. Research limitations/implications The study only takes into consideration basic image search feature, i.e. text-based search. Practical implications The study implies that image search engines should focus on relevant descriptions. The study evaluated text-based image retrieval facilities and thereby offers a choice to users to select best among the available ISEs for their use. Originality/value The study provides an insight into the effectiveness of the three ISEs. The study is one of the few studies to gauge retrieval effectiveness of ISEs. Study also produced key findings that are important for all ISE users and researchers and the Web image search industry. Findings of the study will also prove useful for search engine companies to improve their services.