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Academic literature on the topic 'Search engine'

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Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Search engine"

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MINASYAN, Vladimir. "Search Engine Friendliness of Search Engine Marketing Methods." Journal of Business 3, no. 1 (October 2, 2014): 47–51. http://dx.doi.org/10.31578/job.v3i1.80.

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The article examines search engine friendliness of the most popular and widely used search engine marketing (SEM) methods. In the first part, SEM methods and approaches that are in line with search engines’ official guidelines are examined and analyzed. The second part deals with popular methods which are not welcomed by search engines and are implemented by companies solely for search engine rankings manipulation purposes. Finally, the last section describes several experiments conducted on live websites in order to figure out modern search engines abilities to identify widely used deceptive practices and presents the risks associated with SEM methods which are strongly inconsistent with search engines’ official guidelines.
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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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Jha, Radhika. "Xperia Search Engine." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 6331–40. http://dx.doi.org/10.22214/ijraset.2023.52996.

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Abstract: This research paper delves into the innerworkings of search engines and introduces Xperia, a personalized search engine aimed at enhancing information retrieval. The paper explores the fundamentaltechnologies employed by search engines, tracing their evolution and growth over time. It presents the development and implementation of Xperia, highlighting its unique feature set that goes beyond traditional search engines by providing users with not only relevant resourcelinks but also extracted information from various web sources. The paper begins with an introduction, providing the background and motivation for the research, as well as outlining the research objectives and scope. It then delves into the fundamentals of search engines, discussing their components, crawling and indexing processes, ranking algorithms, and user interfaces. The evolution of search engine technologies is examined, from the early stages to the current advancements in semantic search, natural language processing, and the incorporation of machine learning and artificial intelligence.
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Lee, Sungin, Wonhong Jang, Eunsol Lee, and Sam G. Oh. "Search engine optimization." Library Hi Tech 34, no. 2 (June 20, 2016): 197–206. http://dx.doi.org/10.1108/lht-02-2016-0014.

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Purpose – The purpose of this paper is to examine the effect of, and identify core techniques of, search engine optimization (SEO) techniques applied to the web (http://lg-sl.net) and mobile (http//m.lg-sl.net) Science Land content and services at LG Sangnam Library in Korea. Design/methodology/approach – In accordance with three major SEO guidelines, ten SEO techniques were identified and applied, and their implications were extracted on three areas: improved search engine accessibility, increased relevance between site content and search engine keywords, and improved site credibility. The effects were quantitatively analyzed in terms of registered search engine keywords and influx of visits via search engines. Findings – This study shows that SEO techniques help increase the exposure of the library services and the number of visitors through search engines. Practical implications – SEO techniques have been applied to a few non-Korean information service organizations, but it is not a well-accepted practice in Korean libraries. And the dominant search engines in Korea have published their own SEO guidelines. Prior to this study, no significant endeavors have been undertaken in the context of Korean library services that have adopted SEO techniques to boost exposure of library services and increase user traffics. Originality/value – This is the first published study that has applied optimized SEO techniques to Korean web and mobile library services, in order to demonstrate the usefulness of the techniques for maximized exposure of library content.
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Li, Yi, Zhihui Yuan, Yujie Li, and Jing Liu. "Factors influencing search engine usage behavior." Social Behavior and Personality: an international journal 46, no. 1 (January 9, 2018): 1–10. http://dx.doi.org/10.2224/sbp.6211.

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We analyzed the effect of individual factors, contextual factors, and perception of search engine advertising on users' search engine usage behavior. The sample comprised 404 Chinese who used search engines in the context of their paid employment. Results showed that (a) perceived search skills and perceived search engine reliance significantly and positively impacted users' general search engine usage, (b) perceived advertising clutter reduced the beneficial effects of perceived search skills on users' general search engine usage, (c) users with higher perceived search engine reliance preferred search engines to other online search methods, and (d) prior negative experience reduced the positive link between perceived search engine reliance and users' specific search engine usage. Our findings suggest that search engine designers and operators should focus on individual and contextual factors influencing search engine usage behavior, and should consider users' perception of advertising on search engine programs.
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Goyal, Harsh, and Komal kapoor. "Search Engine Optimization with Google." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 496–501. http://dx.doi.org/10.22214/ijraset.2022.47904.

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Abstract: This paper is a finished survey that how various strategies of Search Engine Optimization (SEO) can help to improve product marketing. Search engines have now become an important channel for increasing SMEs’ global reach and helps companies to compete with other and large companies. SMEs are using search engine optimization to improve their online visibility as a result (SEO). With the help of Search Engine Optimization small companies can actually now compete with the large companies and can appear ahead of large. The main objective of every website to list at the top of all thelinks on search engines. So, search engine optimization is an art which helps to improves a website’s visibility in the search engine results. Search engines makes the business environment more transparent and more competitive. For data collection used literature survey. The findings of this study show that using an SEO strategy can significantly enhance product marketing.
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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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Takama, Yasufumi, Yanjun Zhu, Shogo Kori, Koichi Yamaguchi, Lieu-Hen Chen, and Hiroshi Ishikawa. "Design of Context Search Engine Based on Analysis of User’s Search Intentions." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 6 (November 20, 2016): 910–18. http://dx.doi.org/10.20965/jaciii.2016.p0910.

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The context search engine has been studied for answering trend-related queries. As trend information is obtained from temporal data, which is common in many applications, the context engine is expected to be available regardless of domains. When using existing search engines, it is supposed that users submit a series of queries based on search intention. Therefore, search functions of the context search engine should be designed based on the user’s potential search intention. To analyze user’s behavior in information retrieval, this paper conducted experiments using existing Web search engines. The experimental result is analyzed, based on which the design of a context search engine is described. As another contribution of this paper, new types of temporal variations which can be used to specify queries of the context search engine are also proposed. The results of user experiments confirmed the usability of the proposed temporal variations.
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de Cornière, Alexandre. "Search Advertising." American Economic Journal: Microeconomics 8, no. 3 (August 1, 2016): 156–88. http://dx.doi.org/10.1257/mic.20130138.

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Search engines enable advertisers to target consumers based on the query they have entered. In a framework in which consumers search sequentially after having entered a query, I show that such targeting reduces search costs, improves matches and intensifies price competition. However, a profit-maximizing monopolistic search engine imposes a distortion by charging too high an advertising fee, which may negate the benefits of targeting. The search engine also has incentives to provide a suboptimal quality of sponsored links. Competition among search engines can increase or decrease welfare, depending on the extent of multi-homing by advertisers. (JEL D43, D83, L13, L86, M37)
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Radadiya, Jitendrakumar P., Kalpesh Rasiklal Rakholiya, and Dr Dhaval R. Kathiriya. "Intelligent Sementic Web Search Engine." International Journal of Scientific Research 1, no. 7 (June 1, 2012): 34–35. http://dx.doi.org/10.15373/22778179/dec2012/13.

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Dissertations / Theses on the topic "Search engine"

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

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The prevalence of Search Engine Poisoning in trending topics and popular search terms on the web within search engines is investigated. Search Engine Poisoning is the act of manipulating search engines in order to display search results from websites infected with malware. Research done between February and August 2012, using both manual and automated techniques, shows us how easily the criminal element manages to insert malicious content into web pages related to popular search terms within search engines. In order to provide the reader with a clear overview and understanding of the motives and the methods of the operators of Search Engine Poisoning campaigns, an in-depth review of automated and semi-automated web exploit kits is done, as well as looking into the motives for running these campaigns. Three high profile case studies are examined, and the various Search Engine Poisoning campaigns associated with these case studies are discussed in detail to the reader. From February to August 2012, data was collected from the top trending topics on Google’s search engine along with the top listed sites related to these topics, and then passed through various automated tools to discover if these results have been infiltrated by the operators of Search Engine Poisoning campaings, and the results of these automated scans are then discussed in detail. During the research period, manual searching for Search Engine Poisoning campaigns was also done, using high profile news events and popular search terms. These results are analysed in detail to determine the methods of attack, the purpose of the attack and the parties behind it
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Fahlström, Kamilla, and Caroline Jensen. "Search Engine Marketing in SMEs : The motivations behind using search engine marketing." Thesis, Högskolan i Gävle, Avdelningen för ekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-21116.

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Abstract   Title: Search Engine Marketing in SMEs Level: Final assignment for Bachelor Degree in Business Administration Authors: Kamilla Fahlström & Caroline Jensen Supervisor: Jens Eklinder Frick Date: 2016 January Purpose: The purpose of this study is to use Expectancy theory to describe and analyze small company owners’ motivations for their usage of Search Engine Marketing, in terms of their perceived Valence, Expectancy and Instrumentality. Method: To research the aim of this study a qualitative research approach was used. The empirical data was compiled through ten semi-structured interviews from a varied selection of Swedish companies in the service sector. The data was analyzed with previous research to create an understanding of the motivations for using Search Engine Marketing. Conclusions: The result of this study, when analyzed alongside Expectancy theory, indicates that small business owners are motivated to use Search Engine Marketing. Furthermore, which method of Search Engine Marketing that the owners are motivated to use is dependent on their perceptions of the different methods. Future research: Due to the lack of research into the attitudinal and psychological aspects of Search Engine Marketing and the limitations of this study, it would be interesting if more research were done into this area. For example, it would be interesting to study if trust-based companies are motivated to use Search Engine Marketing, and if demographics affect the motivations. Contribution: This study contributes with results on a previously unexplored area within the research field of Search Engine Marketing. The study also contribute with some information to practice regarding small service company owners’ thoughts about their usage of Search Engine Marketing.  Key words: Search Engine Marketing, SMEs, Expectancy theory, Motivation, Website visibility
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Hurlock, Jonathan. "Twitter search : building a useful search engine." Thesis, Swansea University, 2015. https://cronfa.swan.ac.uk/Record/cronfa43037.

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Millions of digital communications are posted over social media every day. Whilst some state that a large proportion of these posts are considered to be babble, we know that some of these posts actually contain useful information. In this thesis we specifically look at how we can identify reasons as to what makes some of these communications useful or not useful to someone searching for information over social media. In particular we look at what makes messages (tweets) from the social network Twitter useful or not useful users performing search over a corpus of tweets. We identify 16 features that help a tweet be deemed useful, and 17 features as to why a tweet may be deemed not useful to someone performing a search task. From these findings we describe a distributed architecture we have compiled to process large datasets and allow us to perform search over a corpus of tweets. Utilizing this architecture we are able to index tweets based on our findings and describe a crowdsourcing study we ran to help optimize weightings for these features via learning to rank, which quantifies how important each feature is in understanding what makes tweets useful or not for common search tasks performed over twitter. We release a corpus of tweets for the purpose of evaluating other usefulness systems.
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Narayan, Nitesh. "Advanced Intranet Search Engine." Thesis, Mälardalen University, School of Innovation, Design and Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-9408.

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Information retrieval has been a prevasive part of human society since its existence.With the advent of internet and World wide Web it became an extensive area of researchand major foucs, which lead to development of various search engines to locate the de-sired information, mostly for globally connected computer networks viz. internet.Butthere is another major part of computer network viz. intranet, which has not seen muchof advancement in information retrieval approaches, in spite of being a major source ofinformation within a large number of organizations.Most common technique for intranet based search engines is still mere database-centric. Thus practically intranets are unable to avail the benefits of sophisticated tech-niques that have been developed for internet based search engines without exposing thedata to commercial search engines.In this Master level thesis we propose a ”state of the art architecture” for an advancedsearch engine for intranet which is capable of dealing with continuously growing sizeof intranets knowledge base. This search engine employs lexical processing of doc-umetns,where documents are indexed and searched based on standalone terms or key-words, along with the semantic processing of the documents where the context of thewords and the relationship among them is given more importance.Combining lexical and semantic processing of the documents give an effective ap-proach to handle navigational queries along with research queries, opposite to the modernsearch engines which either uses lexical processing or semantic processing (or one as themajor) of the documents. We give equal importance to both the approaches in our design,considering best of the both world.This work also takes into account various widely acclaimed concepts like inferencerules, ontologies and active feedback from the user community to continuously enhanceand improve the quality of search results along with the possibility to infer and deducenew knowledge from the existing one, while preparing for the advent of semantic web.

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

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Search engines have forever changed the way people access and discover knowledge, allowing information about almost any subject to be quickly and easily retrieved within seconds. As increasingly more material becomes available electronically the influence of search engines on our lives will continue to grow. This presents the problem of how to find what information is contained in each search engine, what bias a search engine may have, and how to select the best search engine for a particular information need. This research introduces a new method, search engine content analysis, in order to solve the above problem. Search engine content analysis is a new development of traditional information retrieval field called collection selection, which deals with general information repositories. Current research in collection selection relies on full access to the collection or estimations of the size of the collections. Also collection descriptions are often represented as term occurrence statistics. An automatic ontology learning method is developed for the search engine content analysis, which trains an ontology with world knowledge of hundreds of different subjects in a multilevel taxonomy. This ontology is then mined to find important classification rules, and these rules are used to perform an extensive analysis of the content of the largest general purpose Internet search engines in use today. Instead of representing collections as a set of terms, which commonly occurs in collection selection, they are represented as a set of subjects, leading to a more robust representation of information and a decrease of synonymy. The ontology based method was compared with ReDDE (Relevant Document Distribution Estimation method for resource selection) using the standard R-value metric, with encouraging results. ReDDE is the current state of the art collection selection method which relies on collection size estimation. The method was also used to analyse the content of the most popular search engines in use today, including Google and Yahoo. In addition several specialist search engines such as Pubmed and the U.S. Department of Agriculture were analysed. In conclusion, this research shows that the ontology based method mitigates the need for collection size estimation.
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Edlund, Joakim. "Cognitive Search Engine Optimization." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281882.

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The use of search engines is a common way to navigate through information today. The field of information retrieval is the field of finding documents in large unstructured collections. Within this field there are widely researched baseline solutions to solve this problem. There are also more advanced techniques (often based on machine learning) to improve relevant results further. However, picking the right algorithm or technique when implementing a search engine is no trivial task and deciding which performs better might seem hard. This project takes a commonly used baseline search engine implementation (elasticsearch) and measures its relevance score using standard measurements within the field of information retrieval (precision, recall, f-measure). After establishing a baseline configuration a query expansion algorithm (based on Word2Vec) is implemented in parallel with a recommendation algorithm (collaborative filtering) to compare against each other and the baseline configuration. Finally a combined model using both the query expansion algorithm and collaborative filtering is used to see if they can utilize each other’s strengths to make an even better setup. Findings show that both Word2Vec and collaborative filtering improves relevance over all three measurements (precision, recall, f-measure). These findings could also be confirmed to be significant through statistical analysis. Collaborative filtering seems to be performing better than Word2Vec for the topmost results while Word2Vec improves more the longer the result set is set to be. The combined model did show a significant improvement to all measurements for result sets of sizes 3 and 5 but larger result sets show less of an improvement and even worse performance.
Användandet av sökmotorer är idag en vanlig metod för att navigera genom information. Det akademiska området informationssökning studerar metoder för att hitta dokument inom stora ostrukturerade samlingar av dokument. Det finns flera standardlösningar inom området som ämnas att lösa problemet. Det finns även ett flertal mer avancerade tekniker, ofta baserade på maskininlärning, vars mål är att öka relevansen hos resultaten ytterligare. Att välja rätt algoritm är dock inte trivialt och att avgöra vilken som ger bäst resultat kan tyckas vara svårt. I det här projektet används en ofta använd sökmotor, elasticsearch, i dess standarduppsättning och utvärderas mot vanligen använda mätvärden inom informationssökning (precision, täckning och f-värde). Efter att standaruppsättningens resultat har etablerats så implementeras en frågeutvidgningsalgoritm (query expansion), baserad på Word2Vec, och en rekommendationsalgoritm baserad på collaborative filtering. Alla tre modellerna jämförs senare mot varandra efter de tre mätvärdena. Slutligen implementeras även en kombinerad modell av både Word2Vec och collaborative filtering för att se om det går att nyttja båda modellernas styrkor för en ännu bättre modell. Resultaten visar att både Word2Vec och collaborative filtering ger bättre resultat för alla mätvärden. Resultatförbättringarna kunde verifieras som signifikant bättre efter en statistisk analys. Collaborative filtering verkar prestera bäst när man endast tillåter ett få- tal dokument i resultatmängden medan word2vec blir bättre desto större resultatmängden är. Den kombinerade modellen visade en signifikant förbättring för resultatmängder i storlekarna 3 och 5. Större resultatmängder visade dock ingen förbättring eller till och med en försämring gentemot word2vec och collaborative filtering.
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King, John Douglas. "Search engine content analysis." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/26241/1/John_King_Thesis.pdf.

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Search engines have forever changed the way people access and discover knowledge, allowing information about almost any subject to be quickly and easily retrieved within seconds. As increasingly more material becomes available electronically the influence of search engines on our lives will continue to grow. This presents the problem of how to find what information is contained in each search engine, what bias a search engine may have, and how to select the best search engine for a particular information need. This research introduces a new method, search engine content analysis, in order to solve the above problem. Search engine content analysis is a new development of traditional information retrieval field called collection selection, which deals with general information repositories. Current research in collection selection relies on full access to the collection or estimations of the size of the collections. Also collection descriptions are often represented as term occurrence statistics. An automatic ontology learning method is developed for the search engine content analysis, which trains an ontology with world knowledge of hundreds of different subjects in a multilevel taxonomy. This ontology is then mined to find important classification rules, and these rules are used to perform an extensive analysis of the content of the largest general purpose Internet search engines in use today. Instead of representing collections as a set of terms, which commonly occurs in collection selection, they are represented as a set of subjects, leading to a more robust representation of information and a decrease of synonymy. The ontology based method was compared with ReDDE (Relevant Document Distribution Estimation method for resource selection) using the standard R-value metric, with encouraging results. ReDDE is the current state of the art collection selection method which relies on collection size estimation. The method was also used to analyse the content of the most popular search engines in use today, including Google and Yahoo. In addition several specialist search engines such as Pubmed and the U.S. Department of Agriculture were analysed. In conclusion, this research shows that the ontology based method mitigates the need for collection size estimation.
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Khan, Saiful. "Visualization assisted enterprise search engine." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:d1790b99-c30e-487b-b87e-98d4e3a8b2bb.

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In most organizations, the number of files increases at a rate similar to the growth of data. As one of the big data challenges, many enterprises encounter a common difficulty in a routine operation, that is, finding files in a large-scale file system typically distributed across several physical sites and accessed by thousands of users. This thesis addresses a central question: whether or not visualization techniques can be used to improve the effectiveness and efficiency in performing numerous file searching operations at an industrial scale. All work conducted in this research was done in partnership with Laing O'Rourke as an industrial collaborator. The main technical approaches to support file searching operations include (a) the use of a database to manage searchable records of files and (b) the use of a search engine to add the exploration of a less-structured file repository. With the rapid increase of files, the former approach incurs a huge cost on entering records of files into the database, while the latter suffers from unreliable search results (false positives and false negatives) and difficulties in collaborative search. This thesis focuses on the second approach, that is, to develop a visualization-assisted enterprise search engine. In this thesis, we propose two novel visualization techniques in conjunction with an experimental enterprise search engine. The first technique provides users with focus+context visualization of search results (focus) in relation to the search space (context). This assists users in identifying false positives rapidly, and helps users hypothesize potential false negatives and investigate them through the refinement of search criteria. A number of methods for depicting the multivariate information associated to search results were designed, implemented and compared. Empirical studies were conducted to discover the visual attributes for glyph-based and animation-based methods, and to evaluate different visual designs. The second technique provides users with support for search activities over a period of time and in collaboration. We developed the novel concept of Search Provenance Graph (SPG), and a method for connecting semantically similar queries in SPGs. Methods and software for visualizing SPGs were designed and implemented, enabling users in collaboration to acquire provenance information efficiently and formulate/reformulate queries effectively. In conjunction with the research on visualization techniques, we developed an experimental enterprise search engine, which allows visualization components to be integrated. The search engine is knowledge-based, and is supported by multiple ontologies and crawler agents for exploring the search space. We used query-expansion and results ranking to reduce false positives and negatives, active-learning to enable dynamic learning during search operations, and history-based indexing to facilitate real-time return of search results. This research is the first step towards the development of visualization-assisted enterprise search engines as a new technology that can address a major big data challenge in industry, and can bring a significant amount of cost-effectiveness to everyday operations.
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Slavík, Michal. "Search Engine Marketing neziskových organizací." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-124602.

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The goal of this thesis is to design methodics for Search Engine Marketing (SEM) in nonprofit organizations (NPOs) which takes advantage of their specifics. Other goals include practical evaluation of the methodics and analysis of the current state of NPOs websites. Determined goals are reached by merging theoretical background from relevant literature with the knowledge gained during field research and with author's experience. Designed methodics is built on the following hypotheses: NPOs are able to negotiate better trade terms than trading companies, NPOs can delegate their volunteers to do some SEM activities. Field research confirmed both hypotheses. Hypothesis that NPOs websites are static because NPOs see no profit in regular publishing was disproved. The methodics consists of four phases and also includes recommended tools, metrics, topics for publishing and a list of linkbaiting activities. The thesis consists of five chapters. The first chapter summarizes the necessary theoretical background, while the second chapter defines terms and premises. The main methodics can be found in chapter three. The fourth chapter contains current state analysis based on examination of 31 websites. A comparison of the methodics' hypotheses and activities against the experience of 21 NPOs representatives and 3 experts in the field of SEO is given in the last chapter. Opinions of the both groups of respondents are compared too. Based on the respondents' judgments on costs and utility of the methodics' activities a rank of these activities is finally created. The main contribution of this thesis is a conversion of the universal SEM theory into the specific conditions and language of NPOs practitioners and an analysis of the current state in this field.
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FISTER, JUSTIN M. "CORRELATION ANALYSIS OF ON-PAGE ATTRIBUTES AND SEARCH ENGINE RANKINGS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1178730597.

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Books on the topic "Search engine"

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

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Search engine visibility. 2nd ed. Berkeley, CA: New Riders, 2008.

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

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

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Search Engine Optimization. 2nd ed. Hoboken: Wiley [Imprint], 2009.

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The Search Engine. Philadelphia, USA: American Poetry Review, 2002.

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Search engine society. Cambridge: Polity, 2009.

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Ramos, Andreas. Search Engine Marketing. New York: McGraw-Hill, 2008.

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Ramos, Andreas. Search engine marketing. New York: McGraw-Hill, 2009.

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Search engine visibility. Indianapolis, Ind: New Riders, 2003.

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Book chapters on the topic "Search engine"

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Weik, Martin H. "search engine." In Computer Science and Communications Dictionary, 1528. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_16727.

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Rennie, Frank, and Keith Smyth. "Search engine." In Digital Learning: The Key Concepts, 125–26. 2nd ed. London: Routledge, 2019. http://dx.doi.org/10.4324/9780429425240-167.

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Das, Subhankar. "Search Engine Algorithm and Search Engine Marketing." In Search Engine Optimization and Marketing, 117–80. First edition. | Boca Raton : CRC Press, 2021.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429298509-6.

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Thelwall, Michael. "Automatic Search Engine Searches: LexiURL Searcher." In Introduction to Webometrics: Quantitative Web Research for the Social Sciences, 57–70. Cham: Springer International Publishing, 2009. http://dx.doi.org/10.1007/978-3-031-02261-6_5.

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Goldman, E. "Search Engine Bias and the Demise of Search Engine Utopianism." In Web Search, 121–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75829-7_8.

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Lieberam-Schmidt, Sönke. "Search Engine Optimization." In Analyzing and Influencing Search Engine Results, 163–203. Wiesbaden: Gabler, 2010. http://dx.doi.org/10.1007/978-3-8349-8915-4_5.

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Carterette, Ben. "Search Engine Metrics." In Encyclopedia of Database Systems, 1–6. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_325-2.

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Van Looy, Amy. "Search Engine Optimization." In Social Media Management, 113–32. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-21990-5_6.

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Li, Mingjing, and Wei-Ying Ma. "Image Search Engine." In Encyclopedia of Multimedia, 335–37. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-78414-4_81.

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West, Adrian W. "Search Engine Optimization." In Practical Web Design for Absolute Beginners, 169–82. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1993-5_19.

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Conference papers on the topic "Search engine"

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

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

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

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

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

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

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

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

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Schultz, Carsten D. "SEARCH TRIANGLE: INTEGRATING SEARCH INTENTION IN SEARCH ENGINE ADVERTISING." In Bridging Asia and the World: Global Platform for Interface between Marketing and Management. Global Alliance of Marketing & Management Associations, 2016. http://dx.doi.org/10.15444/gmc2016.12.01.02.

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Zhu, Yunzhang, Gang Wang, Junli Yang, Dakan Wang, Jun Yan, Jian Hu, and Zheng Chen. "Optimizing search engine revenue in sponsored search." In the 32nd international ACM SIGIR conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1571941.1572042.

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Reports on the topic "Search engine"

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

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

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

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

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

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

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Adams, Paige, Pranav Anand, Grant Gehrke, Ralucca Gera, Marco Draeger, Craig Martell, and Kevin Squire. ReSEARCH: A Requirements Search Engine: Progress Report 2. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada529465.

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

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

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Cuevas, Leslie M., Jewon Lyu, and Heejin Lim. Instagram As a Search Engine: Can Browsers Convert to Shoppers? Ames: Iowa State University, Digital Repository, 2017. http://dx.doi.org/10.31274/itaa_proceedings-180814-295.

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