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1
Blaauw, Pieter. "Search engine poisoning and its prevalence in modern search engines." Thesis, Rhodes University, 2013. http://hdl.handle.net/10962/d1002037.
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The prevalence of Search Engine Poisoning in trending topics and popular search terms on the web within search engines is investigated. Search Engine Poisoning is the act of manipulating search engines in order to display search results from websites infected with malware. Research done between February and August 2012, using both manual and automated techniques, shows us how easily the criminal element manages to insert malicious content into web pages related to popular search terms within search engines. In order to provide the reader with a clear overview and understanding of the motives and the methods of the operators of Search Engine Poisoning campaigns, an in-depth review of automated and semi-automated web exploit kits is done, as well as looking into the motives for running these campaigns. Three high profile case studies are examined, and the various Search Engine Poisoning campaigns associated with these case studies are discussed in detail to the reader. From February to August 2012, data was collected from the top trending topics on Google’s search engine along with the top listed sites related to these topics, and then passed through various automated tools to discover if these results have been infiltrated by the operators of Search Engine Poisoning campaings, and the results of these automated scans are then discussed in detail. During the research period, manual searching for Search Engine Poisoning campaigns was also done, using high profile news events and popular search terms. These results are analysed in detail to determine the methods of attack, the purpose of the attack and the parties behind it
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Shen, Yipeng. "Meta-search and distributed search systems /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?COMP%202002%20SHEN.
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Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2002.<br>Includes bibliographical references (leaves 138-144). Also available in electronic version. Access restricted to campus users.
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Natvig, Ola. "Compression in XML search engines." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9119.
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<p>The structure of XML documents can be used by search engines to answer structured queries or to provide better relevancy. Several index structures exist for search in XML data. This study focuses on inverted lists with dictionary coded path types and dewey coded path instances. The dewey coded path index is large, but could be compressed. This study examines query processing with indexes encoded using well known integer coding methods VByte and PFor(delta) and methods tailored for the dewey index. Intersection queries and structural queries are evaluated. In addition to standard document level skipping, skip operations for path types are implemented and evaluated. Four extensions over plain PFor methods are proposed and tested. Path type sorting sorts dewey codes on their path types and store all deweys from one path type together. Column wise dewey storage stores the deweys in columns instead of rows. Prefix coding a well known method, is adapted to the column wise dewey storage, and dynamic column wise method chooses between row wise and column wise storage based on the compressed data. Experiments are performed on a XML collection based on Wikipedia. Queries are generated from the TREC 06 efficiency task query trace. Several different types of structural queries have been executed. Experiments show that column wise methods perform good on both intersection and structural queries. The dynamic column wise scheme is in most cases the best, and creates the smallest index. Special purpose skipping for path types makes some queries extremely fast and can be implemented with only limited storage footprint. The performance of in-memory search with multi-threaded query execution is limited by memory bandwidth.</p>
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Mtshontshi, Lindiwe. "Evaluation and comparison of search engines." Thesis, Stellenbosch : Stellenbosch University, 2004. http://hdl.handle.net/10019.1/49955.
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Thesis (MPhil)--Stellenbosch University, 2004.<br>ENGLISH ABSTRACT: A growing body of studies is developing approaches to evaluate human interaction
with Web search engines. Measuring the information retrieval effectiveness of World
Wide Web search engines is costly because of the human relevance judgements
involved. However, both for business enterprises and people it is important to know
the most effective Web search engine, since such search engines help their users find
a higher number of relevant Web pages with less effort. Furthermore, this information
can be used for several practical purposes. This study does not attempt to describe all
the currently available search engines, but provides a comparison of some, which are
deemed to be among the most useful. It concentrates on search engines and their
characteristics only. The goal is to help a new user get the most useful "hits" when
using the various tools.<br>AFRIKAANSE OPSOMMING: Al hoe meer studies word gedoen om benaderings te ontwikkel vir die evaluasie van
menslike interaksie met Web-soekenjins. Om te meet hoe effektief 'n soekenjin
inligting op die Wêreldwye Web kan opspoor, is duur vanweë die mens se
relevansiebeoordeling wat daarby betrokke is. Dit is egter belangrik dat die
bestuurders van sake-ondememings en ander mense sal weet watter die mees
doeltreffende soekenjins is, aangesien sulke soekenjins hulle gebruikers help om 'n
hoër aantal relevante Webblaaie met minder inspanning te vind. Hierdie inligting
kan ook gebruik word om 'n paar praktiese doelwitte te verwesenlik. Daar word nie
gepoog om al die soekenjins wat tans beskikbaar is, te beskryf nie, maar sommige
van die soekenjins wat as die nuttigste beskou word, word vergelyk. Daar word
alleenlik op soekenjins en hulle kenmerke gekonsentreer. Die doel is om die nuwe
gebruiker te help om die nuttigste inligting te verkry deur gebruik te maak van
verskeie hulpmiddels.
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Zheng, Li. "Towards Next Generation Vertical Search Engines." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1517.
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As the Web evolves unexpectedly fast, information grows explosively. Useful resources become more and more difficult to find because of their dynamic and unstructured characteristics. A vertical search engine is designed and implemented towards a specific domain. Instead of processing the giant volume of miscellaneous information distributed in the Web, a vertical search engine targets at identifying relevant information in specific domains or topics and eventually provides users with up-to-date information, highly focused insights and actionable knowledge representation. As the mobile device gets more popular, the nature of the search is changing. So, acquiring information on a mobile device poses unique requirements on traditional search engines, which will potentially change every feature they used to have. To summarize, users are strongly expecting search engines that can satisfy their individual information needs, adapt their current situation, and present highly personalized search results.
In my research, the next generation vertical search engine means to utilize and enrich existing domain information to close the loop of vertical search engine's system that mutually facilitate knowledge discovering, actionable information extraction, and user interests modeling and recommendation. I investigate three problems in which domain taxonomy plays an important role, including taxonomy generation using a vertical search engine, actionable information extraction based on domain taxonomy, and the use of ensemble taxonomy to catch user's interests. As the fundamental theory, ultra-metric, dendrogram, and hierarchical clustering are intensively discussed. Methods on taxonomy generation using my research on hierarchical clustering are developed. The related vertical search engine techniques are practically used in Disaster Management Domain. Especially, three disaster information management systems are developed and represented as real use cases of my research work.
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Mendoza, Rocha Marcelo Gabriel. "Query log mining in search engines." Tesis, Universidad de Chile, 2007. http://www.repositorio.uchile.cl/handle/2250/102877.
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Doctor en Ciencias, Mención Computación<br>La Web es un gran espacio de información donde muchos recursos como documentos, imágenes u otros contenidos multimediales pueden ser accesados. En este contexto, varias tecnologías de la información han sido desarrolladas para ayudar a los usuarios a satisfacer sus necesidades de búsqueda en la Web, y las más usadas de estas son los motores de búsqueda. Los motores de búsqueda permiten a los usuarios encontrar recursos formulando consultas y revisando una lista de respuestas.
Uno de los principales desafíos para la comunidad de la Web es diseñar motores de búsqueda que permitan a los usuarios encontrar recursos semánticamente conectados con sus consultas. El gran tamaño de la Web y la vaguedad de los términos más comúnmente usados en la formulación de consultas es un gran obstáculo para lograr este objetivo.
En esta tesis proponemos explorar las selecciones de los usuarios registradas en los logs de los motores de búsqueda para aprender cómo los usuarios buscan y también para diseñar algoritmos que permitan mejorar la precisión de las respuestas recomendadas a los usuarios. Comenzaremos explorando las propiedades de estos datos. Esta exploración nos permitirá determinar la naturaleza dispersa de estos datos. Además presentaremos modelos que nos ayudarán a entender cómo los usuarios buscan en los motores de búsqueda.
Luego, exploraremos las selecciones de los usuarios para encontrar asociaciones útiles entre consultas registradas en los logs. Concentraremos los esfuerzos en el diseño de técnicas que permitirán a los usuarios encontrar mejores consultas que la consulta original. Como una aplicación, diseñaremos métodos de reformulación de consultas que ayudarán a los usuarios a encontrar términos más útiles mejorando la representación de sus necesidades.
Usando términos de documentos construiremos representaciones vectoriales para consultas. Aplicando técnicas de clustering podremos determinar grupos de consultas similares. Usando estos grupos de consultas, introduciremos métodos para recomendación de consultas y documentos que nos permitirán mejorar la precisión de las recomendaciones.
Finalmente, diseñaremos técnicas de clasificación de consultas que nos permitirán encontrar conceptos semánticamente relacionados con la consulta original. Para lograr esto, clasificaremos las consultas de los usuarios en directorios Web. Como una aplicación, introduciremos métodos para la manutención automática de los directorios.
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Zamurenko. "WEBSITE PROMOTION TECHNIQUES IN SEARCH ENGINES." Thesis, Київ 2018, 2018. http://er.nau.edu.ua/handle/NAU/33932.
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SHARMA, AMARENDRA KUMAR. "ESSAYS IN INTERNET ECONOMICS." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1028832870.
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Henriksson, Adam. "Alternative Search : From efficiency to experience." Thesis, Umeå universitet, Institutionen Designhögskolan, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-97836.
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Search engines of today are focusing on efficiently and accurately generating search results.Yet, there is much to be explored in the way people interact with the applications and relate to the content. Individuals are commonly unique, with complex preferences, motives and expectations. Not only is it important to be sensitive to these differences, but to accommodate the extremes. Enhancing a search engine does not only rely on technological development, but to explore potential user experiences in broader perspectives - which not only gratifies the needs for information, but supports a diversity of journeys. The aim of the project is to develop an alternate search engine with different functionality based on new values that reflects contemporary needs. The result, Exposeek, is an experiential prototype supporting exploratory browsing based on principles of distributed infrastructure, transparent computation and serendipitous information. Suggestive queries, legible algorithms and augmented results provide additional insights and present an alternative way to seek and peruse the Web.<br>Search Engines, Interaction Design
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Скиданенко, Максим Сергійович, Максим Сергеевич Скиданенко, Maksym Serhiiovych Skydanenko, and A. S. Skidanenko. "Visual search engines as a search tool in the learning process." Thesis, Сумський державний університет, 2012. http://essuir.sumdu.edu.ua/handle/123456789/29424.
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The main objective of the University is the promotion of successful professionals who have practical skills, can predict, model, process information, and integrate the knowledge obtained in a higher educational establishment.
When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/29424
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Zhang, Lu Jansen Bernard J. "A branding model for web search engines." [University Park, Pa.] : Pennsylvania State University, 2009. http://etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-3996/index.html.
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Erola, Cañellas Arnau. "Contributions to privacy in web search engines." Doctoral thesis, Universitat Rovira i Virgili, 2013. http://hdl.handle.net/10803/130934.
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Els motors de cerca d’Internet recullen i emmagatzemen informació sobre els seus usuaris per tal d’oferir-los millors serveis. A canvi de rebre un servei personalitzat, els usuaris perden el control de les seves pròpies dades. Els registres de cerca poden revelar informació sensible de l’usuari, o fins i tot revelar la seva identitat. En aquesta tesis tractem com limitar aquests problemes de privadesa mentre mantenim suficient informació a les dades.
La primera part d’aquesta tesis tracta els mètodes per prevenir la recollida d’informació per part dels motores de cerca. Ja que aquesta informació es requerida per oferir un servei precís, l’objectiu es proporcionar registres de cerca que siguin adequats per proporcionar personalització. Amb aquesta finalitat, proposem un protocol que empra una xarxa social per tal d’ofuscar els perfils dels usuaris.
La segona part tracta la disseminació de registres de cerca. Proposem tècniques que la permeten, proporcionant k-anonimat i minimitzant la pèrdua d’informació.<br>Web Search Engines collects and stores information about their users in order to tailor their services better to their users' needs. Nevertheless, while receiving a personalized attention, the users lose the control over their own data. Search logs can disclose sensitive information and the identities of the users, creating risks of privacy breaches. In this thesis we discuss the problem of limiting the disclosure risks while minimizing the information loss.
The first part of this thesis focuses on the methods to prevent the gathering of information by WSEs. Since search logs are needed in order to receive an accurate service, the aim is to provide logs that are still suitable to provide personalization. We propose a protocol which uses a social network to obfuscate users' profiles.
The second part deals with the dissemination of search logs. We propose microaggregation techniques which allow the publication of search logs, providing $k$-anonymity while minimizing the information loss.
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Kraft, Reiner. "Cost-effective creation of specialized search engines /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2005. http://uclibs.org/PID/11984.
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Risvik, Knut Magne. "Scaling Internet Search Engines - Methods and Analysis." Doctoral thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2004. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-1683.
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<p>This thesis focuses on methods and analysis for building scalable Internet Search Engines. In this work, we have developed a search kernel, an architecture framework and applications that are being used in industrial and commercial products. Furthermore, we present both analysis and design of key elements. </p><p>Essential to building a large-scale search engine is to understand the dynamics of the content in which we are searching. For the challenging case of searching the web, there are multiple dimensions of dynamics that should ideally be handled. In this thesis we start by examining some of these dimensions and the implications they have on search engine design.</p><p>When designing a search engine kernel, the focus has been on selection of algorithms and datastructures in the general case. Also, and even more important, we design worst-case characteristics into the search kernel that are very decisive from a scaling standpoint. A performance model to analyze the behaviour of the kernel is also developed. </p><p>The designed search engine kernel was realized as a predecessor of the current FAST Search kernel (the FMS kernel), and practical experiments and benchmarking demonstrate the correctness of the assumptions from the design of the kernel. </p><p>Then a framework for scaling shared-nothing systems based upon nodes working on separate portions of the data is introduced. The design of the framework is based on the general principles of replication and distribution. A performance model and an algorithm for cluster design are provided. This is in turn applied to construct a larger-scale web search engine and benchmarking of clusters indicate that the assumptions and models for the distributed architecture hold. </p><p>The scaling aspect of search engine is further studied in the context of the application itself. Query locality is explored and used to create an architecture that is a generalized type of caching (through partial replication) using the application behaviour and a configurable correctness trade-off to design super-linear scalable search engines.</p><p>Finally, a discussion of how linguistics are being used in web search engines is provided, focusing on the constraints that apply to ensure the desired scalability. </p>
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Alhalabi, Wadee Saleh. "Induction-Based Approach to Personalized Search Engines." Scholarly Repository, 2008. http://scholarlyrepository.miami.edu/oa_dissertations/106.
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In a document retrieval system where data is stored and compared with a specific query and then compared with other documents, we need to find the document that is most similar to the query. The most similar document will have the weight higher than other documents. When more than one document are proposed to the user, these documents have to be sorted according to their weights. Once the result is presented to the user by a recommender system, the user may check any document of interest. If there are two different documents' lists, as two proposed results presented by different recommender systems, then, there is a need to find which list is more efficient. To do so, the measuring tool "Search Engine Ranking Efficiency Evaluation Tool [SEREET]" came to existence. This tool assesses the efficiency of each documents list and assigns a numerical value to the list. The value will be closer to 100% if the ranking list efficiency is high which means more relevance documents exist in the list and documents are sorted according to their relevance to the user. The value will be closer to 0% when the ranking list efficiency is poor and all of the presented documents are uninteresting documents to the user. A model to evaluate ranking efficiency is proposed in the dissertation, then it is proved it mathematically. Many mechanisms of search engine have been proposed in order to assess the relevance of a web page. They have focused on keyword frequency, page usage, link analysis and various combinations of them. These methods have been tested and used to provide the user with the most interesting web pages, according to his or her preferences. The collaborative filtering is a new approach, which was developed in this dissertation to retrieve the most interesting documents to the user according to his or her interests. Building a user profile is a very important issue in finding the user interest and categorizes each user in a suitable category. This is a requirement in collaborative filtering implementation. The inference tools such as time spent in a web page, mouse movement, page scrolling, mouse clicks and other tools were investigated. Then the dissertation shows that the most efficient and sufficient tool is the time a user spent on a web page. To eliminate errors, the system introduces a low threshold and high threshold for each user. Once the time spent on a web page breaks this threshold, an error is reported. SEREET tool is one of the contributions to the scientific society, which measures the efficiency of a search engine ranking list. Considerable work were carried, then the conclusion was that the amount of time spent on a web page is the most important factor in determining a user interest of a web page and also it is a sufficient tool which does not require collaborations from other tools such as mouse movements or a page scrolling. The results show that implicit rating is a satisfactory measure and can replace explicit rating. New filtering technique was introduced to design a fully functional recommender system. The linear vector algorithm which was introduced improves the vector space algorithm (VSA) in time complexity and efficiency. The use of machine learning enhances the retrieved list efficiency. Machine learning algorithm uses positive and negative examples for the training, these examples are mandatory to improve the error rate of the system. The result shows that the amount of these examples increases proportionally with the error rate of the system.
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Mahalingam, Gayathri. "Natural language access to Internet search engines." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq30514.pdf.
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Jonassen, Simon. "Efficient Query Processing in Distributed Search Engines." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-20206.
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Web search engines have to deal with a rapidly increasing amount of information, high query loads and tight performance constraints. The success of a search engine depends on the speed with which it answers queries (efficiency) and the quality of its answers (effectiveness). These two metrics have a large impact on the operational costs of the search engine and the overall user satisfaction, which determine the revenue of the search engine. In this context, any improvement in query processing efficiency can reduce the operational costs and improve user satisfaction, hence improve the overall benefit. In this thesis, we elaborate on query processing efficiency, address several problems within partitioned query processing, pruning and caching and propose several novel techniques: First, we look at term-wise partitioned indexes and address the main limitations of the state-of-the-art query processing methods. Our first approach combines the advantage of pipelined and traditional (non-pipelined) query processing. This approach assumes one disk access per posting list and traditional term-at-a-time processing. For the second approach, we follow an alternative direction and look at document-at-a-time processing of sub-queries and skipping. Subsequently, we present several skipping extensions to pipelined query processing, which as we show can improve the query processing performance and/or the quality of results. Then, we extend one of these methods with intra-query parallelism, which as we show can improve the performance at low query loads. Second, we look at skipping and pruning optimizations designed for a monolithic index. We present an efficient self-skipping inverted index designed for modern index compression methods and several query processing optimizations. We show that these optimizations can provide a significant speed-up compared to a full (non-pruned) evaluation and reduce the performance gap between disjunctive (OR) and conjunctive (AND) queries. We also propose a linear programming optimization that can further improve the I/O, decompression and computation efficiency of Max-Score. Third, we elaborate on caching in Web search engines in two independent contributions. First, we present an analytical model that finds the optimal split in a static memory-based two-level cache. Second, we present several strategies for selecting, ordering and scheduling prefetch queries and demonstrate that these can improve the efficiency and effectiveness of Web search engines. We carefully evaluate our ideas either using a real implementation or by simulation using real-world text collections and query logs. Most of the proposed techniques are found to improve the state-of-the-art in the conducted empirical studies. However, the implications and applicability of these techniques in practice need further evaluation in real-life settings.
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Chakravarty, Rupak, and Sukhwinder Randhawa. "ACADEMIC SEARCH ENGINES: LIBRARIAN'S FRIEND,RESEARCHER'S DELIGHT." INFLIBNET Centre, 2006. http://hdl.handle.net/10150/105232.
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Search engines are about excitement, optimism, hope and enrichment. Search engines are also about despair and disappointment. A researcher while using search engines for resource discovery might have experienced one or the other sentiments. One may say that user satisfaction depends much upon the search strategies deployed by the user. But at the same time its also depends upon the quality of search engine used for information retrieval. Today, there are many search engines used for resource discovery. They display the results of the searches made in readily-comprehensible manner with lots of customization possibilities including refining and sorting. This paper is an attempt to analyze qualitatively and quantitatively the three most used and popular search engines for academic resource discovery: Google Scholar, Scirus and Windows Live Academic.
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Lewandowski, Dirk. "Web Searching, Search Engines and Information Retrieval." ISO Press, 2005. http://hdl.handle.net/10150/106395.
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This article discusses Web search engines; mainly the challenges in indexing the World Wide Web, the user behaviour, and the ranking factors used by these engines. Ranking factors are divided into query-dependent and query-independent factors, the latter of which have become
more and more important within recent years. The possibilities of these factors are limited, mainly of those that are based on the widely used link popularity measures. The article concludes with an overview of factors that should be considered to determine the quality of Web
search engines.
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Petit, Albin. "Introducing privacy in current web search engines." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEI016/document.
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Au cours des dernières années les progrès technologiques permettant de collecter, stocker et traiter d'importantes quantités de données pour un faible coût, ont soulevés de sérieux problèmes concernant la vie privée. La protection de la vie privée concerne de nombreux domaines, en particulier les sites internet fréquemment utilisés comme les moteurs de recherche (ex. : Google, Bing, Yahoo!). Ces services permettent aux utilisateurs de retrouver efficacement du contenu sur Internet en exploitant leurs données personnelles. Dans ce contexte, développer des solutions pour permettre aux utilisateurs d'utiliser ces moteurs de recherche tout en protégeant leurs vies privées est devenu primordial. Dans cette thèse, nous introduirons SimAttack, une attaque contre les solutions protégeant la vie privée de l'utilisateur dans ses interactions avec les moteurs de recherche. Cette attaque vise à retrouver les requêtes initialement envoyées par l'utilisateur. Nous avons montré avec cette attaque que trois mécanismes représentatifs de l’état de l’art ne sont pas satisfaisants pour protéger la vie privée des utilisateurs. Par conséquent, nous avons développé PEAS, un nouveau mécanisme de protection qui améliore la protection de la vie privée de l'utilisateur. Cette solution repose sur deux types de protection : cacher l'identité de l'utilisateur (par une succession de deux serveurs) et masquer sa requête (en la combinant avec des fausses requêtes). Afin de générer des fausses requêtes réalistes, PEAS se base sur les précédentes requêtes envoyées par les utilisateurs du système. Pour finir, nous présenterons des mécanismes permettant d'identifier la sensibilité des requêtes. Notre objectif est d'adapter les mécanismes de protection existants pour protéger uniquement les requêtes sensibles, et ainsi économiser des ressources (ex. : CPU, mémoire vive). Nous avons développé deux modules pour identifier les requêtes sensibles. En déployant ces modules sur des mécanismes de protection existants, nous avons établi qu'ils permettent d'améliorer considérablement leurs performances<br>During the last few years, the technological progress in collecting, storing and processing a large quantity of data for a reasonable cost has raised serious privacy issues. Privacy concerns many areas, but is especially important in frequently used services like search engines (e.g., Google, Bing, Yahoo!). These services allow users to retrieve relevant content on the Internet by exploiting their personal data. In this context, developing solutions to enable users to use these services in a privacy-preserving way is becoming increasingly important. In this thesis, we introduce SimAttack an attack against existing protection mechanism to query search engines in a privacy-preserving way. This attack aims at retrieving the original user query. We show with this attack that three representative state-of-the-art solutions do not protect the user privacy in a satisfactory manner. We therefore develop PEAS a new protection mechanism that better protects the user privacy. This solution leverages two types of protection: hiding the user identity (with a succession of two nodes) and masking users' queries (by combining them with several fake queries). To generate realistic fake queries, PEAS exploits previous queries sent by the users in the system. Finally, we present mechanisms to identify sensitive queries. Our goal is to adapt existing protection mechanisms to protect sensitive queries only, and thus save user resources (e.g., CPU, RAM). We design two modules to identify sensitive queries. By deploying these modules on real protection mechanisms, we establish empirically that they dramatically improve the performance of the protection mechanisms
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Kalinov, Pavel. "Intelligent Web Exploration." Thesis, Griffith University, 2012. http://hdl.handle.net/10072/365635.
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The hyperlinked part of the internet known as "the Web" arose without much planning for a future of millions of publishers and countless pieces of online content. It has no in-built mechanism to find anything, so tools external to it were introduced: initially web directories and then search engines. Search engines are based on machine learning and have been extremely successful. However, they have some inherent limitations and cannot, by design, address some needs: they serve the "information locating" need only and not "information discovery". Search engine users have learned to accept them and in many cases do not realise how their search has been limited by shortcomings of the model. Before the advent of the search engine, web directories were the only information-finding tool on the web. They were manually built and could not compete economically with the effciency of search engines. This lead to their virtual extinction, with the effect that the "information discovery" need of users is no longer served by any major information provider. Furthermore, none of the dominant information-finding models account for the person of the user in any meaningful way controllable by (or even visible to) the user. This work proposes a method to combine a search engine, a web directory and a personal information management agent into an intelligent Web Exploration Engine in a way which bridges the gaps between these seemingly unrelated tools. Our hybrid, for which we have developed a proof-of-concept prototype [Kalinov et al., 2010b], allows users to both locate specific data and to discover new information. Information discovery is served by a web directory which is built with the assistance of a dynamic hierarchical classifier we developed [Kalinov et al., 2010a]. The category structure achieved by it is also the basis of a large number of nested search engines, allowing information locating both in general (similar to a "standard" search engine) and in a variety of contexts selectable by the user.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>School of Information and Communication Technology<br>Science, Environment, Engineering and Technology<br>Full Text
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Bjørklund, Truls A. "Column Stores versus Search Engines and Applications to Search in Social Networks." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-14782.
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Search engines and database systems both play important roles as we store and organize ever increasing amounts of information and still require the information to be easily accessible. Research on these two types of systems has traditionally been partitioned into two fields, information retrieval and databases, and the integration of these two fields has been a popular research topic. Rather than attempting to integrate the two fields, this thesis begins with a comparison of the technical similarities between search engines and a specific type of database system often used in decision support systems: column stores. Based on an initial assessment of the technical similarities, which includes an evaluation of the feasibility of creating a hybrid system that supports both workloads, the papers in this thesis investigate how the identi_ed similarities can be used as a basis for improving the effciency of the different systems. To improve the efficiency of processing decision support workloads, the use of inverted indexes as an alternative to bitmap indexes is evaluated. We develop a query processing framework for compressed inverted indexes in decision support workloads and find that it outperforms state-of-the-art compressed bitmap indexes by being significantly more compact, and also improves the query processing e_ciency for most queries. Keyword search in social networks with access control is also addressed in this thesis, and a space of solutions is developed along two axes. One of the axes defines the set of inverted indexes that are used in the solution, and the other defines the meta-data used to filter out inaccessible results. With a exible and efficient search system based on a column-oriented storage system, we conduct a thorough set of experiments that illuminate the trade-offs between different extremes in the solution space. We also develop a hybrid scheme in between two of the best extremes. The hybrid approach uses cost models to find the most efficient solution for a particular workload. Together with an effcient query processing framework based on our novel HeapUnion operator, this results in a system that is e_cient for a wide range of workloads that consist of updates and searches with access control in a social network.
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Martin, Carlstedt. "Using NLP and context for improved search result in specialized search engines." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-35181.
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Öfverman, Jakob. "Information Presentation in Search Engines on Mobile Devices." Thesis, Linnaeus University, School of Computer Science, Physics and Mathematics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-7945.
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<p>This thesis discusses the possibilities to visualise the presentation layer of a search engine on a mobile device in an alternative way. Previous work in the area has shown that the use of text-based-lists can be problematic when accessed on a device with a limited display. In the scope of the thesis and in order to tackle the current problems when displaying the results a literature review was carried out. The findings of the review formed the basis for a requirement definition on which a mock-up was developed. The mock-up was then evaluated and tested during a usability test where a number of users got to experience the alternative presentation layer that uses a visualisation technique called tree- map. The results from the test show that the mock-up could be seen as a alternative to the current presentation of results. The mock-up also shows that a future implementation could also include the use of categories and sorting of information in order to provide content with a meaning.</p>
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Økland, Vegard. "Utilizing linguistic analysis in multiple source search engines." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-14468.
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Modern search engines have several data sources available to users, e.g. Newssearch, Image search and Video search. When a user enters a query in a searchengine, it is up to the user to choose a different source than the normal web search.On average, a user will only consider the first few occurrences in a search result anddo so in a few seconds. It would therefore be beneficial to the user experienceif the user did not have to limit the sources manually to refine a search.This project will evaluate different machine learning methods to classify relevantsources to a query. The goal of this is having an automated learning system thattakes some labeled input and uses this to help inform or direct the user to therelevant source.The project will take advantage of a Yahoo! product; Yahoo! Query LinguistAnalysis Service (abbreviated QLAS from now on and through the document). Thegoal is to incorporate semantic data from QLAS into the learning system. Thisshould augment the amount of information available to the learning system, andimprove its performance. It is not clear how this semantic data could be combinedwith the training data and incorporated in the learning system. A substantial partof the project will be to explore this.This project was done in cooperation with Yahoo! Technologies Norway AS (YTN).YTN develops Vespa, a search engine platform that has the possibility to searchfrom multiple sources. YTN is interested in researching the field of learning sourcerelevance to improve the search experience in Yahoo services. YTN is also interestedin researching ways data from QLAS could be used by Vespa to enable sourcerelevance classification when Vespa is used in a multiple-index setup.
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Pinkerton, Brian. "WebCrawler : finding what people want /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/6890.
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Zhao, Hongkun. "Automatic wrapper generation for the extraction of search result records from search engines." Diss., Online access via UMI:, 2007.
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Bian, Jiang. "Contextualized web search: query-dependent ranking and social media search." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37246.
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Due to the information explosion on the Internet, effective information search techniques are required to retrieve the desired information from the Web. Based on much analysis on users' search intention and the variant forms of Web content, we find that both the query and the indexed web content are often associated with various context information, which can provide much essential information to indicate the ranking relevance in Web search. This dissertation seeks to develop new search algorithms and techniques by taking advantage of rich context information to improve search quality and consists of two major parts.
In the first part, we study the context of the query in terms of various ranking objectives of different queries. In order to improve the ranking relevance, we propose to incorporate such query context information into the ranking model. Two general approaches will be introduced in the following of this dissertation. The first one proposes to incorporate query difference into ranking by introducing query-dependent loss functions, by optimizing which we can obtain better ranking model. Then, we investigate another approach which applies a divide-and-conquer framework for ranking specialization.
The second part of this dissertation investigates how to extract the context of specific Web content and explore them to build more effective search system. This study is based on the new emerging social media content. Unlike traditional Web content, social media content is inherently associated with much new context information, including content semantics and quality, user reputation, and user interactions, all of which provide useful information for acquiring knowledge from social media. In this dissertation, we seek to develop algorithms and techniques for effective knowledge acquisition from collaborative social media environments by using the dynamic context information. We first propose a new general framework for searching social media content, which integrates both the content features and the user interactions. Then, a semi-supervised framework is proposed to explicitly compute content quality and user reputation, which are incorporated into the search framework to improve the search quality. Furthermore, this dissertation also investigates techniques for extracting the structured semantics of social media content as new context information, which is essential for content retrieval and organization.
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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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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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Chen, Yan. "Enhanced Web Search Engines with Query-Concept Bipartite Graphs." Digital Archive @ GSU, 2010. http://digitalarchive.gsu.edu/cs_diss/54.
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With rapid growth of information on the Web, Web search engines have gained great momentum for exploiting valuable Web resources. Although keywords-based Web search engines provide relevant search results in response to users’ queries, future enhancement is still needed. Three important issues include (1) search results can be diverse because ambiguous keywords in queries can be interpreted to different meanings; (2) indentifying keywords in long queries is difficult for search engines; and (3) generating query-specific Web page summaries is desirable for Web search results’ previews. Based on clickthrough data, this thesis proposes a query-concept bipartite graph for representing queries’ relations, and applies the queries’ relations to applications such as (1) personalized query suggestions, (2) long queries Web searches and (3) query-specific Web page summarization. Experimental results show that query-concept bipartite graphs are useful for performance improvement for the three applications.
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Martins, Flávio Nuno Fernandes. "Improving search engines with open Web-based SKOS vocabularies." Master's thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8745.
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Dissertação para obtenção do Grau de Mestre em Engenharia Informática<br>The volume of digital information is increasingly larger and even though organiza-tions are making more of this information available, without the proper tools users have great difficulties in retrieving documents about subjects of interest. Good infor-mation retrieval mechanisms are crucial for answering user information needs.
Nowadays, search engines are unavoidable - they are an essential feature in docu-ment management systems. However, achieving good relevancy is a difficult problem particularly when dealing with specific technical domains where vocabulary mismatch problems can be prejudicial. Numerous research works found that exploiting the lexi-cal or semantic relations of terms in a collection attenuates this problem.
In this dissertation, we aim to improve search results and user experience by inves-tigating the use of potentially connected Web vocabularies in information retrieval en-gines. In the context of open Web-based SKOS vocabularies we propose a query expan-sion framework implemented in a widely used IR system (Lucene/Solr), and evaluated using standard IR evaluation datasets.
The components described in this thesis were applied in the development of a new search system that was integrated with a rapid applications development tool in the context of an internship at Quidgest S.A.<br>Fundação para a Ciência e Tecnologia - ImTV research project, in the context of the UTAustin-Portugal collaboration (UTA-Est/MAI/0010/2009); QSearch project (FCT/Quidgest)
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Westerdahl, Simon, and Larsson Fredrik Lemón. "Optimization for search engines based on external revision database." Thesis, Högskolan Kristianstad, Fakulteten för naturvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-21000.
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The amount of data is continually growing and the ability to efficiently search through vast amounts of data is almost always sought after. To efficiently find data in a set there exist many technologies and methods but all of them cost in the form of resources like cpu-cycles, memory and storage. In this study a search engine (SE) is optimized using several methods and techniques. Thesis looks into how to optimize a SE that is based on an external revision database.The optimized implementation is compared to a non-optimized implementation when executing a query. An artificial neural network (ANN) trained on a dataset containing 3 years normal usage at a company is used to prioritize within the resultset before returning the result to the caller. The new indexing algorithms have improved the document space complexity by removing all duplicate documents that add no value. Machine learning (ML) has been used to analyze the user behaviour to reduce the necessary amount of documents that gets retrieved by a query.
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Selberg, Erik Warren. "Towards comprehensive Web search /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/6873.
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Saunders, Tana. "Evaluation of Internet search tools instrument design." Thesis, Stellenbosch : Stellenbosch University, 2004. http://hdl.handle.net/10019.1/49957.
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Thesis (MPhil)--Stellenbosch University, 2004.<br>ENGLISH ABSTRACT: This study investigated Internet search tools / engines to identify desirable features that can be
used as a benchmark or standard to evaluate web search engines. In the past, the Internet was
thought of as a big spider's web, ultimately connecting all the bits of information. It has now
become clear that this is not the case, and that the bow tie analogy is more accurate. This
analogy suggests that there is a central core of well-connected pages, with links IN and OUT to
other pages, tendrils and orphan pages. This emphasizes the importance of selecting a search
tool that is well connected and linked to the central core. Searchers must take into account that
not all search tools search the Invisible Web and this will reflect on the search tool selected. Not
all information found on the Web and Internet is reliable, current and accurate, and Web
information must be evaluated in terms of authority, currency, bias, purpose of the Web site, etc.
Different kinds of search tools are available on the Internet, such as search engines, directories,
library gateways, portals, intelligent agents, etc. These search tools were studied and explored. A
new categorization for online search tools consisting of Intelligent Agents, Search Engines,
Directories and Portals / Hubs is suggested. This categorization distinguishes the major
differences between the 21 kinds of search tools studied. Search tools / engines consist of
spiders, crawlers, robots, indexes and search tool software. These search tools can be further
distinguished by their scope, internal or external searches and whether they search Web pages
or Web sites. Most search tools operate within a relationship with other search tools, and they
often share results, spiders and databases. This relationship is very dynamic. The major
international search engines have identifiable search features. The features of Google, Yahoo,
Lycos and Excite were studied in detail. Search engines search for information in different ways,
and present their results differently. These characteristics are critical to the Recall/Precision ratio.
A well-planned search strategy will improve the Precision/Recall ratio and consider the web-user
capabilities and needs. Internet search tools/engines is not a panacea for all information needs,
and have pros and cons. The Internet search tool evaluation instrument was developed based on
desirable features of the major search tools, and is considered a benchmark or standard for
Internet search tools. This instrument, applied to three South African search tools, provided
insight into the capabilities of the local search tools compared to the benchmark suggested in this
study. The study concludes that the local search engines compare favorably with the major ones,
but not enough so to use them exclusively. Further research into this aspect is needed. Intelligent
agents are likely to become more popular, but the only certainty in the future of Internet search
tools is change, change, and change.<br>AFRIKAANSE OPSOMMING: Hierdie studie het Internetsoekinstrumente/-enjins ondersoek met die doel om gewenste
eienskappe te identifiseer wat as 'n standaard kan dien om soekenjins te evalueer. In die verlede
is die Internet gesien as 'n groot spinnerak, wat uiteindelik al die inligtingsdeeltjies verbind. Dit
het egter nou duidelik geword dat dit glad nie die geval is nie, en dat die strikdas analogie meer
akkuraat is. Hierdie analogie stel voor dat daar 'n sentrale kern van goed gekonnekteerde
bladsye is, met skakels IN en UIT na ander bladsye, tentakels en weesbladsye. Dit beklemtoon
die belangrikheid om die regte soekinstrument te kies, naamlik een wat goed gekonnekteer is, en
geskakel is met die sentrale kern van dokumente. Soekers moet in gedagte hou dat nie alle
soekenjins in die Onsigbare Web soek nie, en dit behoort weerspieël te word in die keuse van die
soekinstrument. Nie alle inligting wat op die Web en Internet gevind word is betroubaar, op
datum en akkuraat nie, en Web-inligting moet geëvalueer word in terme van outoriteit, tydigheid,
vooroordeel, doel van die Webruimte, ens. Verskillende soorte soekinstrumente is op die Internet
beskikbaar, soos soekenjins, gidse, biblioteekpoorte, portale, intelligente agente, ens. Hierdie
soekinstrumente is bestudeer en verken. 'n Nuwe kategorisering vir aanlyn soekinstrumente
bestaande uit Intelligente Agente, Soekinstrumente, Gidse en Portale/Middelpunte word
voorgestel. Hierdie kategorisering onderskei die hoofverskille tussen die 21 soorte
soekinstrumente wat bestudeer is. Soekinstrumente/-enjins bestaan uit spinnekoppe, kruipers,
robotte, indekse en soekinstrument sagteware. Hierdie soekinstrumente kan verder onderskei
word deur hulle omvang, interne of eksterne soektogte en of hulle op Webbladsye of Webruimtes
soek. Die meeste soekinstrumente werk in verhouding met ander soekinstrumente, en hulle deel
dikwels resultate, spinnekoppe en databasisse. Hierdie verhouding is baie dinamies. Die hoof
internasionale soekenjins het soekeienskappe wat identifiseerbaar is. Die eienskappe van
Google, Yahoo en Excite is in besonderhede bestudeer. Soekenjins soek op verskillende
maniere na inligting, en lê hulle resultate verskillend voor. Hierdie karaktereienskappe is krities
vir die Herwinning/Presisie verhouding. 'n Goedbeplande soekstrategie sal die
Herwinning/Presisie verhouding verbeter. Internet soekinstrumente/-enjins is nie die
wondermiddel vir alle inligtingsbehoeftes nie, en het voor- en nadele. Die Internet soekinstrument
evalueringsmeganisme se ontwikkeling is gebaseer op gewenste eienskappe van die hoof
soekinstrumente, en word beskou as 'n standaard vir Internet soekinstrumente. Hierdie
instrument, toegepas op drie Suid-Afrikaanse soekenjins, het insae verskaf in die
doeltreffendheid van die plaaslike soekinstrumente soos vergelyk met die standaard wat in
hierdie studie voorgestel word. In die studie word tot die slotsom gekom dat die plaaslike
soekenjins gunstig vergelyk met die hoof soekenjins, maar nie genoegsaam sodat hulle
eksklusief gebruik kan word nie. Verdere navorsing oor hierdie aspek is nodig. Intelligente
Agente sal waarskynlik meer gewild word, maar die enigste sekerheid vir die toekoms van
Internet soekinstrumente is verandering, verandering en nogmaals verandering.
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Neethling, Riaan. "Search engine optimisation or paid placement systems-user preference /." Thesis, [S.l. : s.n.], 2007. http://dk.cput.ac.za/cgi/viewcontent.cgi?article=1076&context=td_cput.
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Kritzinger, Wouter Thomas. "Development of a search engine marketing model using the application of a dual strategy." Thesis, Cape Peninsula University of Technology, 2017. http://hdl.handle.net/20.500.11838/2603.
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Thesis (DTech (Informatics))--Cape Peninsula University of Technology, 2017.<br>Any e-commerce venture using a website as main shop-front should invest in marketing
their website. Previous empirical evidence shows that most Search Engine Marketing
(SEM) spending (approximately 82%) is allocated to Pay Per Click (PPC) campaigns
while only 12% was spent on Search Engine Optimisation (SEO). The remaining 6% of
the total spending was allocated to other SEM strategies.
No empirical work was found on how marketing expenses compare when used solely for
either the one or the other of the two main types of SEM. In this study, a model will be
designed to guide the development of a dual SEM strategy.
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Олефіренко, Олег Михайлович, Олег Михайлович Олефиренко, Oleh Mykhailovych Olefirenko, Юлія Андріївна Сірік, Юлия Андреевна Сирик та Yuliia Andriivna Sirik. "Використання пошукової оптимізації сайту як засіб маркетингових комунікацій". Thesis, Видавництво СумДУ, 2011. http://essuir.sumdu.edu.ua/handle/123456789/12295.
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Costa, Miguel. "SIDRA: a Flexible Web Search System." Master's thesis, Department of Informatics, University of Lisbon, 2004. http://hdl.handle.net/10451/13914.
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Sidra is a new indexing, searching and ranking system for Web contents. It has a flexible, parallel, distributed and scalable architecture. Sidra maintains several data structures that provide multiple access methods to different data dimensions, giving it the capability to select results reflecting search contexts. Its design addresses current challenges of Web search engines: high performance, short searching and indexing times, good quality of results, scalability and high service availability
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Hicks, Janette M. "Search algorithms for discovery of Web services." Diss., Online access via UMI:, 2005. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:1425747.
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Garcia, Steven, and steven garcia@student rmit edu au. "Search Engine Optimisation Using Past Queries." RMIT University. Computer Science and Information Technology, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080501.093229.
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World Wide Web search engines process millions of queries per day from users all over the world. Efficient query evaluation is achieved through the use of an inverted index, where, for each word in the collection the index maintains a list of the documents in which the word occurs. Query processing may also require access to document specific statistics, such as document length; access to word statistics, such as the number of unique documents in which a word occurs; and collection specific statistics, such as the number of documents in the collection. The index maintains individual data structures for each these sources of information, and repeatedly accesses each to process a query. A by-product of a web search engine is a list of all queries entered into the engine: a query log. Analyses of query logs have shown repetition of query terms in the requests made to the search system. In this work we explore techniques that take advantage of the repetition of user queries to improve the accuracy or efficiency of text search. We introduce an index organisation scheme that favours those documents that are most frequently requested by users and show that, in combination with early termination heuristics, query processing time can be dramatically reduced without reducing the accuracy of the search results. We examine the stability of such an ordering and show that an index based on as little as 100,000 training queries can support at least 20 million requests. We show the correlation between frequently accessed documents and relevance, and attempt to exploit the demonstrated relationship to improve search effectiveness. Finally, we deconstruct the search process to show that query time redundancy can be exploited at various levels of the search process. We develop a model that illustrates the improvements that can be achieved in query processing time by caching different components of a search system. This model is then validated by simulation using a document collection and query log. Results on our test data show that a well-designed cache can reduce disk activity by more than 30%, with a cache that is one tenth the size of the collection.
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Adya, Kaushik. "An implicit-feedback based ranking methodology for Web search engines /." Available to subscribers only, 2005. http://proquest.umi.com/pqdweb?did=1079672381&sid=1&Fmt=2&clientId=1509&RQT=309&VName=PQD.
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Asghariaghamashhadi, Mohammad Bagher <1988>. "Using of Trademarks in Keyword Advertising in Web Search Engines." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amsdottorato.unibo.it/8298/1/FINAL%20VERSION%20OF%20THESIS.pdf.
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Use of Trademarks in keyword advertising has been one of the most debatable issues in trademark law for several years. This entirely new way of using Trademarks over the Internet has provoked a discussion concerning the core concepts of Trademark law. Harmonized EU trademark law proved to be ambiguous on whether it amounted to trademark infringement or not. This ambivalence was also exhibited by the case law of EU Member States. European keyword advertisers simply could not tell which use of a competitor‘s trademark was lawful. In recent years CJEU has continuously expanded the scope and reach of trademark protection in the EU .It is notable that Inconsistencies in the court’s system of infringement criteria clearly come to the fore and this approach has been criticized by analysts who believe that the Court should have adopt a more traditional approach to the analysis of trademark infringement, which was suggested by its Advocate General, in order to arrive at the same conclusion. The premise on which the Court rested its ruling, it is believed, missed salient parts of the evidence, circumvented its preexisting jurisprudence, and most of all, threatened to open the floodgates of abusive trademark use in the future. With reference to above , this thesis will address issue of keyword advertising under EU legislations and will evaluate ECJ case law together with national members case law.
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Deng, Lin. "Mining user preference using SPY voting for search engine personalization /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?COMP%202006%20DENG.
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李莉華 and Lei-wah Lee. "On improving the relevancy ranking algorithm in web search engine." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31222973.
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Lee, Lei-wah. "On improving the relevancy ranking algorithm in web search engine /." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B21607448.
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Young, Joel D. "Exploiting the structure of the web for spidering /." View online version; access limited to Brown University users, 2005. http://wwwlib.umi.com/dissertations/fullcit/3174706.
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Tan, Qingzhao. "The development of an effective co-training framework for adapting metasearch engine rankers /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?COMP%202004%20TAN.
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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.<br>Includes bibliographical references (leaves 65-70). Also available in electronic version. Access restricted to campus users.
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Presgrave, Trevor A. "Collaborative Search Engines: Toward a Meta-Design for Improving the User Experience." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1427799661.
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Gossen, Tatiana [Verfasser], and Andreas [Akademischer Betreuer] Nürnberger. "Targeted search engines for children : search user interfaces and information-seeking behaviour / Tatiana Gossen. Betreuer: Andreas Nürnberger." Magdeburg : Universitätsbibliothek, 2015. http://d-nb.info/1073584372/34.
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