The suggester functionality is an important feature of modern search engines. The aim of the thesis is to implement it for the OpenGrok project. The OpenGrok search engine is based on Apache Lucene and supports its query syntax. Presented suggester implementation supports this query syntax and provides suggestions not only for prefixes but also for wildcards, regular expressions, or phrases. The implementation also takes into account the possibility of grouping queries. That means, if one query is already specified and user is typing another query, then the first query will restrict the suggestions for the second query. The promotion of specific suggestions is based on the underlying Lucene index data structure and previous searches of the users.

Over theWeb, distributed and heterogeneous sources with structured and related content form rich repositories of information commonly referred to as dataspaces. To provide access to this heterogeneous data, information integration systems have traditionally relied on the availability of a mediated schema, along with mappings between this schema and the schema of the source schemas. On dataspaces, where sources are plentiful, autonomous and extremely volatile, a system based on the existence of a pre-defined mediated schema and mapping information presents several drawbacks. Notably, the cost of keeping the mappings up to date as new sources are found or existing sources change can be prohibitively high. We propose a novel querying architecture that requires neither a mediated schema nor source mappings, which is based mainly on indexing mechanisms and on-the-fly rewriting algorithms. Our indexes are designed for data that is represented as relations, and are able to capture the structure of the sources, their instances and the connections between them. In the absence of a mediated schema, the user formulates structured queries based on what she expects to find. These queries are rewritten using a best-effort approach: the proposed rewriting algorithms compare a user query against the source schemas and produces a set of rewritings based on the matches found. Based on this architecture, two different querying approaches are tested. Experiments show that the indexing and rewriting algorithms are scalable, i.e., able to handle a very large number of structured Web sources; and that support simple, yet expressive queries that exploit the inherent structure of the data.

Background: The Internet has developed the world of advertising by giving advertisers the possibility to track specific patterns among their consumers, which shows how consumers are clicking on online advertisements and what translates into sales for the brand. Lately, companies have actively starting to make use of search engines marketing (SEM). The paid advertising on search engines is one option to make a brand’s website visible to its consumers. The attitudes towards advertisements have previously been examined in traditional media and in other online settings, but the research in the context of search engines is limited. Therefore, it calls for deeper insights and knowledge in how consumers hold attitudes towards a brand and its paid advertising on search engines such as Google. Purpose: The purpose is to describe how users’ attitudes towards brands are influenced by the fact that brands have paid for advertising on search engine result pages. This is done through the ABC-model of attitudes. The question asked in this study was: How does paid advertising displayed on search engines affect the attitudes held towards a brand? Methodology: This thesis project used a qualitative approach and was of descriptive nature. The data was gathered through seven unstructured in-depths interviews based on a quota sample considering three criteria: age group, in this case, 18-29-year-olds, and the variable of regular e-commerce buyers, as well as the participants being users of the search engine Google. The researchers verified data saturation at seven interviews. Conclusion: The main finding in this study is that the level of familiarity influences the participants attitudes towards the brand. Previous experience and knowledge with a brand was an affecting factor of how they interpreted the brand’s advertising on Google’s search engine result pages. Knowledge and a positive experience with a brand generated a more positive attitude towards the brand when an unknown brand generated a neutral or more negative attitude towards the brand. Related factors that also influenced the study were the clicking pattern, the landing page, the choices of wording and the intended target groups by the brands. The study also presents a range of recommendations for future research, as well as theoretical and managerial implications.

Over theWeb, distributed and heterogeneous sources with structured and related content form rich repositories of information commonly referred to as dataspaces. To provide access to this heterogeneous data, information integration systems have traditionally relied on the availability of a mediated schema, along with mappings between this schema and the schema of the source schemas. On dataspaces, where sources are plentiful, autonomous and extremely volatile, a system based on the existence of a pre-defined mediated schema and mapping information presents several drawbacks. Notably, the cost of keeping the mappings up to date as new sources are found or existing sources change can be prohibitively high. We propose a novel querying architecture that requires neither a mediated schema nor source mappings, which is based mainly on indexing mechanisms and on-the-fly rewriting algorithms. Our indexes are designed for data that is represented as relations, and are able to capture the structure of the sources, their instances and the connections between them. In the absence of a mediated schema, the user formulates structured queries based on what she expects to find. These queries are rewritten using a best-effort approach: the proposed rewriting algorithms compare a user query against the source schemas and produces a set of rewritings based on the matches found. Based on this architecture, two different querying approaches are tested. Experiments show that the indexing and rewriting algorithms are scalable, i.e., able to handle a very large number of structured Web sources; and that support simple, yet expressive queries that exploit the inherent structure of the data.

Made available in DSpace on 2015-04-20T12:33:27Z (GMT). No. of bitstreams: 1 jucimar.pdf: 2914952 bytes, checksum: 07583d96a43d2aaee11457881962b461 (MD5) Previous issue date: 2013-03-08 / Search engines are among the most used tools to find information on the Web. Recent studies show that the user s confidence on these systems is high. However, the database of search engines usually contains noisy information that may decrease the search results quality and thus negatively affect the confidence of users in these systems. Such type of information is called noise and it can be intentional or not. The main goals of this thesis are to develop and enhance techniques to detect noise in databases of search engines. We evaluated noisy sources of information that may be present either in the anchor text and/or links structures of search engine indexes. As a result, we present methods to turn search engines less susceptible of negative impact caused by the presence of noisy. / Máquinas ou motores de busca são as ferramentas mais usadas para localizar informações na Internet. Para muitas empresas, a porta de entrada para seu negócio está nas respostas dadas por esses mecanismos a determinadas consultas. Por este motivo, é importante que suas páginas estejam bem posicionadas nestas respostas. Pesquisas recentes mostram que a confiança dos usuários neste mecanismo é relativamente alta. Entretanto, existe nos repositórios de máquinas de busca uma série de informações que podem levar à degradação da qualidade das respostas providas por estes sistemas. Tais informações são conhecidas como ruído, podendo ser geradas de maneira intencional ou não. Esta tese tem como principal objetivo desenvolver e aprimorar técnicas de detecção e tratamento de informação ruidosa em máquinas de busca. Foram avaliadas fontes ruidosas que podem estar nos textos de âncora e/ou nas estruturas dos apontadores presentes em repositórios de máquina de busca. Como resultados, são apresentados métodos para tornar máquinas de busca menos suscetíveis aos efeitos negativos da presença do ruído em seus repositórios.

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

Idag finns omkring 625 miljoner webbsidor på internet. Rapporten betonar vikten av att synas bland dessa samt hur man gör för att bli sedd, detta samlingsnamn kallas sökmotoroptimering. I den här studien behandlar vi ämnet sökmotoroptimering med inriktning mot Google, vad det är och hur det används i praktiken. Vårt syfte är att fördjupa oss i detta ämne och bilda oss en uppfattning om vad det handlar om. I resultatet har vi utifrån Googles riktlinjer listat flera begrepp som hjälper dig att optimera din webbsida. Vi har även i resultatet intervjuat flera verksamheter eftersom vi vill veta hur sökmotoroptimering används hos dem. I analysen har vi sedan jämfört Googles riktlinjer med teori och resultat för att ta reda på vad som utmärker sig mest. Vi har kommit fram till att en lättillgänglig sida med bra innehåll är det som utmärker sökmotoroptimering och det som gör att webbsidan blir synligare i träfflistan. Vi kom även fram till att det är tre specifika delar som Google tar upp som utmärker optimering hos verksamheter. Dessa delar är: ”optimera för besökaren”,” förutse sökbeteende” och ”webbplatsanseende”.

Advertisers setting up search engine advertising campaigns for the first time need to place bids on keywords, but typically lack experience and data to determine ranks that maximize a keyword's profit (generally referred to as a cold-start problem). This article aims at solving the problem of bidding on keywords in newly set-up search engine advertising campaigns. We suggest that advertisers collect data from the Google Keyword Planner to obtain precise estimates of the percentage increases in prices per click and clickthrough rates, which are needed to calculate optimal bids (exact approach). Together with the profit contribution per conversion and the conversion rate, the advertiser might then set bids that maximize profit. In case advertisers cannot afford to collect the required data, we suggest two proxy approaches and evaluate their performance using the exact approach as a benchmark. The empirical study shows that both proxy approaches perform reasonably well-the easier approach to implement (proxy 2) sometimes performs even better than the more sophisticated one (proxy 1). As a consequence, advertisers might just use this very simple proxy when bidding on keywords in newly set-up SEA campaigns. This research extends the stream of literature on how to determine optimal bids, which so far focuses on campaigns that are already running and where the required data to calculate bids is already available. This research offers a novel approach of determining bids when advertisers lack the aforementioned information.

A topic of this master thesis is designing and implementing a Content Management System that would contain SEO optimization options in the basis. The objective of this thesis is to improve quality of produced websites and to increase awareness about SEO optimization. According to the defined problem the thesis first analyzes existing CMS systems. Afterwards the solution is suggested which consists of a combination of the existing CMS system OctoberCMS and a plugin that extends the system with the necessary functionality. The last part of the thesis is devoted to designing the implementation and testing the final solution.

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.