Visuomenė, internetas ir jame esanti informacija dabar turi vieną didžiausių įtakų mūsų asmeniniame gyvenime. Be interneto daugelis dabar, net nebeįsivaizduotų savo kasdienybės, nes jis mums ir padeda atlikti daugelį darbų. Tačiau vis augant duomenų kiekiams internete, informacijos paieška tampa vienu svarbiausių internete naudojamų funkcijų.Paieškos sistemos – tai sistemos, ieškančios dokumentų žiniatinklyje, naujienų grupių archyvuose, FTP saugyklose, kuriuos rado paieškos serveris ir įtraukė į savo duomenų bazes. Ieškant informacijos, vartotojai naudojasi šiomis sistemomis, todėl internetinių svetainių kūrėjai yra suinteresuoti, kad jų internetiniai puslapiai būti kuo dažniau randami paieškos sistemų. Todėl besiplečiant paieškos sistemoms išsiplėtojo SEO (Search Engine Optimization, lietuviškai „Optimizavimas paieškos sistemoms“). / Expansion of the Internet and the information contained therein formed such an organization which aims to help the internet users to quickly and efficiently find what he needs. One of them is the Google search engine. It is one of the largest search engine companies in the world. In this paper we will analyse the Google search engine, and its proposed content optimization methods, which it claims to helps achieve better search results positions. The paper will discuss techniques such as HTML meta tags, content headers, site maps, robots.txt file, canonical and Paging functions. These methods will be compared with each other, and we will decide which method is most useful for site optimization.

Using the proper search engine is crucial for efficient and effective web search. The objective of this thesis is to develop methodologies to evaluate search engines in a systematic and reliable manner. A new model for evaluation and comparison of search engines is proposed. This hierarchical model classifies the most common features found in search engines and search results into groups and subgroups. To illustrate the usefulness of the proposed model, several Chinese search engines are evaluated and compared as a case study. It is also very important to evaluate the performance of a search engine over time. Three performance measurement metrics are formulated for this purpose. Performance results for English and Chinese search engines are represented by histograms for visual inspection. The histograms are classified into groups to facilitate the interpretation of the performance metrics and examination of the associated behaviours of the search engines. An automated classification method is developed that eliminates the subjectivity and ambiguity found in visual classification of the histograms.

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. / Search Engines, Interaction Design

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.

Thesis (MTech (Business Information Systems))--Cape Peninsula University of Technology, 2005. / Includes bibliographical references (leaves: 59-66). Also available online.

Thesis (Ph.D.) -- University of Western Sydney, 2004. / "A thesis submitted for the degree of Doctor of Philosophy at the University of Western Sydney" Bibliography : leaves 223-242.

Thesis (M.S.)--University of Texas at El Paso, 2008. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Thesis (M.S.)--State University of New York at Binghamton, Department of Computer Science, Thomas J. Watson School of Engineering and Applied Science, 2007. / Includes bibliographical references.

Thesis (M.S.)--Kent State University, 2007. / Title from PDF t.p. (viewed July 2, 2007). Advisor: David B. Robins. Keywords: information retrieval, search engine, social bookmarking, tagging, folksonomy, Internet, World Wide Web. Includes survey instrument. Includes bibliographical references (p. 137-141).

Thesis (M.Sc.)--Acadia University, 2000. / Includes bibliographical references (leaves 86-89). Also available on the Internet via the World Wide Web.