Especially with the wide use of dynamic page generation, and richer user interaction in Web, traditional web usage mining methods, which are based on the pageview concept are of limited usability. For overcoming the difficulty of capturing usage behaviour, we define the concept of semantic events. Conceptually, events are higher level actions of a user in a web site, that are technically independent of pageviews. Events are modelled as objects in the domain of the web site, with associated properties. A sample event from a video web site is the &#039 / play video event&#039 / with properties &#039 / video&#039 / , &#039 / length of video&#039 / , &#039 / name of video&#039 / , etc. When the event objects belong to the domain model of the web site&#039 / s ontology, they are referred as semantic events. In this work, we propose a new algorithm and associated framework for mining patterns of semantic events from the usage logs. We present a method for tracking and logging domain-level events of a web site, adding semantic information to events, an ordering of events in respect to the genericity of the event, and an algorithm for computing sequences of frequent events.

With the emergence of the Internet and of the World Wide Web, the Web site has become a key communication channel in organizations. To satisfy the objectives of the Web site and of its target audience, adapting the Web site content to the users' expectations has become a major concern. In this context, Web usage mining, a relatively new research area, and Web analytics, a part of Web usage mining that has most emerged in the corporate world, offer many Web communication analysis techniques. These techniques include prediction of the user's behaviour within the site, comparison between expected and actual Web site usage, adjustment of the Web site with respect to the users' interests, and mining and analyzing Web usage data to discover interesting metrics and usage patterns. However, Web usage mining and Web analytics suffer from significant drawbacks when it comes to support the decision-making process at the higher levels in the organization.<p><p>Indeed, according to organizations theory, the higher levels in the organizations need summarized and conceptual information to take fast, high-level, and effective decisions. For Web sites, these levels include the organization managers and the Web site chief editors. At these levels, the results produced by Web analytics tools are mostly useless. Indeed, most of these results target Web designers and Web developers. Summary reports like the number of visitors and the number of page views can be of some interest to the organization manager but these results are poor. Finally, page-group and directory hits give the Web site chief editor conceptual results, but these are limited by several problems like page synonymy (several pages contain the same topic), page polysemy (a page contains several topics), page temporality, and page volatility.<p><p>Web usage mining research projects on their part have mostly left aside Web analytics and its limitations and have focused on other research paths. Examples of these paths are usage pattern analysis, personalization, system improvement, site structure modification, marketing business intelligence, and usage characterization. A potential contribution to Web analytics can be found in research about reverse clustering analysis, a technique based on self-organizing feature maps. This technique integrates Web usage mining and Web content mining in order to rank the Web site pages according to an original popularity score. However, the algorithm is not scalable and does not answer the page-polysemy, page-synonymy, page-temporality, and page-volatility problems. As a consequence, these approaches fail at delivering summarized and conceptual results. <p><p>An interesting attempt to obtain such results has been the Information Scent algorithm, which produces a list of term vectors representing the visitors' needs. These vectors provide a semantic representation of the visitors' needs and can be easily interpreted. Unfortunately, the results suffer from term polysemy and term synonymy, are visit-centric rather than site-centric, and are not scalable to produce. Finally, according to a recent survey, no Web usage mining research project has proposed a satisfying solution to provide site-wide summarized and conceptual audience metrics. <p><p>In this dissertation, we present our solution to answer the need for summarized and conceptual audience metrics in Web analytics. We first described several methods for mining the Web pages output by Web servers. These methods include content journaling, script parsing, server monitoring, network monitoring, and client-side mining. These techniques can be used alone or in combination to mine the Web pages output by any Web site. Then, the occurrences of taxonomy terms in these pages can be aggregated to provide concept-based audience metrics. To evaluate the results, we implement a prototype and run a number of test cases with real Web sites. <p><p>According to the first experiments with our prototype and SQL Server OLAP Analysis Service, concept-based metrics prove extremely summarized and much more intuitive than page-based metrics. As a consequence, concept-based metrics can be exploited at higher levels in the organization. For example, organization managers can redefine the organization strategy according to the visitors' interests. Concept-based metrics also give an intuitive view of the messages delivered through the Web site and allow to adapt the Web site communication to the organization objectives. The Web site chief editor on his part can interpret the metrics to redefine the publishing orders and redefine the sub-editors' writing tasks. As decisions at higher levels in the organization should be more effective, concept-based metrics should significantly contribute to Web usage mining and Web analytics. <p> / Doctorat en sciences appliquées / info:eu-repo/semantics/nonPublished

Online advertising has built a market of hundreds of billions of dollars and still continues to grow. With well developed techniques in big data storage, data mining and analytics, online advertising is able to reach targeted audiences e ctively. Real- time bidding refers to the buying and selling of online ad impressions through ad inventory auctions which occur in the time it takes a webpage to load. How to de- termine the bidding price and how to allocate the budget of advertising is the key to successful ad campaigns. Both of these aspects are fundamental to most campaign optimizations and we will introduce both of them in this thesis. For bidding price determination, we improved the estimation of CTR (Click Through Rate) (one of the most important factors of determining the bidding price) by using a re ned hierar- chical tree structure for the estimation. The result of the experiment and the A/B test showed our proposal can provide stable improvement. For budget allocation, we introduce SCO (Single Campaign Optimization) and CCO (Cross Campaign Opti- mization). SCO has been applied by our commercial partner while CCO needs more research. We will rst introduce the methods of SCO and then give our proposal about CCO. We modeled CCO as a LP (Linear Programming) problem as well as designed an e ective procedure to implement optimal impressions distribution. Our simulation showed our proposal can signi cantly increase global Gross Pro t (GP). / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

Práce se zabývá problematikou měření ukazatelů souvisejících s provozem webových stránek a aplikací a technologickými prostředky k tomu sloužícími ? Web Analytics (WA). Hlavním cílem práce je otestovat a porovnat vybrané zástupce těchto nástrojů a podrobit je srovnání podle objektivních kriterií, dále také kritické zhodnocení možností WA nástrojů obecně. V první části se práce zaměřuje na popis různých způsobů měření provozu na WWW a definuje související metriky. Poskytuje také přehled dostupných WA nástrojů. Následně je vytvořen hodnotící model pro WA nástroje a podle něj je ohodnoceno šest zástupců těchto nástrojů. Hodnocení má podobu uživatelského testování na datech ze dvou reálných webových stránek. Majitelům těchto dvou webových stránek je učiněno doporučení pro volbu vhodného WA nástroje na základě jejich preferencí. Dalším výstupem práce jsou reporty, vygenerované testovanými nástroji, popisující aktivity na zkoumaných webových stránkách.

Obecný popis Web Miningu. Charakteristika a užití technik Web Usage Miningu. Podrobný popis metod a nástrojů zahrnovaných pod pojem ?Web Usage Mining?. Softwarové nástroje a existující řešení pro Web Usage Mining. Praktický návrh konkrétního řešení s využitím popsaných metod Web Usage Miningu ? analýza logovacích souborů webového serveru Fakulty managementu Vysoké školy ekonomické v Praze.

Um dos motivos para o crescente desenvolvimento da área de mineração de dados encontra-se no aumento da quantidade de documentos gerados e armazenados em formato digital, estruturados ou não. A Web contribui sobremaneira para este contexto e, de forma coerente com esta situação, observa-se o surgimento de técnicas específicas para utilização nesta área, como a mineração de estrutura, de conteúdo e de uso. Pode-se afirmar que esta crescente oferta de informação na Web cria o problema da sobrecarga cognitiva. A Hipermídia Adaptativa permite minorar este problema, com a adaptação de hiperdocumentos e hipermídia aos seus usuários segundo suas necessidades, preferências e objetivos. De forma resumida, esta adaptação é realizada relacionando-se informações sobre o domínio da aplicação com informações sobre o perfil de usuários. Um dos tópicos importantes de pesquisa em sistemas de Hipermídia Adaptativa encontra-se na geração e manutenção do perfil dos usuários. Dentre as abordagens conhecidas, existe um contínuo de opções, variando desde cadastros de informações preenchidos manualmente, entrevistas, até a aquisição automática de informações com acompanhamento do uso da Web. Outro ponto fundamental de pesquisa nesta área está ligado à construção das aplicações, sendo que recursos da Web Semântica, como ontologias de domínio ou anotações semânticas de conteúdo podem ser observados no desenvolvimento de sistemas de Hipermídia Adaptativa. Os principais motivos para tal podem ser associados com a inerente flexibilidade, capacidade de compartilhamento e possibilidades de extensão destes recursos. Este trabalho descreve uma arquitetura para a aquisição automática de perfis de classes de usuários, a partir da mineração do uso da Web e da aplicação de ontologias de domínio. O objetivo principal é a integração de informações semânticas, obtidas em uma ontologia de domínio descrevendo o site Web em questão, com as informações de acompanhamento do uso obtidas pela manipulação dos dados de sessões de usuários. Desta forma é possível identificar mais precisamente os interesses e necessidades de um usuário típico. Integra o trabalho a implementação de aplicação de Hipermídia Adaptativa a partir de conceitos de modelagem semântica de aplicações, com a utilização de recursos de serviços Web, para validação experimental da proposta. / One of the reasons for the increasing development observed in Data Mining area is the raising in the quantity of documents generated and stored in digital format, structured or not. The Web plays central role in this context and some specific techniques can be observed, as structure, content and usage mining. This increasing information offer in the Web brings the cognitive overload problem. The Adaptive Hypermedia permits a reduction of this problem, when the contents of selected documents are presented in accordance with the user needs, preferences and objectives. Briefly put, this adaptation is carried out on the basis of relationship between information concerning the application domain and information concerning the user profile. One of the important points in Adaptive Hypermedia systems research is to be found in the generation and maintenance of the user profiles. Some approaches seek to create the user profile from data obtained from registration, others incorporate the results of interviews, and some have the objective of automatic acquisition of information by following the usage. Another fundamental research point is related with the applications construction, where can be observed the use of Web semantic resources, such as semantic annotation and domain ontologies. This work describes the architecture for automatic user profile acquisition, using domain ontologies and Web usage mining. The main objective is the integration of usage data, obtained from user sessions, with semantic description, obtained from a domain ontology. This way it is possible to identify more precisely the interests and needs of a typical user. The implementation of an Adaptive Hypermedia application based on the concepts of semantic application modeling and the use of Web services resources that were integrated into the proposal permitted greater flexibility and experimentation possibilities.

Web usage mining has become popular in various business areas related with Web site development. In Web usage mining, the commonly visited navigational paths are extracted in terms of Web page addresses from the Web server visit logs, and the patterns are used in various applications. The semantic information of the Web page contents is generally not included in Web usage mining. In this thesis, a framework for integrating semantic information with Web usage mining is implemented. The frequent navigational patterns are extracted in the forms of ontology instances instead of Web page addresses and the result is used for making page recommendations to the visitor. Moreover, an evaluation mechanism is implemented to find the success of the recommendation. Test results proved that stronger and more accurate recommendations are obtained by including semantic information in the Web usage mining instead of using on visited Web page addresses.

Many e-commerce web sites such as online book retailers or specialized information hubs such as online movie databases make use of recommendation systems where users are directed to items of interests based on past user interactions. Keyword-based approaches, collaborative and content filtering techniques have been tried and used over the years each having their own shortcomings. While keyword based approaches are naive and do not take content or context into account collaborative and content filtering techniques suffer from biased ratings, first item and first-rater problems. Recent approaches try to incorporate underlying semantic properties of data by employing ontology based usage mining. This thesis aims to design a recommendation system based on ontological data where web pages are seen as objects with attributes and relations. Instead of relying on users&rsquo / content ratings, user sessions are clustered on a iv semantic level to capture different behavioral groups. Since semantic information is used for the clustering distance function, each cluster represents a behavior group instead of simpler data groups. New users are then assigned to individual clusters that best represent their behavior and recommendations are generated accordingly. In this thesis we use the recommendation results as a means for measuring the effectiveness of the clusters we have generated. We have compared the results obtained using the ontological data and the results obtained without using it and shown that semantic integrating semantic knowledge increases both precision and recall.

Emanating from classical Weibull mixture models we propose a framework for clustering survival data with various proportionality restrictions imposed. By introducing mixtures of Weibull proportional hazards models on a multivariate data set a parametric cluster approach based on the EM-algorithm is carried out. The problem of non-response in the data is considered. The application example is a real life data set stemming from the analysis of a world-wide operating eCommerce application. Sessions are clustered due to the dwell times a user spends on certain page-areas. The solution allows for the interpretation of the navigation behavior in terms of survival and hazard functions. A software implementation by means of an R package is provided. (author´s abstract) / Series: Research Report Series / Department of Statistics and Mathematics

In this thesis, a new reactive session reconstruction method &#039 / Smart-SRA&#039 / is introduced. Web usage mining is a type of web mining, which exploits data mining techniques to discover valuable information from navigations of Web users. As in classical data mining, data processing and pattern discovery are the main issues in web usage mining. The first phase of the web usage mining is the data processing phase including session reconstruction. Session reconstruction is the most important task of web usage mining since it directly affects the quality of the extracted frequent patterns at the final step, significantly. Session reconstruction methods can be classified into two categories, namely &#039 / reactive&#039 / and &#039 / proactive&#039 / with respect to the data source and the data processing time. If the user requests are processed after the server handles them, this technique is called as &lsquo / reactive&rsquo / , while in &lsquo / proactive&rsquo / strategies this processing occurs during the interactive browsing of the web site. Smart-SRA is a reactive session reconstruction techique, which uses web log data and the site topology. In order to compare Smart-SRA with previous reactive methods, a web agent simulator has been developed. Our agent simulator models behavior of web users and generates web user navigations as well as the log data kept by the web server. In this way, the actual user sessions will be known and the successes of different techniques can be compared. In this thesis, it is shown that the sessions generated by Smart-SRA are more accurate than the sessions constructed by previous heuristics.