An average linear time algorithm for web data mining

In this paper, we study the complexity of a data mining algorithm for extracting patterns from user web navigation data that was proposed in previous work.3 The user web navigation sessions are inferred from log data and modeled as a Markov chain. The chain's higher probability trails correspond to the preferred trails on the web site. The algorithm implements a depth-first search that scans the Markov chain for the high probability trails. We show that the average behaviour of the algorithm is linear time in the number of web pages accessed

A review of data mining techniques for research in online shopping behaviour through frequent navigation paths

Knowing how consumers navigate online shopping web sites enables retailers to not only better design their sites for navigation but also place buying recommendations at strategic points and personalise the flow of content. Frequent navigation paths can be derived from browsing histories or clickstreams with sequence-oriented data mining techniques. In this working paper, we highlight, with examples, the relevance of frequent navigation paths to online shopping behaviour research and review some relevant data mining techniques

Towards semantic web mining

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. The idea is to improve, on the one hand, the results of Web Mining by exploiting the new semantic structures in the Web; and to make use of Web Mining, on the other hand, for building up the Semantic Web. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable

Detecting Structure In Chaos: A Customer Process Analysis Method

Detecting typical patterns in customer processes is the precondition for gaining an understanding about customer issues and needs in the course of performing their processes. Such insights can be translated into customer-centric service offerings that provide added value by enabling customers to reach their process objectives more effectively and rapidly, and with less effort. However, customer processes performed in less restrictive environments are extremely heterogeneous, which makes them difficult to analyse. Current approaches deal with this issue by considering customer processes in large scope and low detail, or vice versa. However, both views are required to understand customer processes comprehensively. Therefore, we present a novel customer process analysis method capable of detecting the hidden activity-cluster structure of customer processes. Consequently, both the detailed level of process activities and the aggregated cluster level are available for customer process analysis, which increases the chances of detecting patterns in these heterogeneous processes. We apply the method to two datasets and evaluate the results’ validity and utility. Moreover, we demonstrate that the method outperforms alternative solution technologies. Finally, we provide new insights into customer process theory

O USO DE INTELIGÊNCIA ARTIFICIAL NO ENSINO DE CONTABILIDADE: UM MODELO CLASSIFICADOR DO PENSAMENTO CRÍTICO

O desenvolvimento de habilidades cognitivas ligadas à capacidade de pensamento crítico constitui um importante objetivo do processo educacional, há décadas a educação contábil é criticada pela deficiência de seus egressos na aquisição e no uso dessas habilidades. O cenário atual, de avanços tecnológicos, cria um ambiente de constantes mudanças a profissão contábil, sendo necessário que haja mudanças na forma e no conteúdo dos cursos. O corpo doutores e pesquisadores em Contabilidade, não é suficiente para protagonizar essa mudança, e o uso de tecnologia pode auxiliar. O presente estudo desenvolveu um método de classificação do nível de raciocínio crítico em estudantes da disciplina de História da Contabilidade, com base nos logs de acesso do sistema de apoio ao ensino; do processamento da linguagem natural dos textos produzidos para a disciplina; e no índice Flesch–Kincaid de legibilidade dos materiais produzidos. Análises demonstram que o modelo classifica os estudantes com acurácia de 86,20% em relação ao processo realizado por um professor. Entretanto os resultados precisam ser analisados com cuidado, dado que o modelo deve ser testado e melhorado em outras disciplinas e, em outro conjunto de dados, para que possa ser fonte confiável de classificação do nível de raciocínio crítico dos estudantes. Como sugestão de pesquisas futuras pode-se comparar os resultados do modelo de classificação, baseado em inteligência artificial, dessa pesquisa com os resultados de testes consagrados pela literatura, como por exemplo o California Critical Thinking Skills Test (CCTST); o Ennis-Weir Critical Thinking Essay Test (EWCTET)

An Integrative Framework for Knowledge Extraction in Collaborative Virtual Environments

Collaborative virtual environments are becoming an intrinsic part of professional practices. In addition to providing collaboration support, they have the potential to collect vast amounts of data about collaborative activities. The aim of this research is to utilize this data effectively, extract meaningful insights out of it and feeding discovered knowledge back into the environment. The paper presents a framework for integrating knowledge discovery techniques with collaborative virtual environments, starting from early conceptual development. Discovered patterns are deposited in an organizational memory which makes these available within the virtual environment. Two examples of the application of the framework are included

A data warehouse to support web site automation

Background: \ud Due to the constant demand for new information and timely updates of services and content in order to satisfy the user’s needs, web site automation has emerged as a solution to automate several personalization and management activities of a web site. One goal of automation is the reduction of the editor’s effort and consequently of the costs for the owner. The other goal is that the site can more timely adapt to the behavior of the user, improving the browsing experience and helping the user in achieving his/her own goals. \ud \ud Methods: \ud A database to store rich web data is an essential component for web site automation. In this paper, we propose a data warehouse that is developed to be a repository of information to support different web site automation and monitoring activities. We implemented our data warehouse and used it as a repository of information in three different case studies related to the areas of e-commerce, e-learning, and e-news. \ud \ud Result: \ud The case studies showed that our data warehouse is appropriate for web site automation in different contexts. \ud \ud Conclusion: \ud In all cases, the use of the data warehouse was quite simple and with a good response time, mainly because of the simplicity of its structure.FCT - Science and Technology Foundation (SFRH/BD/22516/2005)project Site-O-Matic (POSC/EIA/58367/2004)São Paulo Research Foundation (FAPESP) (grants 2011/19850-9, 2012/13830-9

Web mining for the integration of data mining with business intelligence in web-based decision support systems

Web mining can be defined as the use of data mining techniques to automatically discover and extract information from web documents and services. A decision support system is a computer-based information sy Analysis stem that supports business or organizational decision-making activities. Data mining and business intelligence techniques can be integrated in order to develop more advanced decision support systems. In this chapter, the authors propose to use web mining as a process to develop advanced decision support systems in order to support the management activities of a website. They describe the Web mining process as a sequence of steps for the development of advanced decision support systems. By following such a sequence, the authors can develop advanced decision support systems, which integrate data mining with business intelligence, for websites.Sao Paulo Research Foundation (FAPESP) (grants 2011/19850-9 and 2012/13830-9