65,266 research outputs found
The contribution of data mining to information science
The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research
Intelligent and adaptive tutoring for active learning and training environments
Active learning facilitated through interactive and adaptive learning environments differs substantially from traditional instructor-oriented, classroom-based teaching. We present a Web-based e-learning environment that integrates knowledge learning and skills training. How these tools are used most effectively is still an open question. We propose knowledge-level interaction and adaptive feedback and guidance as central features. We discuss these features and evaluate the effectiveness of this Web-based environment, focusing on different aspects of learning behaviour and tool usage. Motivation, acceptance of the approach, learning organisation and actual tool usage are aspects of behaviour that require different evaluation techniques to be used
Information Technology Applications in Hospitality and Tourism: A Review of Publications from 2005 to 2007
The tourism and hospitality industries have widely adopted information
technology (IT) to reduce costs, enhance operational efficiency, and most importantly to
improve service quality and customer experience. This article offers a comprehensive review of
articles that were published in 57 tourism and hospitality research journals from 2005 to 2007.
Grouping the findings into the categories of consumers, technologies, and suppliers, the article
sheds light on the evolution of IT applications in the tourism and hospitality industries. The
article demonstrates that IT is increasingly becoming critical for the competitive operations of
the tourism and hospitality organizations as well as for managing the distribution and
marketing of organizations on a global scale
Fatores que afetam a adoção de anålises de Big Data em empresas
With the total quantity of data doubling every two years, the low price of computing and data storage, make Big
Data analytics (BDA) adoption desirable for companies, as a tool to get competitive advantage. Given the availability
of free software, why have some companies failed to adopt these techniques? To answer this question,
we extend the unified theory of technology adoption and use of technology model (UTAUT) adapted for the BDA
context, adding two variables: resistance to use and perceived risk. We used the level of implementation of
these techniques to divide companies into users and non-users of BDA. The structural models were evaluated
by partial least squares (PLS). The results show the importance of good infrastructure exceeds the difficulties
companies face in implementing it. While companies planning to use Big Data expect strong results, current
users are more skeptical about its performance.Con la cantidad total de datos duplicåndose cada dos años, el bajo precio de la informåtica y del almacenamiento
de datos, la adopciĂłn del anĂĄlisis Big Data (BDA) es altamente deseable para las empresas, como un
instrumento para conseguir una ventaja competitiva. Dada la disponibilidad de software libre, ¿por qué algunas
empresas no han adoptado estas tĂ©cnicas? Para responder a esta pregunta, ampliamos la teorĂa unificada
de la adopciĂłn y uso de tecnologĂa (UTAUT) adaptado para el contexto BDA, agregando dos variables: resistencia
al uso y riesgo percibido. Utilizamos el grado de implantación de estas técnicas para dividir las empresas
entre: usuarias y no usuarias de BDA. Los modelos estructurales fueron evaluados con partial least squres (PLS).
Los resultados muestran que la importancia de una buena infraestructura excede las dificultades que enfrentan
las empresas para implementarla. Mientras que las compañĂas que planean usar BDA esperan muy buenos
resultados, las usuarias actuales son mås escépticos sobre su rendimiento.Com a quantidade total de dados duplicando a cada dois anos, o baixo preço da computação e do armazenamento
de dados tornam a adoção de anålises de Big Data (BDA) desejåvel para as empresas, como aquelas
que obterĂŁo uma vantagem competitiva. Dada a disponibilidade de software livre, por que algumas empresas
não adotaram essas técnicas? Para responder a essa pergunta, estendemos a teoria unificada de adoção e uso
de tecnologia (UTAUT) adaptado para o contexto do BDA, adicionando duas variĂĄveis: resistĂȘncia ao uso e risco
percebido. Usamos a nĂvel da implementação da tecnologia para dividir as empresas em usuĂĄrios e nĂŁo usuĂĄrios
de técnicas de BDA. Os modelos estruturais foram avaliados por partial least squares (PLS). Os resultados
mostram que a importĂąncia de uma boa infraestrutura excede as dificuldades que as empresas enfrentam para
implementĂĄ-la. Enquanto as empresas que planejam usar Big Data esperam resultados fortes, os usuĂĄrios
atuais são mais céticos em relação ao seu desempenho
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