17,560 research outputs found
Fatores que afetam a adoção de análises 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 duplicándose cada dos años, el bajo precio de la informática y del almacenamiento
de datos, la adopción del análisis 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 técnicas? Para responder a esta pregunta, ampliamos la teoría unificada
de la adopción y uso de tecnología (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 compañías que planean usar BDA esperan muy buenos
resultados, las usuarias actuales son más 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 análises de Big Data (BDA) desejável para as empresas, como aquelas
que obterão 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 variáveis: resistência ao uso e risco
percebido. Usamos a nível da implementação da tecnologia para dividir as empresas em usuários e não usuários
de técnicas de BDA. Os modelos estruturais foram avaliados por partial least squares (PLS). Os resultados
mostram que a importância de uma boa infraestrutura excede as dificuldades que as empresas enfrentam para
implementá-la. Enquanto as empresas que planejam usar Big Data esperam resultados fortes, os usuários
atuais são mais céticos em relação ao seu desempenho
Social media analytics: a survey of techniques, tools and platforms
This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing
Exploratory topic modeling with distributional semantics
As we continue to collect and store textual data in a multitude of domains,
we are regularly confronted with material whose largely unknown thematic
structure we want to uncover. With unsupervised, exploratory analysis, no prior
knowledge about the content is required and highly open-ended tasks can be
supported. In the past few years, probabilistic topic modeling has emerged as a
popular approach to this problem. Nevertheless, the representation of the
latent topics as aggregations of semi-coherent terms limits their
interpretability and level of detail.
This paper presents an alternative approach to topic modeling that maps
topics as a network for exploration, based on distributional semantics using
learned word vectors. From the granular level of terms and their semantic
similarity relations global topic structures emerge as clustered regions and
gradients of concepts. Moreover, the paper discusses the visual interactive
representation of the topic map, which plays an important role in supporting
its exploration.Comment: Conference: The Fourteenth International Symposium on Intelligent
Data Analysis (IDA 2015
Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda
Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online
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