scholarly journals PROPOSTA DE UM FRAMEWORK BASEADO EM MINERAÇÃO DE DADOS PARA REDES 5G

2017 ◽  
Vol 16 (2) ◽  
Author(s):  
Carlos Renato Storck ◽  
Edwaldo Araújo Sales ◽  
Luis Enrique Zárate ◽  
Fátima De L. P. D. Figueiredo
Keyword(s):  
Big Data ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Databases ◽  
5G Networks ◽  
Urban Computing ◽  
Computing Framework

Cidades inteligentes vêm ganhando, cada vez mais, notoriedade. Através delas, a população pode ter melhores serviços e qualidade de vida urbana. Com as futuras redes de celulares de quinta geração (5G) será possível coletar dados por meio de diversas fontes espalhadas pela cidade, tais como sensores, dispositivos móveis, redes veiculares e de telefonia, dentre outras. Nesse cenário, haverá a necessidade de análise de grandes volumes de dados, com o objetivo de extrair conhecimento e informação útil para o planejamento inteligente e dinâmico. Este artigo apresenta uma proposta de framework baseado em mineração de dados para redes 5G, denominado Urban Computing Framework in 5G Networks (CoUrbF5G). Padrões reais de uma rede de telefonia móvel são encontrados e analisados, aplicando técnicas de mineração de dados, em conjunto com métodos auxiliares na condução de processos como Knowledge Discovery in Databases (KDD) e Big Data.

scholarly journals Big Data in Motion: A Vehicle-Assisted Urban Computing Framework for Smart Cities

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 55951-55965 ◽  
Author(s):  
Murk ◽  
Asad Waqar Malik ◽  
Imran Mahmood ◽  
Nadeem Ahmed ◽  
Zahid Anwar
Keyword(s):  
Big Data ◽  
Smart Cities ◽  
Urban Computing ◽  
Computing Framework

scholarly journals Customized Knowledge Discovery in Databases methodology for the Control of Assembly Systems

Machines ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 45
Author(s):  
Edoardo Storti ◽  
Laura Cattaneo ◽  
Adalberto Polenghi ◽  
Luca Fumagalli
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Knowledge Discovery ◽  
Automotive Industry ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Knowledge Discovery In Databases ◽  
Assembly Systems ◽  
Industrial Big Data

The advent of Industry 4.0 has brought to extremely powerful data collection possibilities. Despite this, the potential contained in databases is often partially exploited, especially focusing on the manufacturing field. There are several root causes of this paradox, but the crucial one is the absence of a well-established and standardized Industrial Big Data Analytics procedure, in particular for the application within the assembly systems. This work aims to develop a customized Knowledge Discovery in Databases (KDD) procedure for its application within the assembly department of Bosch VHIT S.p.A., active in the automotive industry. The work is focused on the data mining phase of the KDD process, where ARIMA method is used. Various applications to different lines of the assembly systems show the effectiveness of the customized KDD for the exploitation of production databases for the company, and for the spread of such a methodology to other companies too.

Knowledge Discovery and Data Visualization

2017 ◽  
Vol 7 (3) ◽  
pp. 56-69 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Knowledge Discovery ◽  
Data Visualization ◽  
Knowledge Discovery In Databases ◽  
Statistical Literacy ◽  
Textual Databases ◽  
Literacy Knowledge ◽  
Hidden Knowledge ◽  
The Way

This article reviews the literature in the search for the theories and perspectives of knowledge discovery and data visualization. The literature review highlights the overview of knowledge discovery; Knowledge Discovery in Databases (KDD); Knowledge Discovery in Textual Databases (KDT); the overview of data visualization; the significant perspectives on data visualization; data visualization and big data; and data visualization and statistical literacy. Knowledge discovery is the process of searching for hidden knowledge in the massive amounts of data that individuals are technically capable of generating and storing. Data visualization is an easy way to convey concepts in a universal manner. Organizations, that utilize knowledge discovery and data visualization, are more likely to find both knowledge and information they need when they need them. The findings present valuable insights and further understanding of the way in which knowledge discovery and data visualization efforts should be focused.

scholarly journals Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Shadi Aljawarneh ◽  
Aurea Anguera ◽  
John William Atwood ◽  
Juan A. Lara ◽  
David Lizcano
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Time Series Data ◽  
Medical Patient ◽  
Lessons Learned ◽  
Physiological Signals ◽  
Knowledge Discovery In Databases ◽  
Series Data ◽  
Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

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