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.

Descriptive and Predictive Analytical Methods for Big Data

Web Services ◽  
2019 ◽  
pp. 314-331 ◽  
Author(s):  
Sema A. Kalaian ◽  
Rafa M. Kasim ◽  
Nabeel R. Kasim
Keyword(s):  
Big Data ◽  
Standard Deviation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Knowledge Discovery ◽  
Data Visualization ◽  
Analytical Methods ◽  
Data Analytics ◽  
Enterprise Performance ◽  
Analytical Tools

Data analytics and modeling are powerful analytical tools for knowledge discovery through examining and capturing the complex and hidden relationships and patterns among the quantitative variables in the existing massive structured Big Data in efforts to predict future enterprise performance. The main purpose of this chapter is to present a conceptual and practical overview of some of the basic and advanced analytical tools for analyzing structured Big Data. The chapter covers descriptive and predictive analytical methods. Descriptive analytical tools such as mean, median, mode, variance, standard deviation, and data visualization methods (e.g., histograms, line charts) are covered. Predictive analytical tools for analyzing Big Data such as correlation, simple- and multiple- linear regression are also covered in the chapter.

scholarly journals Training institutes and training in official statistics in Africa: An overview

2021 ◽  
Vol 37 (3) ◽  
pp. 835-851
Author(s):  
Hugues Kouassi Kouadio
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Current Situation ◽  
Official Statistics ◽  
Statistical Literacy ◽  
The Face ◽  
Technological Developments ◽  
Language Areas ◽  
University Training ◽  
And Training

The training offer of official statistics in statistical training institutes has been constantly evolving as it adapts to the statistical environment and technological developments. Based on a literature review and the mobilisation of curricula and programmes offered by statistical training centres in Africa, this paper presents the current situation of training in official statistics as well as the challenges to be faced. Despite harmonisation efforts, there are still differences between language areas and training types. Engineer and vocational statistical training are better suited to the needs of National Statistical Institutes than university training. It is essential that the training of statisticians is strategically thought out so that they can be reactive and dynamic in the face of changes and upheavals they will be confronted with in the context of data revolution and big data. Their training should reinforce the statistical literacy dimension with a view to reducing the gap between producers and users.

scholarly journals Biologically Inspired Techniques for Data Mining

2014 ◽  
pp. 1-10 ◽  
Author(s):  
Shafiq Alam ◽  
Gillian Dobbie ◽  
Yun Sing Koh ◽  
Saeed ur Rehman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Particle Swarm Optimization ◽  
Information Extraction ◽  
Knowledge Discovery ◽  
Swarm Intelligence ◽  
Optimization Techniques ◽  
Biologically Inspired ◽  
Swarm Optimization ◽  
Hidden Knowledge

Knowledge Discovery and Data (KDD) mining helps uncover hidden knowledge in huge amounts of data. However, recently, different researchers have questioned the capability of traditional KDD techniques to tackle the information extraction problem in an efficient way while achieving accurate results when the amount of data grows. One of the ways to overcome this problem is to treat data mining as an optimization problem. Recently, a huge increase in the use of Swarm Intelligence (SI)-based optimization techniques for KDD has been observed due to the flexibility, simplicity, and extendibility of these techniques to be used for different data mining tasks. In this chapter, the authors overview the use of Particle Swarm Optimization (PSO), one of the most cited SI-based techniques in three different application areas of KDD, data clustering, outlier detection, and recommender systems. The chapter shows that there is a tremendous potential in these techniques to revolutionize the process of extracting knowledge from big data using these techniques.

Biologically Inspired Techniques for Data Mining

2016 ◽  
pp. 2275-2284
Author(s):  
Shafiq Alam ◽  
Gillian Dobbie ◽  
Yun Sing Koh ◽  
Saeed ur Rehman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Particle Swarm Optimization ◽  
Information Extraction ◽  
Knowledge Discovery ◽  
Swarm Intelligence ◽  
Optimization Techniques ◽  
Biologically Inspired ◽  
Swarm Optimization ◽  
Hidden Knowledge

Knowledge Discovery and Data (KDD) mining helps uncover hidden knowledge in huge amounts of data. However, recently, different researchers have questioned the capability of traditional KDD techniques to tackle the information extraction problem in an efficient way while achieving accurate results when the amount of data grows. One of the ways to overcome this problem is to treat data mining as an optimization problem. Recently, a huge increase in the use of Swarm Intelligence (SI)-based optimization techniques for KDD has been observed due to the flexibility, simplicity, and extendibility of these techniques to be used for different data mining tasks. In this chapter, the authors overview the use of Particle Swarm Optimization (PSO), one of the most cited SI-based techniques in three different application areas of KDD, data clustering, outlier detection, and recommender systems. The chapter shows that there is a tremendous potential in these techniques to revolutionize the process of extracting knowledge from big data using these techniques.

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.

Descriptive and Predictive Analytical Methods for Big Data

2016 ◽  
pp. 66-83
Author(s):  
Sema A. Kalaian ◽  
Rafa M. Kasim ◽  
Nabeel R. Kasim
Keyword(s):  
Big Data ◽  
Standard Deviation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Knowledge Discovery ◽  
Data Visualization ◽  
Analytical Methods ◽  
Data Analytics ◽  
Enterprise Performance ◽  
Analytical Tools

Data analytics and modeling are powerful analytical tools for knowledge discovery through examining and capturing the complex and hidden relationships and patterns among the quantitative variables in the existing massive structured Big Data in efforts to predict future enterprise performance. The main purpose of this chapter is to present a conceptual and practical overview of some of the basic and advanced analytical tools for analyzing structured Big Data. The chapter covers descriptive and predictive analytical methods. Descriptive analytical tools such as mean, median, mode, variance, standard deviation, and data visualization methods (e.g., histograms, line charts) are covered. Predictive analytical tools for analyzing Big Data such as correlation, simple- and multiple- linear regression are also covered in the chapter.

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