Exploratory Study for Data Visualization in Internet of Things

Khalid, Mohamad Yuzrie; Then, Patrick; Raman, Valliappan

doi:10.1109/compsac.2018.10287

“…Another research paper discusses the importance of data visualization in business and highlights the role of dashboards and visualizations in decision-making. It mentions Tableau as a tool for creating data visualizations and dashboards [15]. Finally, an article briefly mentions Tableau as one of the tools used for data visualization, alongside Python libraries and other tools [16].…”

Section: Tableaumentioning

confidence: 99%

A Comprehensive Review of Data Visualization Tools: Features, Strengths, and Weaknesses

Lavanya,

Sindhuja,

Gaurav

et al. 2023

IJCERT

3

0

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Data visualization tools have revolutionized processing, analysing, and communicating data. With the increasing amount of data available, it has become increasingly important to present data in an easily understandable and visually appealing way. As such, data visualization tools have become essential to data analysis and decisionmaking processes in various fields, including business, healthcare, social sciences, and engineering. This review paper aims to provide an overview of the various data visualization tools available and their features, strengths, and weaknesses. We begin by introducing the concept of data visualization and its importance in the data analysis process. We then provide a brief history of data visualization, highlighting its evolution over time from static charts to interactive and dynamic visualizations. We then discuss the available data visualization tools, including bar charts, line graphs, scatter plots, heat maps, tree maps, and network diagrams. For each type of visualization, we provide examples of when and how they can be used to present and analyse data effectively. Next, we examine the features and functionalities of popular data visualization tools, such as Tableau, Power BI, Google Data Studio, D3.js, and Python libraries like Matplotlib, Seaborn, and Plotly. We discuss the strengths and weaknesses of each tool and provide examples of realworld applications. In addition, we highlight the importance of choosing the right visualization tool based on the data type, audience, and purpose. We also discuss best practices for creating effective data visualizations, such as choosing the right colour scheme, designing for accessibility, and avoiding common pitfalls. Finally, we discuss future trends and developments in data visualization, such as using augmented and virtual reality for data visualization and incorporating machine learning and artificial intelligence in data visualization tools. In conclusion, data visualization tools have become an essential part of the data analysis process. This review paper overviews the available data visualization tools and their features, strengths, and weaknesses. By understanding the strengths and limitations of different visualization tools, researchers and analysts can effectively present and analyse data, leading to better decision-making and insights.

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“…Another research paper discusses the importance of data visualization in business and highlights the role of dashboards and visualizations in decision-making. It mentions Tableau as a tool for creating data visualizations and dashboards [15]. Finally, an article briefly mentions Tableau as one of the tools used for data visualization, alongside Python libraries and other tools [16].…”

Section: Tableaumentioning

confidence: 99%

Assessing the Performance of Python Data Visualization Libraries: A Review

Lavanya,

Gaurav,

Sindhuja

et al. 2023

IJCERT

2

0

View full text Add to dashboard Cite

Data visualization tools have revolutionized processing, analysing, and communicating data. With the increasing amount of data available, it has become increasingly important to present data in an easily understandable and visually appealing way. As such, data visualization tools have become essential to data analysis and decisionmaking processes in various fields, including business, healthcare, social sciences, and engineering. This review paper aims to provide an overview of the various data visualization tools available and their features, strengths, and weaknesses. We begin by introducing the concept of data visualization and its importance in the data analysis process. We then provide a brief history of data visualization, highlighting its evolution over time from static charts to interactive and dynamic visualizations. We then discuss the available data visualization tools, including bar charts, line graphs, scatter plots, heat maps, tree maps, and network diagrams. For each type of visualization, we provide examples of when and how they can be used to present and analyse data effectively. Next, we examine the features and functionalities of popular data visualization tools, such as Tableau, Power BI, Google Data Studio, D3.js, and Python libraries like Matplotlib, Seaborn, and Plotly. We discuss the strengths and weaknesses of each tool and provide examples of realworld applications. In addition, we highlight the importance of choosing the right visualization tool based on the data type, audience, and purpose. We also discuss best practices for creating effective data visualizations, such as choosing the right colour scheme, designing for accessibility, and avoiding common pitfalls. Finally, we discuss future trends and developments in data visualization, such as using augmented and virtual reality for data visualization and incorporating machine learning and artificial intelligence in data visualization tools. In conclusion, data visualization tools have become an essential part of the data analysis process. This review paper overviews the available data visualization tools and their features, strengths, and weaknesses. By understanding the strengths and limitations of different visualization tools, researchers and analysts can effectively present and analyse data, leading to better decision-making and insights.

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“…Na pesquisa apresentada por Khalid et al [7] e discutida a viabilidade de se criar um framework geral para visualização de informações, já que esta e uma lacuna a ser completada no âmbito de IoT. O artigo destaca que por mais que tenham pesquisas para solucionar e muito difícil cobrir toda a análise visual dos dados gerados pro dispositivos IoT, por conta da diversidade de domínios que precisam de um foco específico.…”

Section: Análise Da Literatura Acadêmicaunclassified

“…As ferramentas analisadas foram ThingsBoard 7 , a ferramenta mais focada em IoT, Grafana 8 , e Kibana 9 , ambos sistemas apenas para visualização de informações. O software Kibana não foi possível ser instalado devido a ao erro "Kibana server is not ready yet", foram feitas pesquisas em fóruns, como o Elastic 10 , empresa responsável pelo software, e stackoverflow 11 para tentar corrigir o erro, porem n ao foi possível testar o sistema porque o erro não foi corrigido.…”

Section: Análise Das Ferramentasunclassified