Knowledge discovery for smart grid operation, control, and situation awareness — a big data visualization platform

2016 ◽  
Author(s):  
Yi Gu ◽  
Huaiguang Jiang ◽  
Yingchen Zhang ◽  
Jun Jason Zhang ◽  
Tianlu Gao ◽  
...  
Keyword(s):  
Big Data ◽  
Smart Grid ◽  
Knowledge Discovery ◽  
Data Visualization ◽  
Situation Awareness ◽  
Big Data Visualization ◽  
Operation Control ◽  
Grid Operation

scholarly journals Human-Centric Situational Awareness and Big Data Visualization

10.29007/mq54 ◽  
2019 ◽  
Author(s):  
Sri Teja Bodempudi ◽  
Sharad Sharma ◽  
Atma Sahu ◽  
Rajeev Agrawal
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Situation Awareness ◽  
Situational Awareness ◽  
Research Effort ◽  
Data Sets ◽  
Big Data Visualization ◽  
Immersive Environment ◽  
The Given ◽  
Model Graph

Human-centric situational awareness and visualization are needed for analyzing the big data in an efficient way. One of the challenges is to create an algorithm to analyze the given data without any help of other data analyzing tools. This research effort aims to identify how graphical objects (such as data-shapes) developed in accordance with an analyst's mental model can enhance analyst's situation awareness. Our approach for improved big data visualization is two-fold, focusing on both visualization and interaction. This paper presents the developed data and graph technique based on force- directed model graph in 3D. It is developed using Unity 3D gaming engine. Pilot testing was done with different data sets for checking the efficiency of the system in immersive environment and non-immersive environment. The application is able to handle the data successfully for the given data sets in data visualization. The currently graph can render around 200 to 300 linked nodes in real-time.

An Innovative Methodology for Big Data Visualization for Telemedicine

2019 ◽  
Vol 15 (1) ◽  
pp. 490-497 ◽  
Author(s):  
Antonino Galletta ◽  
Lorenzo Carnevale ◽  
Alessia Bramanti ◽  
Maria Fazio
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Big Data Visualization ◽  
Innovative Methodology

A Visual Insight Into Tweeting Activity Before and During Natural Disasters

2021 ◽  
Vol 12 (3) ◽  
pp. 19-33
Author(s):  
Shadi Maleki ◽  
Milad Mohammadalizadehkorde
Keyword(s):  
Social Media ◽  
Big Data ◽  
Spatial Distribution ◽  
Emergency Management ◽  
Data Visualization ◽  
High Dimensionality ◽  
High Complexity ◽  
Big Data Visualization ◽  
Disaster Studies ◽  
Insight Into

Big data provided by social media has been increasingly used in various fields of research including disaster studies and emergency management. Effective data visualization plays a central role in generating meaningful insight from big data. However, big data visualization has been a challenge due to the high complexity and high dimensionality of it. The purpose of this study is to examine how the number and spatial distribution of tweets changed on the day Hurricane Harvey made landfall near Houston, Texas. For this purpose, this study analyzed the change in tweeting activity between the Friday of Hurricane Harvey and a typical Friday before the event.

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.

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