LiveSankey: Advanced Web Visualization in Data Intelligence Multi Domain Contexts

2020 ◽  
Author(s):  
José M. Conejero ◽  
Juan Carlos Preciado ◽  
Alvaro E. Prieto ◽  
Roberto Rodriguez-Echeverria ◽  
Fernando Sánchez-Figueroa
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Business Intelligence ◽  
Decision Makers ◽  
Need To Evaluate ◽  
Big Data Technologies ◽  
Visualization Tools ◽  
Web Visualization ◽  
Meaningful Relationships ◽  
Multiple Conditions

In the last years, the growing volumes and sources of data has made Big Data technologies to become mainstream. In that sense, techniques like Data Visualization are being used more and more to group large amounts of data in order to transform them into useful information. Nevertheless, these techniques are currently included in Business Intelligence approaches to provide companies and public organizations with helpful tools for making decisions based on evidences instead of intuition. The Sankey diagram is an example of those complex visualization tools allowing the user to graphically trace meaningful relationships in large volumes of data. However, this type of diagram is usually static so they must be continuously and manually rebuilt on top of massive multivariable environments whenever decision makers need to evaluate different options and they do not allow to establish conditions over the data shown. This paper presents LiveSankey, an approach to automatically generate dynamic Sankey Diagrams allowing users to filter the data shown. As a result, multiple conditions may be established over the data used and the corresponding diagram can be dynamically rebuilt.

scholarly journals Business Intelligence Model to analyze Social Media through Big Data analytics

2021 ◽  
Vol 119 ◽  
pp. 07006
Author(s):  
Kawtar Mouyassir ◽  
Mohamed Hanine ◽  
Hassan Ouahmane
Keyword(s):  
Social Media ◽  
Big Data ◽  
Business Intelligence ◽  
Qualitative Data ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Social Business ◽  
Social Media Networks ◽  
Big Data Technologies ◽  
Voice Of Customer

Business Intelligence (BI) is a collection of tools, technologies, and practices that include the entire process of collecting, processing, and analyzing qualitative information, to help entrepreneurs better understand their business and marketplace. Every day, social networks expand at a faster rate and pace, which sees them as a source of Big Data. Therefore, BI is developed in the same way on VoC (Voice of Customer) expressed in social media as qualitative data for company decision-makers, who desire to have a clear perception of customers’ behaviour. In this article, we present a comparative study between traditional BI and social BI, then examine an approach to social business intelligence. Next, we are going to demonstrate the power of Big Data that can be integrated into BI so that we can finally describe in detail how Big Data technologies, like Apache Flume, help to collect unstructured data from various sources such as social media networks and store it in Hadoop storage.

Employing Multidimensional Data Visualization Tools to Assess the Impact of Constraint Uncertainties on Complex Design Problems

2017 ◽  
Author(s):  
Gary M. Stump ◽  
Simon W. Miller ◽  
Michael A. Yukish ◽  
Christopher M. Farrell
Keyword(s):  
Data Visualization ◽  
Decision Makers ◽  
Multidimensional Data ◽  
Design Problems ◽  
Design Constraints ◽  
Performance Space ◽  
Multidimensional Data Visualization ◽  
Complex Design ◽  
Visualization Tools ◽  
The Impact

A potential source of uncertainty within multi-objective design problems can be the exact value of the underlying design constraints. This uncertainty will affect the resulting performance of the selected system commensurate with the level of risk that decision-makers are willing to accept. This research focuses on developing visualization tools that allow decision-makers to specify uncertainty distributions on design constraints and to visualize their effects in the performance space using multidimensional data visualization methods to solve problems with high orders of computational complexity. These visual tools will be demonstrated using an example portfolio design scenario in which the goal of the design problem is to maximize the performance of a portfolio with an uncertain budget constraint.

scholarly journals Graph structures for data visualizations

2021 ◽  
Vol 6 (2) ◽  
pp. 24-31
Author(s):  
Stefana Janićijević ◽  
Vojkan Nikolić
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Types ◽  
Current State ◽  
Graph Structures ◽  
Big Data Visualization ◽  
Visualization Tools ◽  
Graph System ◽  
Data Visualizations

Networks are all around us. Graph structures are established in the core of every network system therefore it is assumed to be understood as graphs as data visualization objects. Those objects grow from abstract mathematical paradigms up to information insights and connection channels. Essential metrics in graphs were calculated such as degree centrality, closeness centrality, betweenness centrality and page rank centrality and in all of them describe communication inside the graph system. The main goal of this research is to look at the methods of visualization over the existing Big data and to present new approaches and solutions for the current state of Big data visualization. This paper provides a classification of existing data types, analytical methods, techniques and visualization tools, with special emphasis on researching the evolution of visualization methodology in recent years. Based on the obtained results, the shortcomings of the existing visualization methods can be noticed.

Big Data Visualization Tools and Techniques

2022 ◽  
pp. 590-621
Author(s):  
Obinna Chimaobi Okechukwu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Visualization ◽  
Data Sets ◽  
New Paradigm ◽  
Volume Velocity ◽  
Big Data Visualization ◽  
Visualization Tools ◽  
Tools And Techniques ◽  
Data Analysis Methods

In this chapter, a discussion is presented on the latest tools and techniques available for Big Data Visualization. These tools, techniques and methods need to be understood appropriately to analyze Big Data. Big Data is a whole new paradigm where huge sets of data are generated and analyzed based on volume, velocity and variety. Conventional data analysis methods are incapable of processing data of this dimension; hence, it is fundamentally important to be familiar with new tools and techniques capable of processing these datasets. This chapter will illustrate tools available for analysts to process and present Big Data sets in ways that can be used to make appropriate decisions. Some of these tools (e.g., Tableau, RapidMiner, R Studio, etc.) have phenomenal capabilities to visualize processed data in ways traditional tools cannot. The chapter will also aim to explain the differences between these tools and their utilities based on scenarios.

scholarly journals Big Data Analytics for Deriving Business Intelligence Rules

2019 ◽  
Vol 9 (1) ◽  
pp. 1629-1637
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Information Storage ◽  
Primary Method ◽  
Scientific Experiments ◽  
Big Data Technologies ◽  
Real Time Applications ◽  
Traditional Approaches

Big data is a large volume of data pool and processing and analyzing these data is tedious jobs. The aim of fulfilling huge information storage needs is that the structural transformation of repository system using traditional approaches to NoSQL technology. However, the existing technologies for storage are inefficient since, they do not generated data that are scalable, consistent and solutions for rapidly evolving diversified data. The primary method for storing huge amounts of data is used for analytics in real time applications like healthcare, scientific experiments, e-business and networks. In this paper, it is in sighted the characteristics, application, tools of big data, Technologies, Big data analytics, challenges and issues in Big data.

scholarly journals Data Visualization Method as the Facilitator for Business Intelligence

2019 ◽  
Vol 8 (6) ◽  
pp. 3925-3928 ◽  
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Business Intelligence ◽  
Visualization Method ◽  
Business Decisions ◽  
Correlation Matrices ◽  
Visual Elements ◽  
Business Analysis ◽  
Scatter Plots ◽  
Pros And Cons

Data visualization methods are used to support business analysis. This paper explores the study on the sophisticated data visualization methods for business inventiveness. These comprise Line and Bar Charts, Scatter Plots, Network Diagrams, Bubble plot, Correlation Matrices and Donut Diagram. The concepts of visual elements are explained in the context of business perception. The paper reviews the capabilities and prophecies of the visualization methods in business analysis. When more data has to be symbolized, the concentration increases and it leads to difficulty in understanding the information to be dillydallied. Data visualization methods for business decisions save the time and resources as well as provide better understanding. The study explores that there exist habituated data visualization methods that are useful in business intelligence. The methods serve as the elite outfits to epitomize Big Data effectively. These techniques are scouted through this literature review. We investigate the pros and cons of data visualization methods in business intelligence.

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