scholarly journals Big Data Visualization Tools: A Survey - The New Paradigms, Methodologies and Tools for Large Data Sets Visualization

2017 ◽  
Author(s):  
Enrico G. Caldarola ◽  
Antonio M. Rinaldi
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Big Data Visualization ◽  
Visualization Tools

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.

ZFDV: Zoom Financial Data Visualization in the Era of Big Data

2014 ◽  
Vol 989-994 ◽  
pp. 2457-2461 ◽  
Author(s):  
Ting Ting Jiang ◽  
Qing Gang Wang ◽  
Hai Kuo Zhang ◽  
Wei Dong Xiao ◽  
Chong Zhang ◽  
...  
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Large Data ◽  
Financial Data ◽  
Human Interaction ◽  
Large Data Sets ◽  
Data Sets ◽  
Preliminary Design ◽  
Interactive Navigation

With the advent of the era of big data, data visualization display faces great challenges. Big data, especially in industries such as telecommunications, finance, almost to the point of "data is the business itself". At this time, in order to let more people understand, use and analyze the data better, we proposed the method to display the big data in the field of finance and the notion of the Zoom financial data visualization (ZFDV). By providing a consistent set of the preliminary design of ZFDV and the interaction of ZFDV techniques, ZFDV makes it possible for users to browse through very large data sets. These techniques use the structure of the displayed data to guide the human-interaction and provide a way to improve interactive navigation in the financial.

Big Data Visualization Tools and Techniques

2018 ◽  
pp. 465-496
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.

A Detailed Study on Classification Algorithms in Big Data

2020 ◽  
pp. 30-46
Author(s):  
Saranya N. ◽  
Saravana Selvam
Keyword(s):  
Big Data ◽  
Random Forest ◽  
Linear Regression ◽  
Comprehensive Evaluation ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Classification Methods ◽  
Computing Science ◽  
Data Collections

After an era of managing data collection difficulties, these days the issue has turned into the problem of how to process these vast amounts of information. Scientists, as well as researchers, think that today, probably the most essential topic in computing science is Big Data. Big Data is used to clarify the huge volume of data that could exist in any structure. This makes it difficult for standard controlling approaches for mining the best possible data through such large data sets. Classification in Big Data is a procedure of summing up data sets dependent on various examples. There are distinctive classification frameworks which help us to classify data collections. A few methods that discussed in the chapter are Multi-Layer Perception Linear Regression, C4.5, CART, J48, SVM, ID3, Random Forest, and KNN. The target of this chapter is to provide a comprehensive evaluation of classification methods that are in effect commonly utilized.

Uncertainty-Based Clustering Algorithms for Large Data Sets

2018 ◽  
pp. 1-33 ◽  
Author(s):  
B. K. Tripathy ◽  
Hari Seetha ◽  
M. N. Murty
Keyword(s):  
Big Data ◽  
Data Clustering ◽  
Clustering Algorithms ◽  
Large Data ◽  
Large Data Sets ◽  
Mining Machine ◽  
Data Sets ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
New Algorithms

Data clustering plays a very important role in Data mining, machine learning and Image processing areas. As modern day databases have inherent uncertainties, many uncertainty-based data clustering algorithms have been developed in this direction. These algorithms are fuzzy c-means, rough c-means, intuitionistic fuzzy c-means and the means like rough fuzzy c-means, rough intuitionistic fuzzy c-means which base on hybrid models. Also, we find many variants of these algorithms which improve them in different directions like their Kernelised versions, possibilistic versions, and possibilistic Kernelised versions. However, all the above algorithms are not effective on big data for various reasons. So, researchers have been trying for the past few years to improve these algorithms in order they can be applied to cluster big data. The algorithms are relatively few in comparison to those for datasets of reasonable size. It is our aim in this chapter to present the uncertainty based clustering algorithms developed so far and proposes a few new algorithms which can be developed further.

Electronic Records Management - An Old Solution to a New Problem

Big Data ◽  
2016 ◽  
pp. 2249-2274
Author(s):  
Chinh Nguyen ◽  
Rosemary Stockdale ◽  
Helana Scheepers ◽  
Jason Sargent
Keyword(s):  
Big Data ◽  
Rapid Development ◽  
Large Data ◽  
Large Data Sets ◽  
Electronic Records ◽  
Future Research ◽  
Records Management ◽  
Data Sets ◽  
Interactive Nature ◽  
Electronic Records Management

The rapid development of technology and interactive nature of Government 2.0 (Gov 2.0) is generating large data sets for Government, resulting in a struggle to control, manage, and extract the right information. Therefore, research into these large data sets (termed Big Data) has become necessary. Governments are now spending significant finances on storing and processing vast amounts of information because of the huge proliferation and complexity of Big Data and a lack of effective records management. On the other hand, there is a method called Electronic Records Management (ERM), for controlling and governing the important data of an organisation. This paper investigates the challenges identified from reviewing the literature for Gov 2.0, Big Data, and ERM in order to develop a better understanding of the application of ERM to Big Data to extract useable information in the context of Gov 2.0. The paper suggests that a key building block in providing useable information to stakeholders could potentially be ERM with its well established governance policies. A framework is constructed to illustrate how ERM can play a role in the context of Gov 2.0. Future research is necessary to address the specific constraints and expectations placed on governments in terms of data retention and use.

Applying the K-Means Algorithm in Big Raw Data Sets with Hadoop and MapReduce

2016 ◽  
pp. 1220-1243
Author(s):  
Ilias K. Savvas ◽  
Georgia N. Sofianidou ◽  
M-Tahar Kechadi
Keyword(s):  
Big Data ◽  
Clustering Algorithm ◽  
File System ◽  
Large Data ◽  
Large Data Sets ◽  
Distributed File System ◽  
Data Sets ◽  
Raw Data ◽  
Hadoop Distributed File System ◽  
Access To Data

Big data refers to data sets whose size is beyond the capabilities of most current hardware and software technologies. The Apache Hadoop software library is a framework for distributed processing of large data sets, while HDFS is a distributed file system that provides high-throughput access to data-driven applications, and MapReduce is software framework for distributed computing of large data sets. Huge collections of raw data require fast and accurate mining processes in order to extract useful knowledge. One of the most popular techniques of data mining is the K-means clustering algorithm. In this study, the authors develop a distributed version of the K-means algorithm using the MapReduce framework on the Hadoop Distributed File System. The theoretical and experimental results of the technique prove its efficiency; thus, HDFS and MapReduce can apply to big data with very promising results.

Effective visualization tools for large data sets

1994 ◽  
Author(s):  
Shaun M. Oborn ◽  
Dean S. Garlick
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Visualization Tools ◽  
Effective Visualization

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.

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