scholarly journals Significance of Digital Data Visualization Tools in Big Data Analysis for Business Decisions

2017 ◽  
Vol 165 (5) ◽  
pp. 15-18
Author(s):  
Kirti Mahajan ◽  
Leena Ajay
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Visualization ◽  
Big Data Analysis ◽  
Digital Data ◽  
Business Decisions ◽  
Visualization Tools

Design and Implementation of Financial Big Data Visualization Analysis Platform

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Xi Chen ◽  
Bo Fan ◽  
Jie Zheng ◽  
Hongyan Cui
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Visualization ◽  
Production Efficiency ◽  
Big Data Analysis ◽  
Research Field ◽  
Visualization Analysis ◽  
Front End ◽  
Big Data Visualization ◽  
Analysis Platform

At present, it has become a hot research field to improve production efficiency and improve life experience through big data analysis. In the process of big data analysis, how to vividly display the results of the analysis is crucial. So, this paper introduces a set of big data visualization analysis platform based on financial field. The platform adopts the MVC system architecture, which is mainly composed of two parts: the background and the front end. The background part is built on the Django framework, and the front end is built with html5, css3, and JavaScript. The chart is rendered by Echarts. The platform can realize the classification of customers' savings potential through bank data, and make portraits of customers with different savings levels. The data analysis results can be dynamically displayed and interact wit

scholarly journals Programming big data analysis: principles and solutions

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Loris Belcastro ◽  
Riccardo Cantini ◽  
Fabrizio Marozzo ◽  
Alessio Orsino ◽  
Domenico Talia ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Message Passing ◽  
Global Analysis ◽  
Big Data Analysis ◽  
Digital Data ◽  
Advantages And Disadvantages ◽  
Depth Analysis ◽  
Social Media Platforms ◽  
Many Sources

AbstractIn the age of the Internet of Things and social media platforms, huge amounts of digital data are generated by and collected from many sources, including sensors, mobile devices, wearable trackers and security cameras. This data, commonly referred to as Big Data, is challenging current storage, processing, and analysis capabilities. New models, languages, systems and algorithms continue to be developed to effectively collect, store, analyze and learn from Big Data. Most of the recent surveys provide a global analysis of the tools that are used in the main phases of Big Data management (generation, acquisition, storage, querying and visualization of data). Differently, this work analyzes and reviews parallel and distributed paradigms, languages and systems used today to analyze and learn from Big Data on scalable computers. In particular, we provide an in-depth analysis of the properties of the main parallel programming paradigms (MapReduce, workflow, BSP, message passing, and SQL-like) and, through programming examples, we describe the most used systems for Big Data analysis (e.g., Hadoop, Spark, and Storm). Furthermore, we discuss and compare the different systems by highlighting the main features of each of them, their diffusion (community of developers and users) and the main advantages and disadvantages of using them to implement Big Data analysis applications. The final goal of this work is to help designers and developers in identifying and selecting the best/appropriate programming solution based on their skills, hardware availability, application domains and purposes, and also considering the support provided by the developer community.

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.

Hadoop Framework for Handling Big Data Needs

2018 ◽  
pp. 101-122
Author(s):  
Rupali Ahuja
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Large Datasets ◽  
Software Frameworks ◽  
Distributed Framework ◽  
Visualization Tools ◽  
Processing And Storage ◽  
And Storage ◽  
Hadoop Framework

The data generated today has outgrown the storage as well as computing capabilities of traditional software frameworks. Large volumes of data if aggregated and analyzed properly may provide useful insights to predict human behavior, to increase revenues, get or retain customers, improve operations, combat crime, cure diseases, etc. In conclusion, the results of effective Big Data analysis can be used to provide actionable intelligence for humans, as well as for machine consumption. New tools, techniques, technologies and methods are being developed to store, retrieve, manage, aggregate, correlate and analyze Big Data. Hadoop is a popular software framework for handling Big Data needs. Hadoop provides a distributed framework for processing and storage of large datasets. This chapter discusses in detail the Hadoop framework, its features, applications and popular distributions, and its Storage and Visualization tools.

scholarly journals Broken data: Conceptualising data in an emerging world

2018 ◽  
Vol 5 (1) ◽  
pp. 205395171775322 ◽  
Author(s):  
Sarah Pink ◽  
Minna Ruckenstein ◽  
Robert Willim ◽  
Melisa Duque
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Human Activity ◽  
Data Cleaning ◽  
Big Data Analysis ◽  
Sound Art ◽  
Digital Data ◽  
Tracking Data ◽  
Everyday Activity ◽  
Music Production

In this article, we introduce and demonstrate the concept-metaphor of broken data. In doing so, we advance critical discussions of digital data by accounting for how data might be in processes of decay, making, repair, re-making and growth, which are inextricable from the ongoing forms of creativity that stem from everyday contingencies and improvisatory human activity. We build and demonstrate our argument through three examples drawn from mundane everyday activity: the incompleteness, inaccuracy and dispersed nature of personal self-tracking data; the data cleaning and repair processes of Big Data analysis and how data can turn into noise and vice versa when they are transduced into sound within practices of music production and sound art. This, we argue is a necessary step for considering the meaning and implications of data as it is increasingly mobilised in ways that impact society and our everyday worlds.

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.

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