Visualization Tools for Big Data Analytics in Quantitative Chemical Analysis

2018 ◽  
pp. 873-917 ◽  
Author(s):  
Gerard G. Dumancas ◽  
Ghalib A. Bello ◽  
Jeff Hughes ◽  
Renita Murimi ◽  
Lakshmi Chockalingam Kasi Viswanath ◽  
...  
Keyword(s):  
Analytical Chemistry ◽  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Computer Applications ◽  
Quantitative Chemical Analysis ◽  
Big Data Visualization ◽  
Programming Tools ◽  
Big Data Storage

Modern instruments have the capacity to generate and store enormous volumes of data and the challenges involved in processing, analyzing and visualizing this data are well recognized. The field of Chemometrics (a subspecialty of Analytical Chemistry) grew out of efforts to develop a toolbox of statistical and computer applications for data processing and analysis. This chapter will discuss key concepts of Big Data Analytics within the context of Analytical Chemistry. The chapter will devote particular emphasis on preprocessing techniques, statistical and Machine Learning methodology for data mining and analysis, tools for big data visualization and state-of-the-art applications for data storage. Various statistical techniques used for the analysis of Big Data in Chemometrics are introduced. This chapter also gives an overview of computational tools for Big Data Analytics for Analytical Chemistry. The chapter concludes with the discussion of latest platforms and programming tools for Big Data storage like Hadoop, Apache Hive, Spark, Google Bigtable, and more.

Chemometrics

2019 ◽  
Vol 2 (1) ◽  
pp. 1-42 ◽  
Author(s):  
Gerard G. Dumancas ◽  
Ghalib Bello ◽  
Jeff Hughes ◽  
Renita Murimi ◽  
Lakshmi Viswanath ◽  
...  
Keyword(s):  
Analytical Chemistry ◽  
Big Data ◽  
Data Storage ◽  
State Of The Art ◽  
Fog Computing ◽  
Big Data Analytics ◽  
Computer Applications ◽  
Analytical Instruments ◽  
Big Data Visualization ◽  
And Storage

The accumulation of data from various instrumental analytical instruments has paved a way for the application of chemometrics. Challenges, however, exist in processing, analyzing, visualizing, and storing these data. Chemometrics is a relatively young area of analytical chemistry that involves the use of statistics and computer applications in chemistry. This article will discuss various computational and storage tools of big data analytics within the context of analytical chemistry with examples, applications, and usage details in relation to fog computing. The future of fog computing in chemometrics will also be discussed. The article will dedicate particular emphasis to preprocessing techniques, statistical and machine learning methodology for data mining and analysis, tools for big data visualization, and state-of-the-art applications for data storage using fog computing.

scholarly journals Towards Big Data Analytics in the e-Learning Space

2019 ◽  
Vol 19 (3) ◽  
pp. 16-24 ◽  
Author(s):  
Ivan P. Popchev ◽  
Daniela A. Orozova
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Students At Risk ◽  
Learning Space ◽  
E Learning ◽  
New Research ◽  
Big Data Storage

Abstract The issues related to the analysis and management of Big Data, aspects of the security, stability and quality of the data, represent a new research, and engineering challenge. In the present paper, techniques for Big Data storage, search, analysis and management in the area of the virtual e-Learning space and the problems in front of them are considered. A numerical example for explorative analysis of data about the students from Burgas Free University is applied, using instrument for Data Mining of Orange. The analysis is a base for a system for localization of students at risk.

scholarly journals On Performance of Big Data Storage on Cloud Mechanics in Mobile Digital Healthcare

2021 ◽  
Vol 12 (5) ◽  
pp. 36-49
Author(s):  
Abhilasha Rangra ◽  
Vivek Kumar Sehgal
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Effectiveness And Efficiency ◽  
Big Data Storage ◽  
Digital Healthcare

In recent years, the concept of cloud computing and big data analysis are considered as two major problems. It empowers the resources of computing to be maintained as the service of information technology with high effectiveness and efficiency. In the present scenario, big data is treated as one of the issues that the experts are trying to solve and finding ways to tackle the problem of handling big data analytics, how it could be managed with the technology of cloud computing and handled in the recent systems, and apart from this, the most significant issue is how to have perfect safety of big data in the cloud computing environment. In this paper, the authors mainly improve the performance of big data storage on cloud mechanics as the integration of mobile digital healthcare. The proposed framework involves the process of refining the sensitivity by using a deep learning approach. After this, it involves the step of computing or storage in the cloud-based server in an optimized manner. The experimental analysis provides a significant improvement in terms of cost, time, and accuracy.

Programming and Pre-Processing Systems for Big Data Storage and Visualization

2018 ◽  
pp. 228-253 ◽  
Author(s):  
Hidayat Ur Rahman ◽  
Rehan Ullah Khan ◽  
Amjad Ali
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Visualization ◽  
Missing Values ◽  
Big Data Analytics ◽  
Mechanical Turk ◽  
The Core ◽  
Programming Tools ◽  
Big Data Storage

This chapter of the book chapter provides detailed overview of the major concept used in Big Data. In order to process the huge volume of data, the first step is the pre-processing which is required to anomalies such as, missing values by applying various transformations. This chapter provides a detail overview of preprocessing tools used for Big Data such as, R, Yahoo! Pipes, Mechanical Turk, Elasticsearch etc. Beside preprocessing tools, the chapter provides detailed overview of storage tools, programming tools, data visualization, log processing tools and caching tools used for Big Data analytics. In other words, this chapter is the core of the book and provides the overview of the major technologies discussed later in the book.

scholarly journals Identifying Requirements for Big Data Analytics and Mapping to Hadoop Tools

2019 ◽  
Vol 8 (3) ◽  
pp. 4384-4392
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Management Tools ◽  
Trade Offs ◽  
Category Comparison ◽  
Pros And Cons ◽  
Hadoop Ecosystem ◽  
Functional Areas

Big data is being generating in a wide variety of formats at an exponential rate. Big data analytics deals with processing and analyzing voluminous data to provide useful insight for guided decision making. The traditional data storage and management tools are not well-equipped to handle big data and its application. Apache Hadoop is a popular open-source platform that supports storage and processing of extremely large datasets. For the purposes of big data analytics, Hadoop ecosystem provides a variety of tools. However, there is a need to select a tool that is best suited for a specific requirement of big data analytics. The tools have their own advantages and drawbacks over each other. Some of them have overlapping business use cases however they differ in critical functional areas. So, there is a need to consider the trade-offs between usability and suitability while selecting a tool from Hadoop ecosystem. This paper identifies the requirements for Big Data Analytics (BDA) and maps tools of the Hadoop framework that are best suited for them. For this, we have categorized Hadoop tools according to their functionality and usage. Different Hadoop tools are discussed from the users’ perspective along with their pros and cons, if any. Also, for each identified category, comparison of Hadoop tools based on important parameters is presented. The tools have been thoroughly studied and analyzed based on their suitability for the different requirements of big data analytics. A mapping of big data analytics requirements to the Hadoop tools has been established for use by the data analysts and predictive modelers.

scholarly journals FACTORS AFFECTING THE ADOPTION OF BIG DATA ANALYTICS IN COMPANIES

2019 ◽  
Vol 59 (6) ◽  
pp. 415-429 ◽  
Author(s):  
JUAN-PEDRO CABRERA-SÁNCHEZ ◽  
ÁNGEL F VILLAREJO-RAMOS
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Perceived Risk ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Structural Models ◽  
Free Software ◽  
Unified Theory ◽  
Factors Affecting ◽  
Use Of Technology

ABSTRACT With the total quantity of data doubling every two years, the low price of computing and data storage, make Big Data analytics (BDA) adoption desirable for companies, as a tool to get competitive advantage. Given the availability of free software, why have some companies failed to adopt these techniques? To answer this question, we extend the unified theory of technology adoption and use of technology model (UTAUT) adapted for the BDA context, adding two variables: resistance to use and perceived risk. We used the level of implementation of these techniques to divide companies into users and non-users of BDA. The structural models were evaluated by partial least squares (PLS). The results show the importance of good infrastructure exceeds the difficulties companies face in implementing it. While companies planning to use Big Data expect strong results, current users are more skeptical about its performance.

scholarly journals Big Data on Machine Learning – A Review

2021 ◽  
Vol 8 (3) ◽  
Author(s):  
Balasree K ◽  
Dharmarajan K
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Rapid Development ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Big Data Technology

In rapid development of Big Data technology over the recent years, this paper discussing about the Machine Learning (ML) playing role that is based on methods and algorithms to Big Data Processing and Big Data Analytics. In evolutionary fields and computing fields of developments that both are complementing each other. Big Data: The rapid growth of such data solutions needed to be studied and provided to handle then to gain the knowledge from datasets and extracting values due to the data sets are very high in velocity and variety. The Big data analytics are involving and indicating the appropriate data storage and computational outline that enhanced by using Scalable Machine Learning Algorithms and Big Data Analytics then the analytics to reveal the massive amounts of hidden data’s and secret correlations. This type of Analytic information useful for organizations and companies to gain deeper knowledge, development and getting advantages over the competition. When using this Analytics we can predict the accurate implementation over the data. This paper presented about the detailed review of state-of-the-art developments and overview of advantages and challenges in Machine Learning Algorithms over big data analytics.

Data Mining, Big Data, Data Analytics

Web Services ◽  
2019 ◽  
pp. 89-104
Author(s):  
Priya P. Panigrahi ◽  
Tiratha Raj Singh
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Biological Data ◽  
Smart Devices ◽  
Data Life Cycle ◽  
The Impact ◽  
Special Relevance ◽  
Generation Sequencing

In this digital and computing world, data formation and collection rate are growing very rapidly. With these improved proficiencies of data storage and fast computation along with the real-time distribution of data through the internet, the usual everyday ingestion of data is mounting exponentially. With the continuous advancement in data storage and accessibility of smart devices, the impact of big data will continue to develop. This chapter provides the fundamental concepts of big data, its benefits, probable pitfalls, big data analytics and its impact in Bioinformatics. With the generation of the deluge of biological data through next generation sequencing projects, there is a need to handle this data trough big data techniques. The chapter also presents a discussion of the tools for analytics, development of a novel data life cycle on big data, details of the problems and challenges connected with big data with special relevance to bioinformatics.

Data Mining, Big Data, Data Analytics

2017 ◽  
pp. 91-111
Author(s):  
Priya P. Panigrahi ◽  
Tiratha Raj Singh
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Biological Data ◽  
Smart Devices ◽  
Data Life Cycle ◽  
The Impact ◽  
Special Relevance ◽  
Generation Sequencing

In this digital and computing world, data formation and collection rate are growing very rapidly. With these improved proficiencies of data storage and fast computation along with the real-time distribution of data through the internet, the usual everyday ingestion of data is mounting exponentially. With the continuous advancement in data storage and accessibility of smart devices, the impact of big data will continue to develop. This chapter provides the fundamental concepts of big data, its benefits, probable pitfalls, big data analytics and its impact in Bioinformatics. With the generation of the deluge of biological data through next generation sequencing projects, there is a need to handle this data trough big data techniques. The chapter also presents a discussion of the tools for analytics, development of a novel data life cycle on big data, details of the problems and challenges connected with big data with special relevance to bioinformatics.

Overview of Big Data and Its Visualization

2022 ◽  
pp. 22-53
Author(s):  
Richard S. Segall ◽  
Gao Niu
Keyword(s):  
Big Data ◽  
Traffic Safety ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Real Data ◽  
Flow Diagram ◽  
Data Sets ◽  
United States Department ◽  
Big Data Visualization ◽  
Challenges And Opportunities

Big Data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. This chapter discusses what Big Data is and its characteristics, and how this information revolution of Big Data is transforming our lives and the new technology and methodologies that have been developed to process data of these huge dimensionalities. This chapter discusses the components of the Big Data stack interface, categories of Big Data analytics software and platforms, descriptions of the top 20 Big Data analytics software. Big Data visualization techniques are discussed with real data from fatality analysis reporting system (FARS) managed by National Highway Traffic Safety Administration (NHTSA) of the United States Department of Transportation. Big Data web-based visualization software are discussed that are both JavaScript-based and user-interface-based. This chapter also discusses the challenges and opportunities of using Big Data and presents a flow diagram of the 30 chapters within this handbook.

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