Efforts toward Research and Development on Inconsistencies and Analytical Tools of Big Data

2015 ◽  
Author(s):  
Ravindra Kumar Yadav ◽  
Khan Atiya Naaz
Keyword(s):  
Big Data ◽  
Research And Development ◽  
Analytical Tools

Big Data Analytics Tools and Platform in Big Data Landscape

2018 ◽  
pp. 80-89
Author(s):  
Mohd Imran ◽  
Mohd Vasim Ahamad ◽  
Misbahul Haque ◽  
Mohd Shoaib
Keyword(s):  
Big Data ◽  
Comparative Analysis ◽  
Comparative Study ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Apache Hadoop ◽  
Pros And Cons ◽  
The Comparative Study ◽  
Analytical Tools

The term big data analytics refers to mining and analyzing of the voluminous amount of data in big data by using various tools and platforms. Some of the popular tools are Apache Hadoop, Apache Spark, HBase, Storm, Grid Gain, HPCC, Casandra, Pig, Hive, and No SQL, etc. These tools are used depending on the parameter taken for big data analysis. So, we need a comparative analysis of such analytical tools to choose best and simpler way of analysis to gain more optimal throughput and efficient mining. This chapter contributes to a comparative study of big data analytics tools based on different aspects such as their functionality, pros, and cons based on characteristics that can be used to determine the best and most efficient among them. Through the comparative study, people are capable of using such tools in a more efficient way.

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.

Process Analytics Model Approach in Healthcare

2020 ◽  
pp. 186-196
Author(s):  
S. Santhosh Kumar ◽  
A. Sumathi
Keyword(s):  
Big Data ◽  
Research And Development ◽  
Process Model ◽  
Data Repository ◽  
Healthcare Analytics ◽  
Process Analytics ◽  
Healthcare Data ◽  
Innovative Solutions ◽  
Treatment Mechanisms ◽  
The Relationship

Process analytics involves the relationship between the doctor, diagnostic centers and patient. The primary advantages of using process analytics in healthcare are expert guidance, global medical assistance, and possible alternate treatment mechanisms. The secondary advantages are the analysis of the same type of disease complications and the creation of a disease-based healthcare data repository. This chapter focuses on the process model-based approach for healthcare analytics. The two emerging techniques Big data and IoT are needed to be incorporated with the process model for storing and analyzing the healthcare data. The first category assists administrators with identifying areas to streamline operations and concretely increase savings. Research and development are crucial aspects of healthcare, providing new innovative solutions and treatments that can be properly tracked, measured, and analyzed.

Big Data Analytics in Healthcare

2021 ◽  
pp. 85-98
Author(s):  
Sam Goundar ◽  
Karpagam Masilamani ◽  
Akashdeep Bhardwaj ◽  
Chandramohan Dhasarathan
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Health Data ◽  
Massive Data ◽  
Healthcare Industry ◽  
Health Records ◽  
Healthcare Data ◽  
Data Formats ◽  
Analytical Tools ◽  
Different Sources

This chapter provides better understanding and use-cases of big data in healthcare. The healthcare industry generates lot of data every day, and without proper analytical tools, it is quite difficult to extract meaningful data. It is essential to understand big data tools since the traditional devices don't maintain this vast data, and big data solves the major issue in handling massive healthcare data. Health data from numerous health records are collected from various sources, and this massive data is put together to form the big data. Conventional database cannot be used in this purpose due to the diversity in data formats, so it is difficult to merge, and so it is quite impossible to process. With the use of big data this problem is solved, and it can process highly variable data from different sources.

Big Data Analytics Tools and Platform in Big Data Landscape

2022 ◽  
pp. 622-631
Author(s):  
Mohd Imran ◽  
Mohd Vasim Ahamad ◽  
Misbahul Haque ◽  
Mohd Shoaib
Keyword(s):  
Big Data ◽  
Comparative Analysis ◽  
Comparative Study ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Apache Hadoop ◽  
Pros And Cons ◽  
The Comparative Study ◽  
Analytical Tools

The term big data analytics refers to mining and analyzing of the voluminous amount of data in big data by using various tools and platforms. Some of the popular tools are Apache Hadoop, Apache Spark, HBase, Storm, Grid Gain, HPCC, Casandra, Pig, Hive, and No SQL, etc. These tools are used depending on the parameter taken for big data analysis. So, we need a comparative analysis of such analytical tools to choose best and simpler way of analysis to gain more optimal throughput and efficient mining. This chapter contributes to a comparative study of big data analytics tools based on different aspects such as their functionality, pros, and cons based on characteristics that can be used to determine the best and most efficient among them. Through the comparative study, people are capable of using such tools in a more efficient way.

The Nature of Intelligent Analytics

2021 ◽  
pp. 1-21
Author(s):  
Zhaohao Sun ◽  
Andrew Stranieri
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Research And Development ◽  
Business Intelligence ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Business Analytics ◽  
Novel Approach ◽  
Advanced Analytics

Intelligent analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores the nature of intelligent analytics. More specifically, this chapter identifies the foundations, cores, and applications of intelligent big data analytics based on the investigation into the state-of-the-art scholars' publications and market analysis of advanced analytics. Then it presents a workflow-based approach to big data analytics and technological foundations for intelligent big data analytics through examining intelligent big data analytics as an integration of AI and big data analytics. The chapter also presents a novel approach to extend intelligent big data analytics to intelligent analytics. The proposed approach in this chapter might facilitate research and development of intelligent analytics, big data analytics, business analytics, business intelligence, AI, and data science.

Predictive Analytics

2015 ◽  
pp. 12-29 ◽  
Author(s):  
Sema A. Kalaian ◽  
Rafa M. Kasim
Keyword(s):  
Logistic Regression ◽  
Big Data ◽  
Multilevel Modeling ◽  
Polynomial Regression ◽  
Predictive Analytics ◽  
Intelligent Information ◽  
Complex Relationships ◽  
Analytical Tools ◽  
Existing Data ◽  
Organizational Performances

Predictive analytics and modeling are analytical tools for knowledge discovery through examining and capturing the complex relationships and patterns among the variables in the existing data in efforts to predict the future organizational performances. Their uses become more common place due largely to collecting massive amount of data, which is referred to as “big data,” and the increased need to transform large amounts of data into intelligent information (knowledge) such as trends, patterns, and relationships. The intelligent information can then be used to make smart and informed data-based decisions and predictions using various methods of predictive analytics. The main purpose of this chapter is to present a conceptual and practical overview of some of the basic and advanced analytical tools of predictive analytics. The chapter provides a detailed coverage of some of the predictive analytics tools such as Simple and Multiple-Regression, Polynomial Regression, Logistic Regression, Discriminant Analysis, and Multilevel Modeling.

Predictive Analytics

2017 ◽  
pp. 49-66
Author(s):  
Sema A. Kalaian ◽  
Rafa M. Kasim
Keyword(s):  
Logistic Regression ◽  
Big Data ◽  
Multilevel Modeling ◽  
Polynomial Regression ◽  
Predictive Analytics ◽  
Intelligent Information ◽  
Complex Relationships ◽  
Analytical Tools ◽  
Existing Data ◽  
Organizational Performances

Predictive analytics and modeling are analytical tools for knowledge discovery through examining and capturing the complex relationships and patterns among the variables in the existing data in efforts to predict the future organizational performances. Their uses become more common place due largely to collecting massive amount of data, which is referred to as “big data,” and the increased need to transform large amounts of data into intelligent information (knowledge) such as trends, patterns, and relationships. The intelligent information can then be used to make smart and informed data-based decisions and predictions using various methods of predictive analytics. The main purpose of this chapter is to present a conceptual and practical overview of some of the basic and advanced analytical tools of predictive analytics. The chapter provides a detailed coverage of some of the predictive analytics tools such as Simple and Multiple-Regression, Polynomial Regression, Logistic Regression, Discriminant Analysis, and Multilevel Modeling.

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