scholarly journals Analysis of Big Data Challenges and Different Analytical Methods

2021 ◽  
Vol 07 (03) ◽  
pp. 33-38
Author(s):  
Gurram Bhaskar ◽  
Motati Dinesh Reddy
Keyword(s):  
Big Data ◽  
Analytical Methods

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.

On the Unstructured Big Data Analytical Methods in Firms: Conceptual Model, Measurement, and Perception

Big Data ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 478-500
Author(s):  
Piotr Tarka ◽  
Elżbieta Jędrych
Keyword(s):  
Big Data ◽  
Conceptual Model ◽  
Analytical Methods ◽  
Model Measurement

The Double-Edged Sword of Big Data in Organizational and Management Research

2017 ◽  
Vol 21 (3) ◽  
pp. 548-591 ◽  
Author(s):  
Ramon Wenzel ◽  
Niels Van Quaquebeke
Keyword(s):  
Big Data ◽  
Analytical Methods ◽  
Policy Agenda ◽  
Management Research ◽  
Research Practices ◽  
Key Characteristics ◽  
Relevant Field

While many disciplines embrace the possibilities that Big Data present for advancing scholarship and practice, organizational and management research has yet to realize Big Data’s potential. In an effort to chart this newfound territory, we briefly describe the principal drivers and key characteristics of Big Data. We then review a broad range of opportunities and risks that are related to the Big Data paradigm, the data itself, and the associated analytical methods. For each, we provide research ideas and recommendations on how to embrace the potentials or address the concerns. Our assessment shows that Big Data, as a paradigm, can be a double- edged sword, capable of significantly advancing our field but also causing backlash if not utilized properly. Our review seeks to inform individual research practices as well as a broader policy agenda in order to advance organizational and management research as a scientifically rigorous and professionally relevant field.

scholarly journals Survey of Analytical Methods for Big Data of Quality Inspection

2018 ◽  
Vol 466 ◽  
pp. 012030
Author(s):  
Yingcheng Xu ◽  
Wei Jiang ◽  
Xiuli Ning ◽  
Bisong Liu ◽  
Ya Li
Keyword(s):  
Big Data ◽  
Analytical Methods ◽  
Quality Inspection

Descriptive and Predictive Analytical Methods for Big Data

2016 ◽  
pp. 66-83
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.

scholarly journals Critical analysis of Big Data challenges and analytical methods

2017 ◽  
Vol 70 ◽  
pp. 263-286 ◽  
Author(s):  
Uthayasankar Sivarajah ◽  
Muhammad Mustafa Kamal ◽  
Zahir Irani ◽  
Vishanth Weerakkody
Keyword(s):  
Big Data ◽  
Analytical Methods ◽  
Critical Analysis

scholarly journals Big Data and Cloud Computing

2019 ◽  
pp. 325-355
Author(s):  
Yun Li ◽  
Manzhu Yu ◽  
Mengchao Xu ◽  
Jingchao Yang ◽  
Dexuan Sha ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analysis ◽  
Data Processing ◽  
Analytical Methods ◽  
Time Frame ◽  
Data Sources ◽  
New Paradigm ◽  
Digital Earth ◽  
Computing Support

Abstract Big data emerged as a new paradigm to provide unprecedented content and value for Digital Earth. Big Earth data are increasing tremendously with growing heterogeneity, posing grand challenges for the data management lifecycle of storage, processing, analytics, visualization, sharing, and applications. During the same time frame, cloud computing emerged to provide crucial computing support to address these challenges. This chapter introduces Digital Earth data sources, analytical methods, and architecture for data analysis and describes how cloud computing supports big data processing in the context of Digital Earth.

Stereo modeling of HVEM specimens by serial tilting and computer graphics

1986 ◽  
Vol 44 ◽  
pp. 34-35
Author(s):  
J.R. McIntosh ◽  
D.L. Stemple ◽  
William Bishop ◽  
G.W. Hannaway
Keyword(s):  
Computer Graphics ◽  
Analytical Methods ◽  
Photographic Film ◽  
Complex Structures ◽  
Multiple Objects ◽  
Multiple Images ◽  
3 Dimensional

EM specimens often contain 3-dimensional information that is lost during micrography on a single photographic film. Two images of one specimen at appropriate orientations give a stereo view, but complex structures composed of multiple objects of graded density that superimpose in each projection are often difficult to decipher in stereo. Several analytical methods for 3-D reconstruction from multiple images of a serially tilted specimen are available, but they are all time-consuming and computationally intense.

Find Out About 'Big Data' to Track Outcomes

ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  
Big Data

Find Out About 'Big Data' to Track Outcomes

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