Research on Big Data Analysis Data Acquisition and Data Analysis

2021 ◽

pp. 308-344

Author(s):

Rohit Rastogi ◽

Devendra Kumar Chaturvedi ◽

Parul Singhal

Keyword(s):

Big Data ◽

Data Analysis ◽

Electronic Health Records ◽

Analysis Data ◽

Healthcare Systems ◽

Clinical Analysis ◽

Big Data Analysis ◽

Diagnostic Information ◽

Unstructured Data ◽

Type I

The Delhi and NCR healthcare systems are rapidly registering electronic health records and diagnostic information available electronically. Furthermore, clinical analysis is rapidly advancing, and large quantities of information are examined and new insights are part of the analysis of this technology experienced as big data. It provides tools for storing, managing, studying, and assimilating large amounts of robust, structured, and unstructured data generated by existing medical organizations. Recently, data analysis data have been used to help provide care. The present study aimed to analyse diabetes with the latest IoT and big data analysis techniques and its correlation with stress (TTH) on human health. The authors have tried to include age, gender, and insulin factor and its correlation with diabetes. Overall, in conclusion, TTH cases increasing with age in case of males and not following the pattern of diabetes variation with age, while in the case of females, TTH pattern variation is the same as diabetes (i.e., increasing trend up to age of 60 then decreasing).

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Big data analysis on the business process and management for the store layout and bundling sales

Business Process Management Journal ◽

10.1108/bpmj-01-2018-0027 ◽

2019 ◽

Vol 25 (7) ◽

pp. 1783-1801 ◽

Cited By ~ 1

Author(s):

Shu-hsien Liao ◽

Yi-Shan Tasi

Keyword(s):

Data Mining ◽

Big Data ◽

Data Analysis ◽

Business Process ◽

Process Management ◽

Business Processes ◽

Analysis Data ◽

Database System ◽

Big Data Analysis ◽

Content Type

Purpose In the retailing industry, database is the time and place where a retail transaction is completed. E-business processes are increasingly adopting databases that can obtain in-depth customers and sales knowledge with the big data analysis. The specific big data analysis on a database system allows a retailer designing and implementing business process management (BPM) to maximize profits, minimize costs and satisfy customers on a business model. Thus, the research of big data analysis on the BPM in the retailing is a critical issue. The paper aims to discuss this issue. Design/methodology/approach This paper develops a database, ER model, and uses cluster analysis, C&R tree and the a priori algorithm as approaches to illustrate big data analysis/data mining results for generating business intelligence and process management, which then obtain customer knowledge from the case firm’s database system. Findings Big data analysis/data mining results such as customer profiles, product/brand display classifications and product/brand sales associations can be used to propose alternatives to the case firm for store layout and bundling sales business process and management development. Originality/value This research paper is an example to develop the BPM of database model and big data/data mining based on insights from big data analysis applications for store layout and bundling sales in the retailing industry.

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Adaptive Data Acquisition Technology for Android System Based on Big Data Analysis

2020 International Conference on Advance in Ambient Computing and Intelligence (ICAACI) ◽

10.1109/icaaci50733.2020.00030 ◽

2020 ◽

Author(s):

Yingbing Cai

Keyword(s):

Big Data ◽

Data Analysis ◽

Data Acquisition ◽

Big Data Analysis ◽

Android System

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Challenges in Big Data Analysis

Encyclopedia of Information Science and Technology, Fifth Edition - Advances in Information Quality and Management ◽

10.4018/978-1-7998-3479-3.ch041 ◽

2021 ◽

pp. 577-585

Author(s):

M. Govindarajan

Keyword(s):

Big Data ◽

Data Analysis ◽

Measurement Errors ◽

Modern Society ◽

Analysis Data ◽

Big Data Analysis ◽

Small Scale ◽

Efficient Representation ◽

And Storage ◽

Scale Data

Big data brings new opportunities to modern society and challenges to data scientists. On one hand, big data holds great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of big data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. Prior to data analysis, data must be well constructed. However, considering the variety of datasets in big data, the efficient representation, access, and analysis of unstructured or semi-structured data are still challenging. Understanding the method by which data can be preprocessed is important to improve data quality and the analysis results. The purpose of this chapter is to highlight the big data challenges and also provide a brief description of each challenge.

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Necessity of big data analysis in construction management

Strategic Direction ◽

10.1108/sd-09-2018-0181 ◽

2019 ◽

Vol 35 (1) ◽

pp. 3-5 ◽

Cited By ~ 1

Author(s):

Mahdi Safa ◽

Lillian Hill

Keyword(s):

Big Data ◽

Data Analysis ◽

Construction Management ◽

Analysis Data ◽

Big Data Analysis ◽

Information Modeling ◽

Content Type ◽

Management Processes ◽

Building Information ◽

Practical Implications

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings Big data analysis in the world of construction management has become a necessity after the discovery of how much time, energy, and money can be preserved. By the investigation of big data in construction, the deduction was made that data analysis, data mining, and Building Information Modeling are the future for construction management. If properly applied to future construction management processes, big data analysis methodologies could eliminate mistakes, setbacks, and wasted resources for projects Practical implications The paper provides strategic insights and practical thinking that have influenced some of the world’s leading organizations. Originality/value The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

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