scholarly journals BIG DATA ANALYSIS IN HEALTH CARE DOMAIN: A SYSTEMATIC REVIEW

2020 ◽  
Vol 5 (2) ◽  
pp. 1-8
Author(s):  
Abhishek Bajpai ◽  
Dr. Sanjiv Sharma
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Healthcare Data ◽  
Challenges And Opportunities ◽  
Business Profit

As the Volume of the data produced is increasing day by day in our society, the exploration of big data in healthcare is increasing at an unprecedented rate. Now days, Big data is very popular buzzword concept in the various areas. This paper provide an effort is made to established that even the healthcare industries are stepping into big data pool to take all advantages from its various advanced tools and technologies. This paper provides the review of various research disciplines made in health care realm using big data approaches and methodologies. Big data methodologies can be used for the healthcare data analytics (which consist 4 V’s) which provide the better decision to accelerate the business profit and customer affection, acquire a better understanding of market behaviours and trends and to provide E-Health services using Digital imaging and communication in Medicine (DICOM).Big data Techniques like Map Reduce, Machine learning can be applied to develop system for early diagnosis of disease, i.e. analysis of the chronic disease like- heart disease, diabetes and stroke. The analysis on the data is performed using big data analytics framework Hadoop. Hadoop framework is used to process large data sets Further the paper present the various Big data tools , challenges and opportunities and various hurdles followed by the conclusion.                                      

scholarly journals Big data analytics steps and tools used in analytical process

2017 ◽  
Vol 7 (1) ◽  
pp. 183-195
Author(s):  
Sasikala V
Keyword(s):  
Big Data ◽  
Customer Service ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Competitive Advantages ◽  
Customer Preferences ◽  
Market Trends

Big data analytics is the process of examining large data sets to uncover hidden patterns,unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits.

scholarly journals Deep Learning Security Systems

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1823-1826
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Security Systems ◽  
Abstract Knowledge

Big Data Analytics and Deep Learning are not supposed to be two entirely different concepts. Big Data means extremely huge large data sets that can be analyzed to find patterns, trends. One technique that can be used for data analysis so that able to help us find abstract patterns in Big Data is Deep Learning. If we apply Deep Learning to Big Data, we can find unknown and useful patterns that were impossible so far. With the help of Deep Learning, AI is getting smart. There is a hypothesis in this regard, the more data, the more abstract knowledge. So a handy survey of Big Data, Deep Learning and its application in Big Data is necessary.

Big Data Analytics and IoT in Smart City Applications

2022 ◽  
pp. 364-380
Author(s):  
Mamata Rath
Keyword(s):  
Big Data ◽  
Smart City ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Business Information ◽  
Data Querying ◽  
Data Elements

Big data analytics is a sophisticated approach for fusion of large data sets that include a collection of data elements to expose hidden prototype, undetected associations, showcase business logic, client inclinations, and other helpful business information. Big data analytics involves challenging techniques to mine and extract relevant data that includes the actions of penetrating a database, effectively mining the data, querying and inspecting data committed to enhance the technical execution of various task segments. The capacity to synthesize a lot of data can enable an association to manage impressive data that can influence the business.

Big Data Analytics and IoT in Smart City Applications

2021 ◽  
pp. 586-601
Author(s):  
Mamata Rath
Keyword(s):  
Big Data ◽  
Smart City ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Business Information ◽  
Data Querying ◽  
Data Elements

Big data analytics is a sophisticated approach for fusion of large data sets that include a collection of data elements to expose hidden prototype, undetected associations, showcase business logic, client inclinations, and other helpful business information. Big data analytics involves challenging techniques to mine and extract relevant data that includes the actions of penetrating a database, effectively mining the data, querying and inspecting data committed to enhance the technical execution of various task segments. The capacity to synthesize a lot of data can enable an association to manage impressive data that can influence the business.

scholarly journals An Analysis of Data Processing for Big Data Analytics

2021 ◽  
pp. 130-138
Author(s):  
Steve Blair ◽  
Jon Cotter
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
High Performance ◽  
Big Data Analytics ◽  
Large Data ◽  
Data Availability ◽  
Large Data Sets ◽  
Data Sets ◽  
Wide Range

The need for high-performance Data Mining (DM) algorithms is being driven by the exponentially increasing data availability such as images, audio and video from a variety of domains, including social networks and the Internet of Things (IoT). Deep learning is an emerging field of pattern recognition and Machine Learning (ML) study right now. It offers computer simulations of numerous nonlinear processing layers of neurons that may be used to learn and interpret data at higher degrees of abstractions. Deep learning models, which may be used in cloud technology and huge computational systems, can inherently capture complex structures of large data sets. Heterogeneousness is one of the most prominent characteristics of large data sets, and Heterogeneous Computing (HC) causes issues with system integration and Advanced Analytics. This article presents HC processing techniques, Big Data Analytics (BDA), large dataset instruments, and some classic ML and DM methodologies. The use of deep learning to Data Analytics is investigated. The benefits of integrating BDA, deep learning, HPC (High Performance Computing), and HC are highlighted. Data Analytics and coping with a wide range of data are discussed.

Real Time Analysis Based on Intelligent Applications of Big Data and IoT in Smart Health Care Systems

2020 ◽  
pp. 1839-1857
Author(s):  
Mamata Rath
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Analytics ◽  
Treatment Plan ◽  
Big Data Analytics ◽  
Data Sets ◽  
Care Services ◽  
Real Time Analysis ◽  
Health Care Applications ◽  
Care Systems

Currently, there is an expanding interest for additional medical data from patients about their healthcare choices and related decisions, and they further need investment in their basic health issues. Big data provides patients presumptuous data to help them settle on the best choice and align with their medicinal treatment plan. One of the very advanced concepts related to the synthesis of big data sets to reveal the hidden pattern in them is big data analytics. It involves demanding techniques to mine and extract relevant data that includes the actions of piercing a database, effectively mine the data, query and inspect the data and is committed to enhance the technical execution of various task segments. The capacity to synthesize a lot of data can enable an association to manage data that can influence the business. In this way, the primary goal of big data analytics is to help business relationships to have enhanced comprehension of data, and subsequently, settle on proficient and very much educated decisions. Big data analytics empowers data diggers and researchers to examine an extensive volume of data that may not be outfit utilizing customary apparatuses. Big data analytics require advances and statistical instruments that can change a lot of organized, unstructured, and semi-organized data into more reasonable data and metadata designed for explanatory procedures. There is tremendous positive potential concerning the application of big data in human health care services and many related major applications are still in their developmental stages. The deployment of big data in health service demonstrates enhancing health care results and controlling the expenses of common people due to treatment, as proven by some developing use cases. Keeping in view such powerful processing capacity of big data analytics in various technical fields of modern civilization related to health care, the current research article presents a comprehensive study and investigation on big data analytics and its application in multiple sectors of society with significance in health care applications.

Research Challenges in Big Data Analytics

2017 ◽  
pp. 83-99
Author(s):  
Sivamathi Chokkalingam ◽  
Vijayarani S.
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
New Technologies ◽  
Big Data Analytics ◽  
Large Data ◽  
Data Sets ◽  
Data Types ◽  
Customer Preferences ◽  
Research Challenges

The term Big Data refers to large-scale information management and analysis technologies that exceed the capability of traditional data processing technologies. Big Data is differentiated from traditional technologies in three ways: volume, velocity and variety of data. Big data analytics is the process of analyzing large data sets which contains a variety of data types to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. Since Big Data is new emerging field, there is a need for development of new technologies and algorithms for handling big data. The main objective of this paper is to provide knowledge about various research challenges of Big Data analytics. A brief overview of various types of Big Data analytics is discussed in this paper. For each analytics, the paper describes process steps and tools. A banking application is given for each analytics. Some of research challenges and possible solutions for those challenges of big data analytics are also discussed.

Modern Health Management With Cognitive Computing and Big Data Analytics

2019 ◽  
pp. 206-224
Author(s):  
Mamata Rath
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Health Management ◽  
Big Data Analytics ◽  
Large Data ◽  
Data Sets ◽  
Cognitive Computing ◽  
Business Information ◽  
Data Querying ◽  
Data Elements

Big data analytics is an refined advancement for fusion of large data sets that include a collection of data elements to expose hidden prototype, undetected associations, showcase business logic, client inclinations, and other helpful business information. Big data analytics involves challenging techniques to mine and extract relevant data that includes the actions of penetrating a database, effectively mining the data, querying and inspecting data committed to enhance the technical execution of various task segments. The capacity to synthesize a lot of data can enable an association to manage impressive data that can influence the business. In this way, the primary goal of big data analytics is to help business relationship to have enhanced comprehension of data and, subsequently, settle on proficient and educated decisions.

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.

scholarly journals Big Data in the Industry - Overview of Selected Issues

2017 ◽  
Vol 25 (4) ◽  
pp. 251-254 ◽  
Author(s):  
Sylwia Gierej
Keyword(s):  
Big Data ◽  
Mass Production ◽  
Data Analytics ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Manufacturing Companies ◽  
Professional Literature ◽  
Mass Data ◽  
Definition Of

AbstractThis article reviews selected issues related to the use of Big Data in the industry. The aim is to define the potential scope and forms of using large data sets in manufacturing companies. By systematically reviewing scientific and professional literature, selected issues related to the use of mass data analytics in production were analyzed. A definition of Big Data was presented, detailing its main attributes. The importance of mass data processing technology in the development of Industry 4.0 concept has been highlighted. Subsequently, attention was paid to issues such as production process optimization, decision making and mass production individualisation, and indicated the potential for large volumes of data. As a result, conclusions were drawn regarding the potential of using Big Data in the industry.

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