scholarly journals An Overview of Milestones of Big Data Analytics in Clinical and Medical Analysis

2021 ◽  
Vol 10 (5) ◽  
pp. 416-421
Author(s):  
Manu M R ◽  
B Balamurugan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Large Data ◽  
Human Interaction ◽  
Time Data ◽  
Effective Decision ◽  
Hadoop Cluster ◽  
And Behavior ◽  
Medical Analysis

The technological advancements make changes during availability of knowledge in a huge way. As the volume of data is increasing exponentially, there is a need for better management of data to research and industry. This data, referred to as Big Data, is now employed by various organizations to extract valuable information which may reanalyzed computationally to reveal patterns, trends and associations revealing the human interaction and behavior for making various industrial decisions But the data must be optimized, integrated, secured and visualized to make any effective decision. Analyzing of the large volume of data is not beneficial always unless it is analyzed properly. The existing techniques are insufficient to analyze the large Data and identify the frequent services accessed by the cloud users. Various services can be integrated to provide a better environment to work in emergency cases pretty earlier. Using these services, people become widely vulnerable to exposure. The data is large and provides an insight in to future predictions, which could definitely prevent maximum medical cases from happening. But without big data analytics techniques and therefore the Hadoop cluster, this data remains useless. Through this paper, we'll explain how real time data may be useful to research and predict severe

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.

scholarly journals A Survey on Big Data Analytics Using HADOOP

2019 ◽  
Vol 8 (S3) ◽  
pp. 35-40
Author(s):  
S. Mamatha ◽  
T. Sudha
Keyword(s):  
Big Data ◽  
Social Networking Sites ◽  
Data Analytics ◽  
Business Processes ◽  
Big Data Analytics ◽  
Large Data ◽  
Structured Data ◽  
Map Reduce ◽  
Data Set ◽  
Digital World

In this digital world, as organizations are evolving rapidly with data centric asset the explosion of data and size of the databases have been growing exponentially. Data is generated from different sources like business processes, transactions, social networking sites, web servers, etc. and remains in structured as well as unstructured form. The term ― Big data is used for large data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Big data varies in size ranging from a few dozen terabytes to many petabytes of data in a single data set. Difficulties include capture, storage, search, sharing, analytics and visualizing. Big data is available in structured, unstructured and semi-structured data format. Relational database fails to store this multi-structured data. Apache Hadoop is efficient, robust, reliable and scalable framework to store, process, transforms and extracts big data. Hadoop framework is open source and fee software which is available at Apache Software Foundation. In this paper we will present Hadoop, HDFS, Map Reduce and c-means big data algorithm to minimize efforts of big data analysis using Map Reduce code. The objective of this paper is to summarize the state-of-the-art efforts in clinical big data analytics and highlight what might be needed to enhance the outcomes of clinical big data analytics tools and related fields.

scholarly journals Urban Planning and Smart City Decision Management Empowered by Real-Time Data Processing Using Big Data Analytics

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2994 ◽  
Author(s):  
Bhagya Silva ◽  
Murad Khan ◽  
Changsu Jung ◽  
Jihun Seo ◽  
Diyan Muhammad ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Smart City ◽  
Data Analytics ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Time Data ◽  
Real Time Data

The Internet of Things (IoT), inspired by the tremendous growth of connected heterogeneous devices, has pioneered the notion of smart city. Various components, i.e., smart transportation, smart community, smart healthcare, smart grid, etc. which are integrated within smart city architecture aims to enrich the quality of life (QoL) of urban citizens. However, real-time processing requirements and exponential data growth withhold smart city realization. Therefore, herein we propose a Big Data analytics (BDA)-embedded experimental architecture for smart cities. Two major aspects are served by the BDA-embedded smart city. Firstly, it facilitates exploitation of urban Big Data (UBD) in planning, designing, and maintaining smart cities. Secondly, it occupies BDA to manage and process voluminous UBD to enhance the quality of urban services. Three tiers of the proposed architecture are liable for data aggregation, real-time data management, and service provisioning. Moreover, offline and online data processing tasks are further expedited by integrating data normalizing and data filtering techniques to the proposed work. By analyzing authenticated datasets, we obtained the threshold values required for urban planning and city operation management. Performance metrics in terms of online and offline data processing for the proposed dual-node Hadoop cluster is obtained using aforementioned authentic datasets. Throughput and processing time analysis performed with regard to existing works guarantee the performance superiority of the proposed work. Hence, we can claim the applicability and reliability of implementing proposed BDA-embedded smart city architecture in the real world.

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.

Big Data Analytics in Intra-Data Center Networks and Components of Data Mining

2016 ◽  
pp. 82-89
Author(s):  
Pushpa Mannava
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Center ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Large Data ◽  
Data Center Networks ◽  
Advanced Analytics ◽  
Scalable Design ◽  
Data Collections

Data mining is considered as a vital procedure as it is used for locating brand-new, legitimate, useful as well as reasonable kinds of data. The assimilation of data mining methods in cloud computing gives a versatile and also scalable design that can be made use of for reliable mining of significant quantity of data from virtually incorporated data resources with the goal of creating beneficial information which is useful in decision making. The procedure of removing concealed, beneficial patterns, as well as useful info from big data is called big data analytics. This is done via using advanced analytics techniques on large data collections. This paper provides the information about big data analytics in intra-data center networks, components of data mining and also techniques of Data mining.

scholarly journals Infrastructure for Time Critical Applications of Big Data Systems Using Light Weight YARN Architecture

2021 ◽  
Author(s):  
Saravanan A.M. ◽  
K. Loheswaran ◽  
G. Naga Rama Devi ◽  
Karuppathal R ◽  
C Balakrishnan ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Systems ◽  
Computational Framework ◽  
Internet Resources ◽  
Big Data Systems ◽  
Hadoop Cluster ◽  
Almost All ◽  
Time Critical

Abstract Increasing of humanity and development of Internet resources, storage size is growing with each day, whereby digital records are accessible in clouds of an exploratory format. The immediate future of Big Data is coming shortly for almost all other sectors. Big data can aid in the metamorphosis of significant company operations by offering a recommended and reliable overview of available data. Big data has also figured prominently in the detection of violence. Present framework for designing Big data implementations is capable of processing vast quantities of data through Big data analytics using collections of computing devices together to execute complex processing. Furthermore, existing technologies have not been built to fulfil the specifications of time-critical application areas and are far more oriented on real applications than on time-critical ones. This paper proposes the lightweight architecture called Yet Another Resource Negotiator (YARN), which focuses on the concept of a time-critical big-data system from the perspective of specifications and analyses the essential principles of several common big-data implementations. YARN as the normal computational framework to help MapReduce and another application instances within that Hadoop cluster. YARN requires multiple programs to execute concurrently on a constitutive common server and assent programs to delegate services depending on need. The final evaluation is accompanied by problems stemming from infrastructure and services that serve applications, recommend frameworkand provide preliminary efficiency behaviours that often contribute system impacts to implementation reliability.

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 in Healthcare using Machine Learning Algorithms: A Comparative Study

2020 ◽  
Vol 16 (13) ◽  
pp. 19
Author(s):  
Sai Hanuman Akundi ◽  
Soujanya R ◽  
Madhuri PM
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Comparative Study ◽  
Data Analytics ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
Large Data ◽  
Heterogeneous Data ◽  
Machine Learning Algorithms ◽  
Healthcare Sector

In recent years vast quantities of data have been managed in various ways of medical applications and multiple organizations worldwide have developed this type of data and, together, these heterogeneous data are called big data. Data with other characteristics, quantity, speed and variety are the word big data. The healthcare sector has faced the need to handle the large data from different sources, renowned for generating large amounts of heterogeneous data. We can use the Big Data analysis to make proper decision in the health system by tweaking some of the current machine learning algorithms. If we have a large amount of knowledge that we want to predict or identify patterns, master learning would be the way forward. In this article, a brief overview of the Big Data, functionality and ways of Big data analytics are presented, which play an important role and affect healthcare information technology significantly. Within this paper we have presented a comparative study of algorithms for machine learning. We need to make effective use of all the current machine learning algorithms to anticipate accurate outcomes in the world of nursing.

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 Automation of Big Data Analytics Using Robotic Process Automation

2021 ◽  
pp. 602-605
Author(s):  
G. Malini
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Business Processes ◽  
Big Data Analytics ◽  
Human Interaction ◽  
Process Automation ◽  
Raw Data ◽  
Robotic Process Automation ◽  
Manual Intervention ◽  
Almost All

Robotic Process Automation (RPA) is now becomes a buzzword and makes it mark on almost all fields in assisting automation of repetitive human intensive tasks in a simpler manner. RPA is nothing but a software solution that mimics the human interaction with computing software and applications without manual intervention. RPA has already been adapted in almost every business processes which are repetitive. As we are in the age of information the need for retrieval of patterns from raw data is increasing unimaginably so the needs for effective tools are also in a greater need. The effectiveness of RPA can be incorporated into the ever growing data analytics to automate the process of finding patterns and predictions from big data.

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