Insight Into Big Data Analytics

2018 ◽  
pp. 67-79
Author(s):  
Mohd Vasim Ahamad ◽  
Misbahul Haque ◽  
Mohd Imran
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Huge Amount ◽  
Business Decision ◽  
Digital Era ◽  
Data Formats ◽  
Recent Trends ◽  
Analytical Approaches

In the present digital era, more data are generated and collected than ever before. But, this huge amount of data is of no use until it is converted into some useful information. This huge amount of data, coming from a number of sources in various data formats and having more complexity, is called big data. To convert the big data into meaningful information, the authors use different analytical approaches. Information extracted, after applying big data analytics methods over big data, can be used in business decision making, fraud detection, healthcare services, education sector, machine learning, extreme personalization, etc. This chapter presents the basics of big data and big data analytics. Big data analysts face many challenges in storing, managing, and analyzing big data. This chapter provides details of challenges in all mentioned dimensions. Furthermore, recent trends of big data analytics and future directions for big data researchers are also described.

Big Data and Big Data Analytics for Improved Healthcare Service and Management

2020 ◽  
Vol 8 (1) ◽  
pp. 13-51
Author(s):  
Pijush Kanti Dutta Pramanik ◽  
Saurabh Pal ◽  
Moutan Mukhopadhyay
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Future Market ◽  
Data Generation ◽  
Related Data ◽  
Healthcare Data ◽  
Different Types

Like other fields, the healthcare sector has also been greatly impacted by big data. A huge volume of healthcare data and other related data are being continually generated from diverse sources. Tapping and analysing these data, suitably, would open up new avenues and opportunities for healthcare services. In view of that, this paper aims to present a systematic overview of big data and big data analytics, applicable to modern-day healthcare. Acknowledging the massive upsurge in healthcare data generation, various ‘V's, specific to healthcare big data, are identified. Different types of data analytics, applicable to healthcare, are discussed. Along with presenting the technological backbone of healthcare big data and analytics, the advantages and challenges of healthcare big data are meticulously explained. A brief report on the present and future market of healthcare big data and analytics is also presented. Besides, several applications and use cases are discussed with sufficient details.

scholarly journals IOT And Big Data Analytics In E-Learning: A Technological Perspective and Review

2018 ◽  
Vol 7 (1.8) ◽  
pp. 164 ◽  
Author(s):  
S Kusuma ◽  
D Kasi Viswanath
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Digital Culture ◽  
Educational Institutions ◽  
Learning Approaches ◽  
E Learning ◽  
Recent Trends ◽  
Conducting Research ◽  
The Internet Of Things

The internet of things & Big data analytics in eLearning brings tremendous challenges & opportunities to educational institutions & students. In recent trends, the growth of Pervasive computing, Social media, evolving IoT capabilities, technologies such as cloud computing, and big data and analytics are improving the core values of teaching and conducting research but also instilling a new digital culture and developing an IoT-centric society. The primary purpose of this paper is to provide an impact of IoT & Big data analytics in the area of E-learning and study on different E-learning approaches. 

Architecture of modern platforms for big data analytics

2021 ◽  
pp. 67-74
Author(s):  
Liudmyla Zubyk ◽  
Yaroslav Zubyk
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Business Processes ◽  
Service Providers ◽  
Big Data Analytics ◽  
Business Value ◽  
Third Party ◽  
Unstructured Data ◽  
Business Decision ◽  
World Industry

Big data is one of modern tools that have impacted the world industry a lot of. It also plays an important role in determining the ways in which businesses and organizations formulate their strategies and policies. However, very limited academic researches has been conducted into forecasting based on big data due to the difficulties in capturing, collecting, handling, and modeling of unstructured data, which is normally characterized by it’s confidential. We define big data in the context of ecosystem for future forecasting in business decision-making. It can be difficult for a single organization to possess all of the necessary capabilities to derive strategic business value from their findings. That’s why different organizations will build, and operate their own analytics ecosystems or tap into existing ones. An analytics ecosystem comprising a symbiosis of data, applications, platforms, talent, partnerships, and third-party service providers lets organizations be more agile and adapt to changing demands. Organizations participating in analytics ecosystems can examine, learn from, and influence not only their own business processes, but those of their partners. Architectures of popular platforms for forecasting based on big data are presented in this issue.

Big Data Predictive and Prescriptive Analytics

2014 ◽  
pp. 370-391 ◽  
Author(s):  
Ganesh Chandra Deka
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
Action Plan ◽  
Big Data Analytics ◽  
Clear Understanding ◽  
Future Planning ◽  
Prescriptive Analytics ◽  
Hybrid Data ◽  
Recent Trends

The Analytics tools are capable of suggesting the most favourable future planning by analyzing “Why” and “How” blended with What, Who, Where, and When. Descriptive, Predictive, and Prescriptive analytics are the analytics currently in use. Clear understanding of these three analytics will enable an organization to chalk out the most suitable action plan taking various probable outcomes into account. Currently, corporate are flooded with structured, semi-structured, unstructured, and hybrid data. Hence, the existing Business Intelligence (BI) practices are not sufficient to harness potentials of this sea of data. This change in requirements has made the cloud-based “Analytics as a Service (AaaS)” the ultimate choice. In this chapter, the recent trends in Predictive, Prescriptive, Big Data analytics, and some AaaS solutions are discussed.

Big Data Analytics in Aquaculture Using Hive and Hadoop Platform

2018 ◽  
pp. 29-35
Author(s):  
P. Venkateswara Rao ◽  
A. Ramamohan Reddy ◽  
V. Sucharita
Keyword(s):  
Big Data ◽  
Data Management ◽  
Single Machine ◽  
Data Analytics ◽  
Historical Data ◽  
Big Data Analytics ◽  
Huge Amount ◽  
Hadoop Platform ◽  
Different Sources

In the field of Aquaculture with the help of digital advancements huge amount of data is constantly produced for which the data of the aquaculture has entered in the big data world. The requirement for data management and analytics model is increased as the development progresses. Therefore, all the data cannot be stored on single machine. There is need for solution that stores and analyzes huge amounts of data which is nothing but Big Data. In this chapter a framework is developed that provides a solution for shrimp disease by using historical data based on Hive and Hadoop. The data regarding shrimps is acquired from different sources like aquaculture websites, various reports of laboratory etc. The noise is removed after the collection of data from various sources. Data is to be uploaded on HDFS after normalization is done and is to be put in a file that supports Hive. Finally classified data will be located in particular place. Based on the features extracted from aquaculture data, HiveQL can be used to analyze shrimp diseases symptoms.

Big Data Predictive and Prescriptive Analytics

Big Data ◽  
2016 ◽  
pp. 30-55 ◽  
Author(s):  
Ganesh Chandra Deka
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
Action Plan ◽  
Big Data Analytics ◽  
Clear Understanding ◽  
Future Planning ◽  
Prescriptive Analytics ◽  
Hybrid Data ◽  
Recent Trends

The Analytics tools are capable of suggesting the most favourable future planning by analyzing “Why” and “How” blended with What, Who, Where, and When. Descriptive, Predictive, and Prescriptive analytics are the analytics currently in use. Clear understanding of these three analytics will enable an organization to chalk out the most suitable action plan taking various probable outcomes into account. Currently, corporate are flooded with structured, semi-structured, unstructured, and hybrid data. Hence, the existing Business Intelligence (BI) practices are not sufficient to harness potentials of this sea of data. This change in requirements has made the cloud-based “Analytics as a Service (AaaS)” the ultimate choice. In this chapter, the recent trends in Predictive, Prescriptive, Big Data analytics, and some AaaS solutions are discussed.

scholarly journals A Big Data Analytics Approach for the Development of Advanced Cardiology Applications

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 60 ◽  
Author(s):  
Lorenzo Carnevale ◽  
Antonio Celesti ◽  
Maria Fazio ◽  
Massimo Villari
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Response Times ◽  
Distributed Processing ◽  
Big Data Analytics ◽  
Reliable System ◽  
Huge Amount ◽  
Ecg Signals ◽  
Big Data Applications ◽  
Electrocardiogram Ecg

Nowadays, we are observing a growing interest about Big Data applications in different healthcare sectors. One of this is definitely cardiology. In fact, electrocardiogram produces a huge amount of data about the heart health status that need to be stored and analysed in order to detect a possible issues. In this paper, we focus on the arrhythmia detection problem. Specifically, our objective is to address the problem of distributed processing considering big data generated by electrocardiogram (ECG) signals in order to carry out pre-processing analysis. Specifically, an algorithm for the identification of heartbeats and arrhythmias is proposed. Such an algorithm is designed in order to carry out distributed processing over the Cloud since big data could represent the bottleneck for cardiology applications. In particular, we implemented the Menard algorithm in Apache Spark in order to process big data coming form ECG signals in order to identify arrhythmias. Experiments conducted using a dataset provided by the Physionet.org European ST-T Database show an improvement in terms of response times. As highlighted by our outcomes, our solution provides a scalable and reliable system, which may address the challenges raised by big data in healthcare.

scholarly journals Decision Framework for Engaging Cloud-Based Big Data Analytics Vendors

2020 ◽  
Vol 22 (4) ◽  
pp. 60-74
Author(s):  
Emmanuel Wusuhon Yanibo Ayaburi ◽  
Michele Maasberg ◽  
Jaeung Lee
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Competitive Advantage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Unique Contribution ◽  
Data Services ◽  
Business Environments ◽  
Digital Era ◽  
Service Oriented

Organizations face both opportunities and risks with big data analytics vendors, and the risks are now profound, as data has been likened to the oil of the digital era. The growing body of research at the nexus of big data analytics and cloud computing is examined from the economic perspective, based on agency theory (AT). A conceptual framework is developed for analyzing these opportunities and challenges regarding the use of big data analytics and cloud computing in e-business environments. This framework allows organizations to engage in contracts that target competitive parity with their service-oriented decision support system (SODSS) to achieve a competitive advantage related to their core business model. A unique contribution of this paper is its perspective on how to engage a vendor contractually to achieve this competitive advantage. The framework provides insights for a manager in selecting a vendor for cloud-based big data services.

scholarly journals Proposed Usage of ICT and Big Data in Healthcare of Madhya Pradesh

2021 ◽  
Vol 9 (12) ◽  
pp. 2242-2248
Author(s):  
Mimoh Ojha
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Madhya Pradesh ◽  
Information And Communications Technology ◽  
Area Network ◽  
Wide Area Network ◽  
Healthcare Data ◽  
Health Application

Abstract: This paper gives an insight of how information and communications technology (ICT) in combination with big data analytics can help to improve healthcare services in Madhya Pradesh, which is a state in India having approximately 75 million populations. With ongoing projects like ‘Digital India’ which will allow computerization of hospitals and digitization of healthcare data. Digital India coupled with ICT, can play an indispensable role in providing effective healthcare services through e-health application like electronic health record, e-prescription, computerized physician order entry, telemedicine, mhealth along with the network like State wide area network (SWAN) and National health information network which will allow sharing of healthcare records across the network. Data stored through e-health application is of huge size having different formats which makes it difficult to perform analytics on it. But with big data analytics we can perform analytics on large voluminous healthcare data and useful result obtained from data analytics, patients can be given better and specific treatments. It will also help doctors to exchange their knowledge and treatment practices. This paper also illustrates a case study on M.Y. hospital located in Indore, Madhya Pradesh. Keywords: ICT, E-health, Digital India, SWAN, CUG, Big Data Analytics.

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