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.

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 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.

Importance of Big Data and Hadoop in E-Servicing

2018 ◽  
pp. 39-52
Author(s):  
Karthiga Shankar ◽  
Suganya R.
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Opinion Mining ◽  
Big Data Analytics ◽  
Online Information ◽  
Campaign Strategies ◽  
Security Issues ◽  
Customer Group ◽  
Big Data Applications ◽  
The Web

Consumers are spending more and more time on the web to search information and receive e-services. E-commerce, e-government, e-business, e-learning, e-science, etc. reflect the growing importance of the web in all aspects of our lives. Along with the tremendous growth of online information, the use of big data has become a vital force in growing revenues. Consumers are today shopping multiple products across multiple channels online. This transformation is substantial and many of the e-commerce companies have now turned to big data analytics for focused customer group targeting using opinion mining for evaluating campaign strategies and maintaining a competitive advantage, especially during the festive shopping season. So, the role of intelligent techniques in e-servicing is massive. This chapter focuses on the importance of big data (since there is a large volume of data online) and big data analytics in the field of e-servicing and explains the various applications, platforms to implement the big data applications, and security issues in the era of big data and e-servicing.

Big Data Applications in Healthcare

Big Data ◽  
2016 ◽  
pp. 1247-1259 ◽  
Author(s):  
Jayanthi Ranjan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Industry ◽  
Big Data Applications ◽  
Business Functions ◽  
Automated Algorithms ◽  
Almost All ◽  
Tools And Techniques ◽  
Research Centres

Big data is in every industry. It is being utilized in almost all business functions within these industries. Basically, it creates value by converting human decisions into transformed automated algorithms using various tools and techniques. In this chapter, the authors look towards big data analytics from the healthcare perspective. Healthcare involves the whole supply chain of industries from the pharmaceutical companies to the clinical research centres, from the hospitals to individual physicians, and anyone who is involved in the medical arena right from the supplier to the consumer (i.e. the patient). The authors explore the growth of big data analytics in the healthcare industry including its limitations and potential.

Importance of Big Data and Hadoop in E-Servicing

2022 ◽  
pp. 1634-1644
Author(s):  
Karthiga Shankar ◽  
Suganya R.
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Opinion Mining ◽  
Big Data Analytics ◽  
Online Information ◽  
Campaign Strategies ◽  
Security Issues ◽  
Customer Group ◽  
Big Data Applications ◽  
The Web

Consumers are spending more and more time on the web to search information and receive e-services. E-commerce, e-government, e-business, e-learning, e-science, etc. reflect the growing importance of the web in all aspects of our lives. Along with the tremendous growth of online information, the use of big data has become a vital force in growing revenues. Consumers are today shopping multiple products across multiple channels online. This transformation is substantial and many of the e-commerce companies have now turned to big data analytics for focused customer group targeting using opinion mining for evaluating campaign strategies and maintaining a competitive advantage, especially during the festive shopping season. So, the role of intelligent techniques in e-servicing is massive. This chapter focuses on the importance of big data (since there is a large volume of data online) and big data analytics in the field of e-servicing and explains the various applications, platforms to implement the big data applications, and security issues in the era of big data and e-servicing.

Novel Scalable Deep Learning Approaches for Big Data Analytics Applied to ECG Processing

2018 ◽  
Vol 9 (4) ◽  
pp. 33-51
Author(s):  
Rostom Mennour ◽  
Mohamed Batouche
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Learning Approaches ◽  
Ecg Signals ◽  
Research Areas ◽  
Gradient Descent Algorithm ◽  
Big Data Technologies ◽  
Active Research

Big data analytics and deep learning are nowadays two of the most active research areas in computer science. As the data is becoming bigger and bigger, deep learning has a very important role to play in data analytics, and big data technologies will give it huge opportunities for different sectors. Deep learning brings new challenges especially when it comes to large amounts of data, the volume of datasets has to be processed and managed, also data in various applications come in a streaming way and deep learning approaches have to deal with this kind of applications. In this paper, the authors propose two novel approaches for discriminative deep learning, namely LS-DSN, and StreamDSN that are inspired from the deep stacking network algorithm. Two versions of the gradient descent algorithm were used to train the proposed algorithms. The experiment results have shown that the algorithms gave satisfying accuracy results and scale well when the size of data increases. In addition, StreamDSN algorithm have been applied to classify beats of ECG signals and provided good promising results.

Big data analytics for proactive industrial decision support

atp magazin ◽  
2016 ◽  
Vol 58 (09) ◽  
pp. 62 ◽  
Author(s):  
Martin Atzmueller ◽  
Benjamin Klöpper ◽  
Hassan Al Mawla ◽  
Benjamin Jäschke ◽  
Martin Hollender ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Historical Data ◽  
Big Data Analytics ◽  
Lessons Learned ◽  
Support Functions ◽  
Big Data Applications ◽  
Big Data Technologies ◽  
Process Plants ◽  
Plant Data

Big data technologies offer new opportunities for analyzing historical data generated by process plants. The development of new types of operator support systems (OSS) which help the plant operators during operations and in dealing with critical situations is one of these possibilities. The project FEE has the objective to develop such support functions based on big data analytics of historical plant data. In this contribution, we share our first insights and lessons learned in the development of big data applications and outline the approaches and tools that we developed in the course of the project.

scholarly journals Survey: Big Data Analytics in Agricultural Products Logistics

2020 ◽  
pp. 14-16
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Agricultural Products ◽  
Agricultural Product ◽  
Quality And Safety ◽  
Huge Amount ◽  
Safety Issues ◽  
Food Quality And Safety ◽  
Overall Performance

Big Data Analytics is one of the most cutting edge technology in the world. Big Data Analytics will provide data management to store, process and analyze the huge amount of data. Agricultural is one of the domains that assuring big data analytics used to make a change in the field. With the consistent enhancements of the peoples living lifestyle, step by step peoples concentrate on the demand of perishable agricultural products. However, the periodic eruption of food quality and safety issues affected the concern of the end-users. To enrich the distribution performance of agricultural product logistics and to provide the freshness, quality, and safety of the agricultural products has become a thread of the current agricultural domain. Big Data Analytics in Agricultural Products Logistics has an essential prospect of optimizing the distribution path of products, prediction of product demands in the market, traceability of the products, analyzing customer feedbacks and increasing overall performance of logistics in agriculture. This paper investigates the key challenges, methods used, technologies used, algorithms for distribution of products and future of Big Data Analytics in agro-logistics.

Novel Scalable Deep Learning Approaches for Big Data Analytics Applied to ECG Processing

2020 ◽  
pp. 633-653
Author(s):  
Rostom Mennour ◽  
Mohamed Batouche
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Learning Approaches ◽  
Ecg Signals ◽  
Research Areas ◽  
Gradient Descent Algorithm ◽  
Big Data Technologies ◽  
Active Research

Big data analytics and deep learning are nowadays two of the most active research areas in computer science. As the data is becoming bigger and bigger, deep learning has a very important role to play in data analytics, and big data technologies will give it huge opportunities for different sectors. Deep learning brings new challenges especially when it comes to large amounts of data, the volume of datasets has to be processed and managed, also data in various applications come in a streaming way and deep learning approaches have to deal with this kind of applications. In this paper, the authors propose two novel approaches for discriminative deep learning, namely LS-DSN, and StreamDSN that are inspired from the deep stacking network algorithm. Two versions of the gradient descent algorithm were used to train the proposed algorithms. The experiment results have shown that the algorithms gave satisfying accuracy results and scale well when the size of data increases. In addition, StreamDSN algorithm have been applied to classify beats of ECG signals and provided good promising results.

scholarly journals Big Data Analytics in Healthcare

2020 ◽  
Vol 5 (1) ◽  
pp. 19-27
Author(s):  
Jaimin Navinchandra Undavia ◽  
Atul Manubhai Patel
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Heterogeneous Data ◽  
Simple Type ◽  
Healthcare Industry ◽  
Technological Advancement ◽  
Huge Amount ◽  
High Level ◽  
Almost All

The technological advancement has also opened up various ways to collect data through automatic mechanisms. One such mechanism collects a huge amount of data without any further maintenance or human interventions. The health industry sector has been confronted by the need to manage the big data being produced by various sources, which are well known for producing high volumes of heterogeneous data. High level of sophistication has been incorporated in almost all the industry, and healthcare is one of them. The article shows that the existence of huge amount of data in healthcare industry and the data generated in healthcare industry is neither homogeneous nor a simple type of data. Then the various sources and objectives of data are also highlighted and discussed. As data come from various sources, they must be versatile in nature in all aspects. So, rightly and meaningfully, big data analytics has penetrated the healthcare industry and its impact is also highlighted.

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