Big Data Analytics for Intelligent Healthcare Management

Nilanjan Dey, Himansu Das, Bighnaraj Naik, H S Behera

• Publisher: Academic Press
• Year: 2019
• ISBN:
  9780128181478
• Topic: Biological Sciences
• Subtopic: Biotechnology
• Index: Biological Sciences

Big Data Analytics for Intelligent Healthcare Management covers both the theory and application of hardware platforms and architectures, the development of software methods, techniques and tools, applications and governance, and adoption strategies for the use of big data in healthcare and clinical research. The book provides the latest research findings on the use of big data analytics with statistical and machine learning techniques that analyze huge amounts of real-time healthcare data.

• Examines the methodology and requirements for development of big data architecture, big data modeling, big data as a service, big data analytics, and more
• Discusses big data applications for intelligent healthcare management, such as revenue management and pricing, predictive analytics/forecasting, big data integration for medical data, algorithms and techniques, etc.
• Covers the development of big data tools, such as data, web and text mining, data mining, optimization, machine learning, cloud in big data with Hadoop, big data in IoT, and more

Chapter 1

Bio-Inspired Algorithms for Big Data Analytics: A Survey, Taxonomy, and Open Challenges

Sukhpal Singh Gill; Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Parkville, VIC, Australia

Abstract

Presently, various governments and organizations are focusing on digitization of technical and academic documents, which overloads digital libraries. It is challenging to manage a massive amount of data (big data) with the current data processing techniques. In literature, bio-inspired algorithm-based models and architectures have been introduced by numerous industry and academic institutions to facilitate data analytics for big data. This chapter presents a systematic review of bio-inspired algorithms for big data analytics. The current status of bio-inspired algorithms is categorized into three categories: ecological, swarm-based, and evolutionary. This chapter compares the existing models and architectures, explores the current trends, and recognizes the existing open issues in the development of big data analytical techniques. This research work will also help to choose the most appropriate bio-inspired algorithm for big data analytics in a specific type of data along with promising directions for future research.

Keywords

Big data; Data management; Bio-inspired optimization; Big data analytics; Cloud computing

Acknowledgments

One of the authors, Dr. Sukhpal Singh Gill [Postdoctoral Research Fellow], gratefully acknowledges the Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Australia, for awarding him the Fellowship to carry out this research work. This research work is supported by Discovery Project of Australian Research Council (ARC), Grant/Award Number: DP160102414.

1.1 Introduction

Cloud computing is now the spine of the modern economy, which offers on-demand services to cloud customers through the Internet. To improve the performance and effectiveness of cloud computing systems, new technologies, such as internet of things (IoT) applications (healthcare services, smart cities etc.) and big data, are emerging, which further requires effective data processing to process data [1]. However, there are two problems in existing big data processing approaches, which degrade the performance of computing systems such as large response time and delay due to data being transferred twice [2]: (1) computing systems to cloud and (2) cloud to IoT applications. Presently, IoT devices collect data with a huge amount of volume (big data) and variety and these systems are growing with the velocity of 500 MB/seconds or more [3].

For IoT based smart cities, the transfer of data is used to make effective decisions for big data analytics. Data is stored and processed on cloud servers after collection and aggregation of data from smart devices on IoT networks. Further, to process the large volume of data, there is a need for automatic highly scalable cloud technology, which can further improve the performance of the systems [4]. Literature reported that existing cloud-based data processing systems are not able to satisfy the performance requirements of IoT applications when a low response time and latency is needed. Moreover, other reasons for a large response time and latency are: geographical distribution of data and communication failures during transfer of data [5]. Cloud computing systems become bottlenecked due to continually receiving raw data from IoT devices [6]. Therefore, a bio-inspired algorithm based big data analytics is an alternative paradigm that provides a platform between computing systems and IoT devices to process user data in an efficient manner [7].

1.1.1 Dimensions of Data Management

As identified from existing literature [1–6], there are five kinds of dimensions of data, which are required for effective management. Fig. 1.1 shows the dimensions of data management for big data analytics: (1) volume, (2) variety, (3) velocity, (4) veracity, and (5) variability.

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Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Contributors
• Preface
• Acknowledgments
• Chapter 1: Bio-Inspired Algorithms for Big Data Analytics: A Survey, Taxonomy, and Open Challenges
• Chapter 2: Big Data Analytics Challenges and Solutions
• Chapter 3: Big Data Analytics in Healthcare: A Critical Analysis
• Chapter 4: Transfer Learning and Supervised Classifier Based Prediction Model for Breast Cancer
• Chapter 5: Chronic TTH Analysis by EMG and GSR Biofeedback on Various Modes and Various Medical Symptoms Using IoT
• Chapter 6: Multilevel Classification Framework of fMRI Data: A Big Data Approach
• Chapter 7: Smart Healthcare: An Approach for Ubiquitous Healthcare Management Using IoT
• Chapter 8: Blockchain in Healthcare: Challenges and Solutions
• Chapter 9: Intelligence-Based Health Recommendation System Using Big Data Analytics
• Chapter 10: Computational Biology Approach in Management of Big Data of Healthcare Sector
• Chapter 11: Kidney-Inspired Algorithm and Fuzzy Clustering for Biomedical Data Analysis
• Index