Springer Tracts in Nature-Inspired Computing

Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing

Editors: Fong, Simon James, Millham, Richard C (Eds.)

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  • Provides insights into recently developed bio-inspired algorithms 
  • Presents the evaluation of traditional algorithms, both sequential and parallel, for use in data mining 
  • Includes the latest work from researchers and experts in the field
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About this book

This book aims to provide some insights into recently developed bio-inspired algorithms within recent emerging trends of fog computing, sentiment analysis, and data streaming as well as to provide a more comprehensive approach to the big data management from pre-processing to analytics to visualization phases. The subject area of this book is within the realm of computer science, notably algorithms (meta-heuristic and, more particularly, bio-inspired algorithms). Although application domains of these new algorithms may be mentioned, the scope of this book is not on the application of algorithms to specific or general domains but to provide an update on recent research trends for bio-inspired algorithms within a specific application domain or emerging area. These areas include data streaming, fog computing, and phases of big data management. One of the reasons for writing this book is that the bio-inspired approach does not receive much attention but shows considerable promise and diversity in terms of approach of many issues in big data and streaming. Some novel approaches of this book are the use of these algorithms to all phases of data management (not just a particular phase such as data mining or business intelligence as many books focus on); effective demonstration of the effectiveness of a selected algorithm within a chapter against comparative algorithms using the experimental method. Another novel approach is a brief overview and evaluation of traditional algorithms, both sequential and parallel, for use in data mining, in order to provide an overview of existing algorithms in use. This overview complements a further chapter on bio-inspired algorithms for data mining to enable readers to make a more suitable choice of algorithm for data mining within a particular context. In all chapters, references for further reading are provided, and in selected chapters, the author also include ideas for future research. 

 

About the authors

Simon Fong graduated from La Trobe University, Australia, with a First-Class Honours B.E. Computer Systems degree and a Ph.D. Computer Science degree in 1993 and 1998, respectively. Simon is now working as an Associate Professor at the Computer and Information Science Department of the University of Macau. He is a Co-Founder of the Data Analytics and Collaborative Computing Research Group in the Faculty of Science and Technology. Prior to his academic career, Simon took up various managerial and technical posts, such as Systems Engineer, IT Consultant, and E-commerce Director in Australia and Asia. Dr. Fong has published over 500 international conference and peer-reviewed journal papers, mostly in the areas of data mining, data stream mining, big data analytics, meta-heuristics optimization algorithms, and their applications. He serves on the editorial boards of the Journal of Network and Computer Applications of Elsevier, IEEE IT Professional Magazine, and various special issues of SCIE-indexed journals. Currently, Simon is chairing a SIG, namely Blockchain for e-Health at IEEE Communication Society. 

Richard Millham a B.A. (Hons.) from the University of Saskatchewan in Canada, M.Sc. from the University of Abertay in Dundee, Scotland, and a Ph.D. from De Montfort University in Leicester, England. After working in industry in diverse fields for 15 years, he joined academe and he has taught in Scotland, Ghana, South Sudan, and the Bahamas before joining DUT. His research interests include software and data evolution, cloud computing, big data, bio-inspired algorithms, and aspects of IOT. 

Table of contents (12 chapters)

Table of contents (12 chapters)
  • The Big Data Approach Using Bio-Inspired Algorithms: Data Imputation

    Pages 1-19

    Millham, Richard (et al.)

  • Parameter Tuning onto Recurrent Neural Network and Long Short-Term Memory (RNN-LSTM) Network for Feature Selection in Classification of High-Dimensional Bioinformatics Datasets

    Pages 21-42

    Millham, Richard (et al.)

  • Data Stream Mining in Fog Computing Environment with Feature Selection Using Ensemble of Swarm Search Algorithms

    Pages 43-65

    Fong, Simon (et al.)

  • Pattern Mining Algorithms

    Pages 67-80

    Millham, Richard (et al.)

  • Extracting Association Rules: Meta-Heuristic and Closeness Preference Approach

    Pages 81-95

    Millham, Richard (et al.)

Buy this book

eBook 117,69 €
price for Spain (gross)
  • ISBN 978-981-15-6695-0
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase Institutional customers should get in touch with their account manager
Hardcover 145,59 €
price for Spain (gross)
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Bibliographic Information

Bibliographic Information
Book Title
Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing
Editors
  • Simon James Fong
  • Richard C Millham
Series Title
Springer Tracts in Nature-Inspired Computing
Copyright
2021
Publisher
Springer Singapore
Copyright Holder
The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
eBook ISBN
978-981-15-6695-0
DOI
10.1007/978-981-15-6695-0
Hardcover ISBN
978-981-15-6694-3
Series ISSN
2524-552X
Edition Number
1
Number of Pages
IX, 226
Number of Illustrations
8 b/w illustrations, 41 illustrations in colour
Topics