Bio-inspired algorithms for data streaming and visualization, big data management, and fog computing / Simon James Fong, Richard C. Millham, editors.
Fong, Simon, editor.; Millham, Richard C., editor.
2021
QA76.9.N37 B56 2021
Available Online

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Title Bio-inspired algorithms for data streaming and visualization, big data management, and fog computing / Simon James Fong, Richard C. Millham, editors.
ISBN 9789811566950 (electronic book)
981156695X (electronic book)
9789811566967 (print)
9811566968
9789811566974 (print)
9811566976
9811566941
9789811566943
DOI https://doi.org/10.1007/978-981-15-6695-0
Published Singapore : Springer, [2021]
Language English
Description 1 online resource
Item Number 10.1007/978-981-15-6695-0 doi
10.1007/978-981-15-6
Call Number QA76.9.N37 B56 2021
Dewey Decimal Classification 006.3
Summary 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.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from digital title page (viewed on October 07, 2020).
Added Author Fong, Simon, editor.
Millham, Richard C., editor.
Series Springer tracts in nature-inspired computing.
Available in Other Form Bio-inspired algorithms for data streaming and visualization, big data management, and fog computing.
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Chapter 1. The Big Data Approach Using Bio-Inspired Algorithms: Data Imputation
Chapter 2. Parameter Tuning onto Recurrent Neural Network and Long Short Term Memory (RNN-LSTM) Network for Feature Selection in Classification of High-dimensional Bioinformatics Datasets
Chapter 3. Data Stream Mining in Fog Computing Environment with Feature Selection Using Ensemble of Swarm Search Algorithms
Chapter 4. Pattern Mining Algorithms
Chapter 5. Extracting Association Rules: Meta-Heuristic and Closeness Preference Approach
Chapter 6. Lightweight Classifier-based Outlier Detection Algorithms from Multivariate Data Stream
Chapter 7. Comparison of Contemporary Meta-heuristic Algorithms for Solving Economic Load Dispatch Problem
Chapter 8. The paradigm on fog computing with bio-inspired search methods and the '5Vs of big data
Chapter 9. Approach for sentiment analysis on social media sites
Chapter 10. Data Visualisation techniques and Algorithms
Chapter 11. Business Intelligence
Chapter 12. Big Data Tools for Tasks.

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