Table Of ContentSpringer Tracts in Nature-Inspired Computing
Simon James Fong
Richard C. Millham Editors
Bio-inspired
Algorithms for Data
Streaming and
Visualization, Big
Data Management,
and Fog Computing
Springer Tracts in Nature-Inspired Computing
Series Editors
Xin-SheYang,SchoolofScienceandTechnology,MiddlesexUniversity,London,
UK
Nilanjan Dey, Department of Information Technology, Techno India College of
Technology, Kolkata, India
Simon Fong, Faculty of Science and Technology, University of Macau, Macau,
Macao
The book series is aimed at providing an exchange platform for researchers to
summarize the latest research and developments related to nature-inspired
computing in the most general sense. It includes analysis of nature-inspired
algorithms and techniques, inspiration from natural and biological systems,
computational mechanisms and models that imitate them in various fields, and
the applications to solve real-world problems in different disciplines. The book
series addresses the most recent innovations and developments in nature-inspired
computation,algorithms,modelsandmethods,implementation,tools,architectures,
frameworks, structures, applications associated with bio-inspired methodologies
and other relevant areas.
The book series covers the topics and fields of Nature-Inspired Computing,
Bio-inspired Methods, Swarm Intelligence, Computational Intelligence,
Evolutionary Computation, Nature-Inspired Algorithms, Neural Computing, Data
Mining, Artificial Intelligence, Machine Learning, Theoretical Foundations and
Analysis, and Multi-Agent Systems. In addition, case studies, implementation of
methods and algorithms as well as applications in a diverse range of areas such as
Bioinformatics, Big Data, Computer Science, Signal and Image Processing,
Computer Vision, Biomedical and Health Science, Business Planning, Vehicle
Routing and others are also an important part of this book series.
The series publishes monographs, edited volumes and selected proceedings.
More information about this series at http://www.springer.com/series/16134
Simon James Fong Richard C. Millham
(cid:129)
Editors
Bio-inspired Algorithms
for Data Streaming
and Visualization, Big Data
Management, and Fog
Computing
123
Editors
Simon James Fong Richard C.Millham
University of Macau Durban University of Technology
Taipa, China Durban,SouthAfrica
ISSN 2524-552X ISSN 2524-5538 (electronic)
SpringerTracts inNature-Inspired Computing
ISBN978-981-15-6694-3 ISBN978-981-15-6695-0 (eBook)
https://doi.org/10.1007/978-981-15-6695-0
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Preface
The purpose of this book is to provide some insights into recently developed
bio-inspired algorithms withinrecent emerging trends offogcomputing,sentiment
analysis,anddata streamingaswell astoprovideamore comprehensiveapproach
tothebigdatamanagementfrompre-processingtoanalyticstovisualisationphases.
Althoughtheapplicationdomainsofthesenewalgorithmsmaybementioned,these
algorithms are not confined to any particular application domain. Instead, these
algorithms provide an update into emerging research areas such as data streaming,
fog computing, and phases of big data management.
This book begins with the description of bio-inspired algorithms with a
description on how they are developed, along with an applied focus on how they
can be applied to missing value extrapolation (an area of big data pre-processing).
Thebookproceedstochaptersincludingidentifyingfeaturesthroughdeeplearning,
overview of data mining, recognising association rules, data streaming, data visu-
alisation, business intelligence and current big data tools.
One of the reasons for writing this book is that the bio-inspired approach does
notreceivemuchattentionalthoughitcontinuestoshowconsiderablepromiseand
diversityintermsofapproachofmanyissuesinbigdataandstreaming.Thisbook
outlines the use of these algorithms to all phases of data management, not just a
specific phase such as data mining or business intelligence. Most chapters
demonstrate the effectiveness of a selected bio-inspired algorithm by experimental
evaluationofitagainstcomparative algorithms. One chapterprovides anoverview
andevaluationoftraditionalalgorithms,bothsequentialandparallel,foruseindata
mining. This chapter is complemented by another chapter that uses a bio-inspired
algorithm for data mining in order to enable the reader to choose the most
appropriate choice of algorithms for data mining within a particular context. In all
chapters, references for further reading are provided, and in selected chapters, we
will also include ideas for future research.
Taipa, China Simon James Fong
Durban, South Africa Richard C. Millham
v
Contents
1 The Big Data Approach Using Bio-Inspired Algorithms: Data
Imputation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Richard Millham, Israel Edem Agbehadji, and Hongji Yang
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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Richard Millham, Israel Edem Agbehadji, and Hongji Yang
3 Data Stream Mining in Fog Computing Environment
with Feature Selection Using Ensemble of Swarm
Search Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Simon Fong, Tengyue Li, and Sabah Mohammed
4 Pattern Mining Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
Richard Millham, Israel Edem Agbehadji, and Hongji Yang
5 Extracting Association Rules: Meta-Heuristic and Closeness
Preference Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
Richard Millham, Israel Edem Agbehadji, and Hongji Yang
6 Lightweight Classifier-Based Outlier Detection Algorithms
from Multivariate Data Stream. . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
Simon Fong, Tengyue Li, Dong Han, and Sabah Mohammed
7 Comparison of Contemporary Meta-Heuristic Algorithms
for Solving Economic Load Dispatch Problem . . . . . . . . . . . . . . . . 127
Simon Fong, Tengyue Li, and Zhiyan Qu
8 The Paradigm of Fog Computing with Bio-inspired Search
Methods and the “5Vs” of Big Data . . . . . . . . . . . . . . . . . . . . . . . . 145
Richard Millham, Israel Edem Agbehadji,
and Samuel Ofori Frimpong
vii
viii Contents
9 Approach to Sentiment Analysis and Business Communication
on Social Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
Israel Edem Agbehadji and Abosede Ijabadeniyi
10 Data Visualization Techniques and Algorithms. . . . . . . . . . . . . . . . 195
Israel Edem Agbehadji and Hongji Yang
11 Business Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207
Richard Millham, Israel Edem Agbehadji, and Emmanuel Freeman
12 Big Data Tools for Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
Richard Millham
About the Editors
Simon James Fong graduated from La Trobe University, Australia, with a
First-ClassHonoursB.E.ComputerSystemsdegreeandaPh.D.ComputerScience
degree in 1993 and 1998, respectively. Simon is now working as an Associate
ProfessorattheComputerandInformationScienceDepartmentoftheUniversityof
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,ITConsultant,andE-commerceDirectorinAustraliaandAsia.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 ofthe JournalofNetwork and Computer Applications ofElsevier,
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 C. 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
diversefieldsfor15years,hejoinedacademeandhehastaughtinScotland,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.
ix
Chapter 1
The Big Data Approach Using
Bio-Inspired Algorithms: Data
Imputation
RichardMillham,IsraelEdemAgbehadji,andHongjiYang
1 Introduction
In this chapter, the concept of big data is defined based on the five characteristics
namelyvelocity,volume,value,veracity,andvariety.Oncedefined,thesequential
phases of big data are denoted, namely data cleansing, data mining, and visual-
ization.Eachphaseconsistsofseveralsub-phasesorsteps.Thesestepsarebriefly
described. In order to manipulate data, a number of methods may be employed.
In this chapter, we look at an approach for data imputation or the extrapolation of
missingvaluesindata.Theconceptofgeneticalgorithmsalongwithitsoff-shoot,
meta-heuristic algorithms, is presented. A specialized type of meta-heuristic algo-
rithm, bio-inspired algorithms, is introduced with several example algorithms. An
example,abio-inspiredalgorithm,thekestrel,isintroducedusingthestepsoutlined
forthedevelopmentofabio-inspiredalgorithm(Zangetal.2010).Thiskestrelalgo-
rithm will be used as an approach for data imputation within the big data phases
framework.
B
R.Millham( )·I.E.Agbehadji
ICTandSocietyResearchGroup,DurbanUniversityofTechnology,Durban,SouthAfrica
e-mail:[email protected]
I.E.Agbehadji
e-mail:[email protected]
H.Yang
DepartmentofInformatics,UniversityofLeicester,Leicester,England,UK
e-mail:[email protected]
©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicense 1
toSpringerNatureSingaporePteLtd.2021
S.J.FongandR.C.Millham(eds.),Bio-inspiredAlgorithmsforData
StreamingandVisualization,BigDataManagement,andFogComputing,
SpringerTractsinNature-InspiredComputing,
https://doi.org/10.1007/978-981-15-6695-0_1
