Big Data and Visual Analytics

262 Pages·2017·8.38 MB·English

by  Sang C. Suh

Sang C. Suh
Thomas Anthony Editors
Big Data
and Visual
Analytics
Big Data and Visual Analytics
Sang C. Suh • Thomas Anthony
Editors
Big Data and Visual
Analytics
123
Editors
SangC.Suh ThomasAnthony
DepartmentofComputerScience DepartmentofElectricaland
TexasA&MUniversity-Commerce ComputerEngineering
Commerce,TX,USA TheUniversityofAlabamaatBirmingham
Birmingham,AL,USA
ISBN978-3-319-63915-4 ISBN978-3-319-63917-8 (eBook)
https://doi.org/10.1007/978-3-319-63917-8
LibraryofCongressControlNumber:2017957724
©SpringerInternationalPublishingAG2017
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Foreword
Theeditorsofthisbook,anaccomplishedseniordatascientistandsystemsengineer,
Thomas Anthony, and an academic leader, Dr. Sang Suh, with broad expertise
fromartificialintelligencetodataanalytics,constituteaperfectteamtoachievethe
goal of compiling a book on Big Data and Visual Analytics. For most uninitiated
professionals“BigData”isnothingbutabuzzwordoranewfad.Forthepeoplein
thetrenches,suchasSangandThomas,BigDataandassociatedanalyticsisamatter
ofseriousbusiness.Iamhonoredtohavebeengiventheopportunitytoreviewtheir
compiledvolumeandwriteaforewordtoit.
Afterreviewingthechapters,Irealizedthattheyhavedevelopedacomprehensive
book for data scientists and students by taking into account both theoretical and
practical aspects of this critical and growing area of interest. The presentations
are broad and deep as the need arise. In addition to coveringall criticalprocesses
involvingdatascience,theyhaveuniquelyprovidedverypracticalvisualanalytics
applicationssothatthereaderlearnsfromtheperspectiveexecutedasanengineer-
ing discipline. This style of presentation is a unique contribution to this new and
growingareaandplacesthisbookatthetopofthelistofcomparablebooks.
Thechapterscoveredare1.AutomatedDetectionofCentralRetinalVeinOcclu-
sion Using ConvolutionalNeuralNetwork,by “Bismita Choudhury,PatrickH. H.
Then,andValliappanRaman”;2.SwarmIntelligenceAppliedtoBigDataAnalytics
forRescueOperationswithRASENSensorNetworks,by“U.JohnTanik,Yuehua
Wang, and Serkan G. ldal”; 3. Gender Classification Based on Deep Learning,
by “Dhiraj Gharana, Sang Suh, and Mingon Kang”; 4. Social and Organizational
Culture in Korea and Women’s Career Development, by “Choonhee Yang and
YongmanKwon”;5.BigDataFrameworkforAgileBusiness(BDFAB)asaBasis
for DevelopingHolistic Strategies in Big Data Adoption, by “Bhuvan Unhelkar”;
6.ScalableGeneSequenceAnalysisonSpark,by“MuthaharSyed,JinohKim,and
TaehyunHwang”;7.BigSensorDataAcquisitionandArchivingwithCompression,
by “Dongeun Lee”; 8. Advanced High Performance Computing for Big Data
LocalVisualMeaning,“OzgurAksu”;9.TransdisciplinaryBenefitsofConvergence
in Big Data Analytics, “U. John Tanik and Darrell Fielder”; 10. A Big Data
Analytics Approach in Medical Image Segmentation Using Deep Convolutional
v
vi Foreword
NeuralNetworks,by“ZhengZhang,DavidOdaibo,andMuratM.Tanik”;11.Big
DatainLibraries,by“RobertOlendorfandYanWang”;12.AFrameworkforSocial
Network Sentiment Analysis Using Big Data Analytics, by “Bharat Sri Harsha
KarpurapuandLeonJololian”;13.BigDataAnalyticsandVisualization:Finance,
by“P. ShyamandLarryMave”;14.Studyof HardwareTrojansin a Closed Loop
Control System for an Internet-of-Things Application, by “Ranveer Kumar and
Karthikeyan Lingasubramanian”; 15. High Performance/Throughput Computing
Workflow for a Neuro-Imaging Application: Provenance and Approaches, by
“T.Anthony,J.P.Robinson,J.Marstrander,G.Brook,M.Horton,andF.Skidmore.”
The review of the above diverse content convinces me that the promise of
the wide application of big data becomes abundantly evident. A comprehensive
transdisciplinary approach is also evident from the list of chapters. At this point
IhavetoinvoketheroadmappublishedbytheNationalAcademyofSciencestitled
“Convergence:FacilitatingTransdisciplinaryIntegrationofLifeSciences,Physical
Sciences, Engineering, and Beyond” (ISBN 978-0-309-30151-0). This document
and its NSF counterpart states convergence as “an approach to problem solving
thatcutsacrossdisciplinaryboundaries.Itintegratesknowledge,tools,andwaysof
thinkingfromlife and health sciences, physical,mathematical, and computational
sciences, engineering disciplines, and beyond to form a comprehensive synthetic
frameworkfortacklingscientificandsocietalchallengesthatexistattheinterfaces
ofmultiplefields.” Big data andassociated analyticsis a twenty-firstcenturyarea
of interest, providing a transdisciplinary framework to the problems that can be
addressedwithconvergence.
Interestingly,theSocietyforDesignandProcessScience(SDPS),www.sdpsnet.
org,whichoneoftheauthorshasbeeninvolvedwithfromthebeginning,hasbeen
investigating convergence issues since 1995. The founding technical principle of
SDPShasbeentoidentifytheunique“approachtoproblemsolvingthatcutsacross
disciplinaryboundaries.”TheanswerwastheobservationthatthenotionsofDesign
and Process cut across all disciplines and they should be studied scientifically in
their own merits, while being applied for developing the engineering of artifacts.
Thisbookbringsthedesignandprocessmatterstotheforefrontthroughthestudy
of data science and, as such, brings an important perspective on convergence.
Incidentally,theSDPS2017conferencewasdedicatedto“ConvergenceSolutions.”
SDPSisaninternational,cross-disciplinary,multiculturalorganizationdedicated
to transformative research and education through transdisciplinary means. SDPS
celebrateditstwenty-secondyearduringtheSDPS2017conferencewithemphasis
on convergence. Civilizations depend on technology and technology comes from
knowledge. The integration of knowledge is the key for the twenty-first century
problems.Data science in generaland Big Data Visual Analyticsin particularare
partoftheanswertoourgrowingproblems.
This book is a timely addition to serve data science and visual analytics
communityofstudentsandscientists.We hopethatitwillbepublishedontimeto
bedistributedduringtheSDPS2018conference.Thecomprehensiveandpractical
natureof thebook,addressingcomplextwenty-firstcenturyengineeringproblems
in a transdisciplinary manner, is something to be celebrated. I am, as one of the
Foreword vii
foundersof SDPS, a military and commercialsystems developer,industrial grade
software developer, and a teacher, very honored to write this foreword for this
importantpracticalbook. I am convincedthat it will take its rightfulplace in this
growingareaofimportance.
ElectricalandComputerEngineeringDepartment MuratM.Tanik
UAB,Birmingham,AL,USA WallaceR.Bunn
EndowedProfessorof
Telecommunications
Contents
AutomatedDetectionof CentralRetinalVeinOcclusionUsing
ConvolutionalNeuralNetwork................................................. 1
BismitaChoudhury,PatrickH.H.Then,andValliappanRaman
SwarmIntelligence Applied to Big Data AnalyticsforRescue
OperationswithRASENSensorNetworks.................................... 23
U.JohnTanik,YuehuaWang,andSerkanGüldal
GenderClassificationBasedonDeepLearning .............................. 55
DhirajGharana,SangC.Suh,andMingonKang
SocialandOrganizationalCultureinKoreaandWomen’sCareer
Development...................................................................... 71
ChoonheeYangandYongmanKwon
BigDataFrameworkforAgileBusiness(BDFAB)AsaBasisfor
DevelopingHolisticStrategiesinBigDataAdoption ........................ 85
BhuvanUnhelkar
ScalableGeneSequenceAnalysisonSpark................................... 97
MuthaharSyed,TaehyunHwang,andJinohKim
BigSensorDataAcquisitionandArchivingwithCompression ............ 115
DongeunLee
AdvancedHighPerformanceComputingforBigDataLocalVisual
Meaning........................................................................... 145
OzgurAksu
TransdisciplinaryBenefitsofConvergenceinBigDataAnalytics.......... 165
U.JohnTanikandDarrellFielder
ABigDataAnalyticsApproachinMedicalImagingSegmentation
UsingDeepConvolutionalNeuralNetworks.................................. 181
ZhengZhang,DavidOdaibo,FrankM.Skidmore,andMuratM.Tanik
ix
x Contents
BigDatainLibraries ............................................................ 191
RobertOlendorfandYanWang
AFrameworkforSocialNetworkSentimentAnalysisUsingBig
DataAnalytics.................................................................... 203
BharatSri HarshaKarpurapuandLeonJololian
BigDataAnalyticsandVisualization:Finance............................... 219
ShyamPrabhakarandLarryMaves
StudyofHardwareTrojansinaClosedLoopControlSystemfor
anInternet-of-ThingsApplication ............................................. 231
RanveerKumarandKarthikeyanLingasubramanian
High Performance/Throughput Computing Workflow for
aNeuro-ImagingApplication:ProvenanceandApproaches................ 245
T.Anthony,J.P.Robinson,J.R. Marstrander,G.R. Brook,M.Horton,
andF.M.Skidmore
Index............................................................................... 257
Automated Detection of Central Retinal Vein
Occlusion Using Convolutional Neural Network
BismitaChoudhury,PatrickH.H.Then,andValliappanRaman
Abstract TheCentralRetinalVeinOcclusion(CRVO)isthenextsupremereason
for the vision loss among the elderly people, after the Diabetic Retinopathy.
The CRVO causes abrupt, painless vision loss in the eye that can lead to visual
impairmentoverthetime.Therefore,theearlydiagnosisofCRVOisveryimportant
to preventthe complicationsrelated to CRVO. But, the early symptomsof CRVO
are so subtle that manually observing those signs in the retina image by the
ophthalmologists is difficult and time consuming process. There are automatic
detectionsystemsfordiagnosingoculardisease,buttheirperformancedependson
variousfactors.Thehaemorrhages,theearlysignofCRVO,canbeofdifferentsize,
color and texture from dot haemorrhages to flame shaped. For reliable detection
of the haemorrhages of all types; multifaceted pattern recognition techniques are
required.Toanalysethetortuosityanddilationoftheveins,complexmathematical
analysis is required in order to extract those features. Moreover, the performance
of such feature extraction methods and automatic detection system depends on
the quality of the acquired image. In this chapter, we have proposed a prototype
for automated detection of the CRVO using the deep learning approach. We have
designed a Convolutional Neural Network (CNN) to recognize the retina with
CRVO. The advantage of using CNN is that no extra feature extraction step is
required. We have trained the CNN to learn the features from the retina images
havingCRVOandclassifythemfromthenormalretinaimage.Wehaveobtainedan
accuracyof97.56%fortherecognitionofCRVO.
Keywords Retinal vein occlusion (cid:129) Central retinal vein occlusion (cid:129) Convolu-
tion (cid:129) Features
B.Choudhury((cid:2))(cid:129)P.H.H.Then(cid:129)V.Raman
CentreforDigitalFuturesandFacultyofEngineering,ComputingandScience,Swinburne
UniversityofTechnology,SarawakCampus,Kuching,Sarawak,Malaysia
e-mail:[e-mail]
©SpringerInternationalPublishingAG2017 1
S.C.Suh,T.Anthony(eds.),BigDataandVisualAnalytics,
https://doi.org/10.1007/978-3-319-63917-8_1