The Recent Advances in Transdisciplinary Data Science. First Southwest Data Science Conference, SDSC 2022 Waco, TX, USA, March 25–26, 2022 Revised Selected Papers

234 Pages·2022·24.13 MB·English

by  Henry Han, Erich Baker

Henry Han
Erich Baker (Eds.)
Communications in Computer and Information Science 1725
The Recent Advances
in Transdisciplinary
Data Science
First Southwest Data Science Conference, SDSC 2022
Waco, TX, USA, March 25–26, 2022
Revised Selected Papers
Communications
in Computer and Information Science 1725
EditorialBoardMembers
JoaquimFilipe
PolytechnicInstituteofSetúbal,Setúbal,Portugal
AshishGhosh
IndianStatisticalInstitute,Kolkata,India
RaquelOliveiraPrates
FederalUniversityofMinasGerais(UFMG),BeloHorizonte,Brazil
LizhuZhou
TsinghuaUniversity,Beijing,China
Moreinformationaboutthisseriesathttps://link.springer.com/bookseries/7899
·
Henry Han Erich Baker (Eds.)
The Recent Advances
in Transdisciplinary
Data Science
First Southwest Data Science Conference, SDSC 2022
Waco, TX, USA, March 25–26, 2022
Revised Selected Papers
Editors
HenryHan ErichBaker
BaylorUniversity BaylorUniversity
Waco,TX,USA Waco,TX,USA
ISSN 1865-0929 ISSN 1865-0937 (electronic)
CommunicationsinComputerandInformationScience
ISBN 978-3-031-23386-9 ISBN 978-3-031-23387-6 (eBook)
https://doi.org/10.1007/978-3-031-23387-6
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Preface
Data science is influencing more and more aspects of business, health, science, and
engineering.Differentdata-driventechniquesarebeingdevelopedtocustomizedifferent
types of data from different fields. For example, AI is highly involved in trading and
investment in finance. Machine learning applications are found not only in healthcare
and biomedical data analytics but also in heliophysics, fluid dynamics, and chemical
engineering.Thesedatasciencetechniquesprovideapowerfulwaytoexploitlargeor
evenmassiveamountsofdataincomparisontothetraditionalmodel-drivenmethods,
whichgenerallyhavetheoreticalassumptionsthatmaynotmatchthedatareality.
Althoughthistransdisciplinaryfieldmaystillneedtimetobecomeanindependent
field,datafromdifferentfieldsaredrivingunprecedentedexpansionofthedatascience
domain as its scope becomes broader and deeper. Investigators from different disci-
plinesaregeneratingnewknowledgeandtechniquestoextenddatasciencefrommore
specific applications. For example, researchers in astrophysics and business may both
investigatetheapplicationsofConvolutionalNeuralNetworks(CNN)intheirfields,but
theformermaywanttoconductglitchdetectionforgravitywavedatawhilstthelatter
seektodesignanefficienttradingmachinebyexploitingthepowerfullearningcapabili-
tiesofthedeeplearningmodel.ThedifferenttypesofdatamaygeneratedifferentCNN
techniquesforthecorrespondingproblemsolvingandtherebyenrichAIandmachine
learning.
Thefutureofdatascienceliesinthegenerationofdifferentsubdomainsaccording
to different data, with AI and machine learning serving the core of the general data
sciencedomain.Thecorrespondingmethodsineachsubdomainwillbefurthertailored
and optimized to accommodate different subdomain data. It is expected that machine
learninganddeeplearningmethodswillbedevelopedanddefinedaccordingtospecific
typesofdata.Forexample,atransformermodelwillberedefinedorpolisheddifferently
under financial data, biomedical data, and social science data. Furthermore, the data
science subdomains, such as financial data science, health data science, social data
science,cyberdatascience,orevendataengineering,etc.,willinteractwitheachother
throughexplainableAItoenhancetheinterpretabilityoftheresultsandtechniquesof
differentdatasciencesubdomains.
Thisvolume(CCIS1725)aimstoreportrecentadvancesinbusinessdatascience,
healthdatascience,applieddatascience,artificialintelligence,anddataengineering.It
collates the work of data science researchers from business, bioinformatics, computer
science, health, physics, and engineering fields. We hope that tthe unique contents,
includingnoveltechniquesinthedifferentdatasciencedomains,willserveasagood
guidefordatascientistsandpractitionersinrelatedfields.
ThisvolumecontainsthebestpapersfromtheSouthwestDataScienceConference
(SDSC) 2022, which was organized by the Data Science group at Baylor University,
Waco, Texas, USA, and took place during March 25–26, 2022. SDSC 2022 attracted
a total of 72 submissions, consisting of 49 full papers and 23 long abstracts, which
underwentrigoroussingle-blindpeerreview.Eachpaperwasreviewedbyatleastthree
vi Preface
ProgramCommitteemembersandfinallyXXXpaperswereacceptedforpresentation
attheconferenceandinclusioninthisproceedings.
HenryHan
ErichJ.Baker
Organization
GeneralChair
HenryHan BaylorUniversity,USA
ProgramChair
ErichJ.Baker BaylorUniversity,USA
SteeringCommittee
HenryHan BaylorUniversity,USA
ErichJ.Baker DepartmentofComputerScience,Baylor
University,USA
JamesStamey DepartmentofStatistics,BaylorUniversity,USA
JonathanTrower HankamerBusinessSchool,BaylorUniversity,
USA
GregHamerly DepartmentofComputerScience,Baylor
University,USA
GregSpeegle DepartmentofComputerScience,Baylor
University,USA
LiangWang DepartmentofPublicHealth,BaylorUniversity,
USA
KeithSchubert ElectricalandComputerEngineering,Baylor
University,USA
ProgramCommittee
MaryLaurenBenton BaylorUniversity,USA
LiangDong BaylorUniversity,USA
JeffForest SlipperyRockUniversity,USA
MichaelGallaugher BaylorUniversity,USA
GregHamerly BaylorUniversity,USA
ShaunHutton BaylorUniversity,USA
ChanGu BallStateUniversity,USA
HaiquanLi UniversityofArizona,USA
ZhenLi TexasWoman’sUniversity,USA
BillPoucher BaylorUniversity,USA
DavidPeng LehighUniversity,USA
viii Organization
EunjeeSong BaylorUniversity,USA
GregSpeegle BaylorUniversity,USA
YangSun CaliforniaNorthstateUniversity,USA
YeTian CaseWesternReserveUniversity,USA
StellarTao UTHealthCenter,USA
LiangWang BaylorUniversityUSA
JeffZhang CaliforniaStateUniversity,Northbridge,USA
KeynoteSpeaker
MarkFerguson UniversityofSouthCarolina,USA
AdditionalReviewers
YaodongChen NorthwestUniversity,China
HemingHuang QinghaiNormalUniversity,China
ChunLi HannaiNormalUniversity,China
DavidLi InstituteofPlantPhysiologyandEcology,China
WenbinLiu GuangzhouUniversity,China
JoeJi ColumbiaUniversity,USA
JenniferRen BaylorUniversity,USA
JuanWang QufuNormalUniversity,China
JayWang MonmouthUniversity,USA
YiWu GeorgiaInstituteofTechnology,USA
JuanyingXie ShaanxiNormalUniversity,China
LiangZhao UniversityofMichigan,AnnArbor,USA
The Convergence of BI and AI: With Applications
for the Better Management of Higher Education
(Keynote)
MarkFerguson
AssociateDeanforAcademicsandResearch,TheDeweyH.JohnsonProfessorof
ManagementScienceDarlaMooreSchoolofBusiness,UniversityofSouthCarolina,
Columbia,SC29028,USA
[email protected]
Abstract.ThetermsArtificialIntelligence(AI)andMachineLearning
(ML)havebeenaroundforsometimebuthasobtainedamuchbroader
exposureduringthelastfiveyears.Thisincreaseinexposureseemstobe
comingattheexpenseofBusinessIntelligence(BI),asmoreuniversity
classes and company projects are being rebranded from BI to AI. The
producersofBItoolsandsolutionsmostfrequentlyrefertothemselves
atBusinessAnalysts,whiletheproducersofAItoolsandsolutionsrefer
tothemselvesasDataScientist. I’llfirstpresentacasethatthebiggest
opportunities for most organizations is in the convergence and tighter
integrationofthesetwoareas. Asanexampleapplicationofthepotential
ofthisconvergence,I’lldiscussanindustrythatshouldbefamiliartomost
oftheaudience,highereducation. Despiteemployingarguablysomeof
thebesttalentavailableinboththeAIandBIspace,theadministrationof
mostcollegesanduniversitieslagsfarbehindintheuseofthesepowerful
toolsascomparedtosimilarsizedorganizationsinotherindustries. I’ll
share some examples of how the tools are being employed at my own
universityandpostulateonsomeofthefactorsthatinhibitwideradoption.
Keywords:AI·BusinessIntelligence·Highereducationmanagement
Dr.MarkFergusonistheSeniorAssociateDeanforAcademicsandResearchandthe
Dewey H. Johnson Professor of Management Science at the Darla Moore School of
BusinessattheUniversityofSouthCarolina.HereceivedhisPh.D.inBusinessAdmin-
istrationwithaconcentrationinoperationsmanagementfromDukeUniversityin2001.
HeholdsaB.S.inMechanicalEngineeringfromVirginiaTechandanM.S.inIndustrial
EngineeringfromGeorgiaTech.