Data Science: Time Complexity, Inferential Uncertainty, and Spacekime Analytics

490 Pages·2021·7.852 MB·English

by  Ivo D. Dinov, Milen Velchev Velev

IvoD.Dinov,MilenVelchevVelev
DataScience
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Ivo D. Dinov, Milen Velchev Velev
Data Science
Time Complexity, Inferential Uncertainty,
and Spacekime Analytics
Authors
Prof.Dr.IvoD.Dinov
UniversityofMichigan
426NorthIngallsStr.
AnnArbor48109-2003
USA
[email protected]
Assoc.Prof.Dr.MilenVelchevVelev
“Prof.Dr.A.Zlatarov”University
1Prof.Yakimovbul.
8010Burgas
Bulgaria
[email protected]
ISBN978-3-11-069780-3
e-ISBN(PDF)978-3-11-069782-7
e-ISBN(EPUB)978-3-11-069797-1
LibraryofCongressControlNumber:2021933301
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detailedbibliographicdataareavailableontheInternetathttp://dnb.dnb.de.
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... dedicatedtoall known polymaths,forgotten intellectuals,andunknown scholars
thatcamebeforeus,whosegiant shoulderssupportthisscientific workandallother
contemporary researchinquiries, as well as, all prospective, yet tobe unearthed dis-
coveries...

Preface
Acquiring information, processing data, assessing risk, and projecting forecasts in
spaceandtimehavealwaysparalleledhumanevolutionandtheever-changingsocial
dynamicsthataccompanyit.Theneedforreliableaccounting,robustrecordkeeping,
commontime,spaceandcelestialreferencing,andbuildingofusefultools,weapons,
and structures emerged with the dawn of mankind’s scientific quest to understand
nature.Objectcoding,canonicalrepresentations,andmathematicaloperationsarise
inmanyofman’searlyearthlyactivities,e.g.,trackingnumericaldata(Mesopotamia,
c.3500BC),fractionalarithmetic(OldEgyptKingdom,c.2500BC),medicaltexts(an-
cientEgypt,c.1600BC),empiricalastronomy(Babylonia,c.700BC),exploringgeo-
metricproperties(ancientGreece,c.550BC),naturalphilosophy(Plato’sAcademy,
c.380BC),etc.[1,2].Althoughalloftheseingeniousadvanceswerebasedonreal,
tangible,andphysicalobservations,averybasiclevelofinformationgathering,cal-
culation,andcommunicationwasalsorequiredtoestablishanearlyscientificepis-
temology.Historicalrecordsindicatethatmanydiscoverieswerelost,rediscovered,
or reformulated over the centuries. However, during the Renaissance (c. 1400 AD)
andtheEnlightenment(c.1600AD)periods,arapidaccelerationofknowledgeaccu-
mulationledtodevelopingamoresolidfoundationforallcontemporaryscientificad-
vances.Asaresultofthischain-reactionandtrackablediscoveryprovenance,most
contemporaryscholarscanreliablytracetheiracademicgenealogytoSharafal-Dīn
al-Ṭūsī(Mesopotamia,c.1160AD,https://mathgenealogy.org/extrema.php,accessed
January29,2021)[3,4].
Over the past 5,000 years, the scientific rise of humanity reflected the trans-
formationofhumancivilizationfromahunter-gatherer(c.10000BC),topastoral
(c.7500BC),agricultural(c.3000BC),feudal(c.900AD),industrial(c.1700AD),
and digital information (c. 1980) societies. The 21st century represents a new era
where information generation, processing, transmission, and rapid interpretation
become fundamental sources of productivity, power, wealth, and affluence. This
shiftfromindustrialismtodata-informationalismdirectlytiestocurrentandprospec-
tiveglobal,cultural,environmental,andsocioeconomicdynamics.
Since2010,therehasbeencompellingevidenceillustratingextremelyrapid,ubiq-
uitous, anddisruptivequantization of allhuman experiences.Therate of increase of
the amount of data we collect doubles every 12–14 months (Kryder’s law) [5–7], the
rateofexpansionofcomputationalpoweralsogrowsexponentially(Moore’slaw)[8],
andtheuniversalproliferationofinformationdigitalization(digitaltransformation)[9]
nowcoversallfacetsoflife.Thisdigitaltransformationrepresentsthenaturalprogres-
sion from continuous analogue communication to exchanges of discrete information
bits.Alllifeforms,includinghumans,haveperfectlyevolvedtointerpretintrinsically
discreteprocessesascontinuouspatterns.Now,humancivilizationisswingingthe
pendulum in the opposite direction, toward quantizing information detection, ag-
gregation,communication,andinterpretation.Thisleadstointerestinganddisruptive
https://doi.org/10.1515/9783110697827-202
VIII Preface
human-machineinterfacesthatpromisetopooltheexceptionalhumancapabilities
of reason, emotion, intelligence, and creativity, with the unique computer ability
for rapid en masse processing of data, algorithmic precision, and impassive treat-
ment of complex information. The result of this is an explosion of new machine
learning techniques, artificial intelligence tools, augmented reality experiences,
andhuman-machineinterfaces.Mostoftheseadvancesrequirenoveldatascience
methods,computationalalgorithms,andinferentialstrategies.
Today, contemporary digital transformation is now seamlessly blending techno-
logicaladvanceswithinallhumanexperiences;impactinghowweperceivetheenvi-
ronment,transformingourvaluesystem,anddisruptingtraditionaldailyinteractions.
Beyondthat,aculturalchangeisalsotakingplacerequiringindividual,social,govern-
mental,public,andprivateorganizationstocontinuallychallengethestatusquo,
explorealternatives,andgetcomfortablewithsomedegreeoffailureintherelent-
lessmarchtowarddigitalization.
The momentum of volume and complexity growth of digital information ap-
pearstobeexceedingtheincreaseinourabilitytointerpretitholistically.Thisim-
balance between the natural progression of digital transformation and mankind’s
capacitytoderivevaluefromthisinformationstressesresourcesandinhibitsknowl-
edge-drivenactions.Asanexample,it’snowcommonforscientiststogatherhetero-
geneousdataontheorderofapetabyte,1PB=1015bytes.Tograsptheenormityand
thecomplexityofhandlingsuchvoluminousinformation,consideritinrelationto
theMilkyWaygalaxy,whichhasapproximately2×1011stars.Ifeachstarrepresents
abyte,thenonepetabyteofdatacorrespondsto5,000MilkyWaygalaxies.Justas
thespatiallocation,luminosity,chemicalcomposition,andmomentumofeachstar
inthegalaxychangeswithtime,andinrelationtoothergalacticbodies,thescien-
tificinformationcollectedbyresearchershaslongitudinalpatternsandspatialasso-
ciationsthatfurthercomplicatetheinterpretationofsuchcomplexdataarchives.
Thereisascientificrevolutionunderwaytodevelopadaptive,semi-supervised,
self-reinforced, and reliable computational and data science methods capable of
transfer-learning supporting effective data-driven decision support systems. All of
the efforts to design, implement, and validate such new techniques require an in-
depth basic scientificknowledge across manyapplied, experimental, andtheoreti-
caldisciplines,aswellastransdisciplinaryteam-basedopen-sciencecollaborations
withamalgamationofexploratoryandconfirmatoryscientificdiscoveries.
Thisbookprovidesmotivationalongwithsubstantialmathematicalbackground,
examples of promising approaches, and a new methodological foundation for data
science that extends the concept of event order (time) to the complex plane. It also
includeshands-ondemonstrationsofseveralbiomedicalandeconometriccasestud-
ies, implementation protocols, and applications. Supplementary online materials
includedatasets;anRpackagewithasuiteoflibrariesformanagingandinterro-
gatingraw,derived,observed,experimental,andsimulatedbighealthcaredatasets;
and other web-based services for data visualization and processing. The supporting
Preface IX
websitesprovideadditionalinteractivecontent,learningmodules,casestudies,and
demonstrations(https://spacekime.org, accessed January 29, 2021 and https://tciu.
predictive.space,accessedJanuary29,2021).
Thecontentofthisbookmaybeappropriateforavarietyofupper-divisionun-
dergraduateorgraduatescience,technology,engineering,andmathematics(STEM)
courses. To address the specific needs of their students, instructors can reshuffle
andpresentthematerialsinalternativeways.Thebook’schaptersareorganizedse-
quentiallyillustratingoneintuitivewayofcoveringthetopics;however,thecontent
can be restructuredandtailored to fitspecificcurricular objectivesoraudience re-
quirements.Thisisnotageneraltextbookforlearningthefoundationsofcomputa-
tionalanddatasciences.Asof2021,therearemanyothertextbooksthatcoverthe
breadthanddepthofdatascienceandpredictiveanalytics.Capitalizingontheex-
isting wealth of knowledge, this book develops the spacekime analytics technique
and includes illustrative examples of data science applications; inferential uncer-
tainty; complex-time representation of large, high-dimensional, and longitudinal
datasets.
Thisbookutilizestheconstructivedefinitionof“BigData”providedinDataSci-
enceandPredictiveAnalytics(https://DSPA.predictive.space,accessedJanuary29,
2021) [10], which is based on examining the common characteristics of many bio-
medical,social-science,andhealthcarecasestudies.Thesevenkey“BigData”charac-
teristics identified from such studies include large size, heterogeneity, incongruency,
incompleteness, multiscale format, time-varying nature, and multisource origins.
These properties play a critical role in many scientific inference processes repre-
senting compound systematic investigations. Specifically, many scientific work-
flowsincludecriticalstepstohandeachoftheBigDatacharacteristicsfromtheinitial
study-design, to subsequent data collections, wrangling, and management; model-
basedstatisticalanalyses,model-freemachinelearningmethods,outcomepredictions,
trendforecasting,derivedcomputedphenotyping,algorithmfine-tuning,assessments,
comparisons;andscientificvalidations.
The authorsare profoundly indebted to all of their students, mentors, advisors,
andcollaboratorsforinspiringthisstudy,guidingthecoursesoftheircareers,nurtur-
ingtheircuriosity,andprovidingconstructiveandcriticalresearchfeedback.Among
these scholars are Guentcho Skordev (Sofia University); Kenneth Kuttler and Anant
Godbole (Michigan Tech University); De Witt L. Sumners and Fred Huffer (Florida
StateUniversity);JandeLeeuw,NicolasChristou,andMichaelMega(UCLA);Arthur
Toga(USC);BrianAthey,H.V.Jagadish,KathleenPotempa,JanetLarson,Patricia
Hurn,GilbertOmenn,andEricMichielssen(UniversityofMichigan).
Particularlyusefulfeedbackandconstructiverecommendationswereprovidedby
Yueyang Shen, Yuxin Wang, Zijing Li, Daxuan Deng, Yufei Yang, Christopher Hale,
and Yupeng Zhang. Many other colleagues, students, researchers, and fellows have
sharedtheirexpertise,creativity,valuabletime,andcriticalassessmentforgenerating,
validating,andenhancingtheseopen-scienceresources.AmongtheseareYongkai
X Preface
Qiu, Zhe Yin, Rongqian Zhang, Yuyao Liu, Yunjie Guo, Jinwen Cao, Reza Soroush-
mehr,YumingSun,LingcongXu,SimeoneMarino,AlexandrKalinin,KalyaniDesi-
kan, Christoph Köhn, Vimal Rathee, Daniel Rowe, Manthan Mehta, Hristo Pavlov
Pavlov,VesselinGueorguiev,andmanyothers.
In addition, students and colleagues from the Statistics Online Computational
Resource (SOCR), the Michigan Institute for Data Science (MIDAS), and “Prof. Dr.
Asen Zlatarov” University in Burgas provided encouragement, support, and valu-
able suggestions. Insightful comments, suggestions, corrections, and constructive
critiquesfrommanyanonymousreviewerssignificantlyimprovedandclarifiedthe
material. We welcome broader contributions, comments, feedback, and input from
theentirescientificcommunityviaanonlinewebform(https://tciu.predictive.space,
accessedJanuary29,2021)andthroughanopen-sourcesoftwareversioncontrolplat-
form(https://github.com/SOCR/TCIU,accessedJanuary29,2021).
The research methods, computational developments, and scientific applica-
tions reported in this book were partially supported by the US National Science
Foundation(grants1916425,1734853,1636840,1416953,0716055and1023115),US
NationalInstitutesofHealth(grantsP20NR015331,U54EB020406,UL1TR002240,
P30 DK089503, R01 CA233487, R01 MH121079, R01 MH126137, T32 GM141746),
“Prof.Dr.AsenZlatarov”UniversityinBurgas,andtheUniversityofMichigan.
IvoD.Dinov
(AnnArbor,Michigan,USA)
MilenV.Velev
(Burgas,Bulgaria,EU)