Résumé:
Business Processes(BP)constitutetheheartofInformationSystems(IS)ofmodern
organizations. Thus,theyareintensivelyutilized,bothinthemanagementofvarious
companies’ resourcesandindecision-makingandstrategicalignmentactivities.The
abstract specifications (or models) expressing thebusinesslogicbehindtheBPsarees-
sentialconceptualtoolsusefulforvarioustasks,varyingformmodeling,analysis,moni-
toring andmaintenance.However,withthespectacularincreaseinthevolumeofdata
handled duringthelifecycleofBPs,whichisoftenheterogeneousinnature,conven-
tional approachesformodelingandminingBPmodelsprovetobeineffective,hindering
decision-making actions.
Toovercometheselimitations,inthisthesisweleveragethelatestadvancementsachieved
in theAIareainordertoimprovedecisionsupportsystemsinthefieldofBPsmanage-
ment.Thefirstcontributionofthisthesisconsistsofaconceptualframework,calledDSS
for BP(DSS4BP),whichallowsconstructingaKnowledgeGraph(KG)thatrepresents
the datamanipulatedbytheBPsandtheirlinks.TheconstructedKGispoweredbya
graphical capsuleneuralnetwork,anditspurposeistoenablepredictiveanalysisoffu-
ture activitiesduringtheprogressionofaBP.ThisDSS4BPisbasedontheG-CAPS-NN
architecturetrainedtodiscovertheKG-BP.ThisKGexcelsincapturingcomplexde-
pendencieswithintheactivityflowscontainedinthedifferentBPsspecifications.Thus,
the developedgraphpromotesahighpredictionoffutureeventsandadeepcontextual
understanding ofBPvariationsandevolution.Oursecondcontributionisachat-bot
named BPforDecisionSupportSystem(BP-DSS3),whichrefinestheGPT-3.5-turbo
chat-bottoassistBPmanagedmakingmoreinformeddecisions.ThisBP-DSS3chat-bot
leveragesdeeplearningtechniquestoprovidepersonalizedanddomain-specificdecision
support.Afterthetrainingphase,itachievesahighlevelofprecisionandaccuracy
when managingreal-worldscenarios,suchasAlignmentwithOrganizationalObjectives
(AOO)andRiskManagementandContingencyPlanning(RMCP).
The experimentsareconductedbasingonreal-worlddata,thetwoproposedframe-
workshavedemonstratedsignificantimprovementsintermsofefficiency,adaptability,
and performancecomparedtotraditionalapproaches.DSS4BPenablesorganizationsto
proactivelyidentifyinefficienciesandpredictfutureoutcomesofdeployedbusinesspro-
cesses, whileBP-DSS3significantlyimprovesdecision-makingbyprovidingactionable
and domain-specificinformation.
