towards open smart services platform
TRANSCRIPT
Hamid R. Motahari-Nezhad1, and Larisa Schwartz2
1 IBM Almaden Research Center2 IBM TJ Watson Research Center
Towards Open Smart Services PlatformA Foundation for Cognitive Enterprise Services
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EnterpriseServices
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A. Service Provider
• Individual• Institution• Public or Private
C. Service Target: The reality to be transformed or operated on by A, for the sake of B
• Individuals or people, dimensions of • Institutions or business and societal organizations,
organizational (role configuration) dimensions of• Infrastructure/Product/Technology/Environment,
physical dimensions of• Information or Knowledge, symbolic dimensions
B. Service Customer
• Individual• Institution• Public or Private
Forms ofOwnership Relationship
(B on C)
Forms ofService Relationship
(A & B co-create value)
Forms ofResponsibility Relationship
(A on C)
Forms ofService Interventions
(A on C, B on C)
Spohrer, J., Maglio, P. P., Bailey, J. & Gruhl, D. (2007). Steps toward a science of service systems. Computer, 40, 71-77.From… Gadrey (2002), Pine & Gilmore (1998), Hill (1977)
A B
C
Vargo, S. L. & Lusch, R. F. (2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68, 1 – 17.
“Service is the application of competence for the benefit of another entity.”
MajorTypesofService(providerperspective):• Computational/technologyservices• Business/Enterpriseservices• PeopleServices
ServiceOfferings
Definition&Design
ServiceSalesPursuit
TransitionandTransformation
ServiceDelivery&Operation
LifecycleofEnterprise(IT)Services
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MegaTrendsinEnterpriseServices
Outsourcing deal sizes are shrinking!n In 2013, number of contracts climbed 2%, total contract value
(TCV) declined 18.2%
Deal shrinkage due to more service integrationn “Hybrid Cloud and IT as Service Broker” became a top trend in
2014
Service integration guidelines for Multi-sourcingn UK govt. authored SIAM (service integration and management)n SIAM is a guideline for managing multiple suppliers by a given
business (the consumer side)*
Autonomic and cognitive computing impact on service management
n Virtual knowledge engineers” absorb work previously carried out by human and shifting sales and delivery of IT services
Old New Future!
Source:Gartner VirtualKnowledgeEngineers★ JamieErbes,HamidR.Motahari Nezhad,SvenGraupner:TheFutureofEnterprise ITinthe
Cloud. IEEEComputer45(5):66-72(2012)
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EnterpriseWorkforce:ThemovetowardsOnDemand,andDigital!
Enterprise workforce shift: online staffingn Enterprise crowdsourcing was named a major trend in Accenture
Technology Vision 2014 n The revenue for Enterprise Crowdsourcing grew approximately
53% from 2009 to 2010 and accelerated to 75% from 2010 to 2011
On-demand workforce in IT outsourcingn IT service managers have started using crowdsourced staff (e.g. Axios)n The rise of specialized crowdsource-based IT service providers
(e.g., onForce onsite IT services with warranty, and contingent classification)
n Crowdsourced staff is used in training machine learning and AI algorithms, and agents (e.g., WorkFusion)
Robots and Chatbots are the pioneers “Virtual (knowledge) engineers”n RPA (Robotic Process Automation): A bank deployed 85 bots for 13 processes,
handling 1.5 million requests per year. It added capacity equivalent to more than 200 full-time employees at approximately 30 percent of their cost.
n Chatbots have been developed in IT and in customer support and representatives in different industries
n Nevertheless, cognitively enabled bots (chatbots) have been applied in limited (reps) roles
n And, many companies, large and startups, are competing to provide the platform of chose for bots
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Theneedforanopenplatform/marketplaceforEnterpriseITServices
Services Financial
TechnologyOrganizational
§Simplifying service contract and pricing: Using standard market services at all-inclusive unit pricing
§Pay per use with flexibility to in- / decrease§ Incentive to move to aaS model due to lower prices§Transparent financial model that establishes costs in
business terms
§Standardize platforms based on open standards with high automation
§Continuous improvement based on analytics and cognitive technologies (just-in-time capabilities)
§Use of Software Defined Environment across IT infrastructure (compute, storage and network)
§Focusing on business outcome, rather than IT capabilities
§Value-based choices through tiered standard offerings & service levels
§LOBs manage consumption and service level choices
§CMOs & LoB owners make sourcing decisions, often “business-driven”
§Cultural shift of organization from procure/run to consumption of IT services
§More consumable IT services offered by vendors
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Open Services Management Platform: Conceptual Architecture
Business-defined solution specs and restrictions, e.g., “full service,” “selected service,” or “multi-sourced”
requirements
Business-centric performance and risk predictions, alerts, traditional ITSM reporting
Business Functions of ClientsCRM HR Payroll Finance Collaboration…
Vendor and Partner IT Service, and Enterprise Workforce Providers
Client-Facing Components
Hybrid services provider analytics
Solution composition and service orchestration
Governance and analytical substrate
IT provider bid management
IT S
ervi
ces
Plat
form
Man
agem
ent
Service Management Components
Service Providers Management Components
Client Business-Centric Self-service Portal
Business-IT service/requirement mapping and translation
Service composition and orchestration
Business-IT risk/compliance/policy/SLA analysis
Hybrid services provider analytics
Knowledge management and analytics)
Services Offerings Management
Provider Subscription Management
Multi-vendor and Hybrid Service Governance
Workforcemanagement
Travel
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SelectTechnicalChallengesofOpenServicesPlatform
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•Discoveryofclientbusiness “functions” anddependencies onclient'sITcomponents and/orrequirements•Automateddecomposition ofthefunctions tointomultiple(independent) serviceproviders, translationofbusiness-to-IT requirements(e.g.,SLAs)
Businessfunctionouttasking
•Business function riskassessment/prediction andITincidentresolution basedonmultiple(interdependent) serviceprovideractivities,withlimitedcontrolsintoproviders’ infrastructures
•Howdoes theServicebroker coordinatetheactivitiesofdifferentservice providers toprovideplatform-levelguarantees?
Multi-providergovernance
•Universaldatamodels (ontologies) andconfigurationmanagementsupporting low-risk, low-delayserviceproviderinterchangeability(e.g.,whatifagentsareused)
Service integrationabstractions,andmodels
•Coordinating activities, andintegrating theoutputs fromcomputationalunits(digitalworkforce)andcrowd-sourced staffforouttasking
Crowdsourced andDigitalworkforcemanagement
•Abilitytoofferadvancedanalytics totheserviceprovidersonopportunity selection, clientrequirementanalysis
•Abilityfortheserviceprovidertoputtogether asolutionoverthecapabilitiesofserviceproviders.
ServiceAnalytics andCognitiveSupport forservicesales&
operation
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TheLifecycleofanEnterpriseITServices(Contract)
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PriorDeals ServiceOfferings
Guidelines,methodologies
PeopleProfiles
LessonsLearned
ServiceDeliveryData
Opportunity DealDeal Deal Checkpoints/ Contract T&TSteady-StateRenewalIdentification Validation Qualification Pursuit QA/RiskAnalysis Delivery
EngagementTransition &
Transformation RenewalSteady-StateDelivery
BusinessDevelopment
Current DealsPipeline
Revenue&FinanceInformation
IntegrateandMaketheDataAvailableUsingInterfaces(APIs)DealInformationManagement
EnableReusingDealArtifactsandSharingKnowledge
CognitiveBusinessRequirementsUnderstanding AnalyzingRFPstoextractrequirements,andauthorRFPResponse
CognitiveSolutioning Composethesetofserviceofferingsthatmeetsclientsrequirements
CognitiveProcessAutomation
CognitiveRoboticAutomation
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ProblemofRequirementsIdentificationinRFPs
§ RFPturnaroundtimesarechallenging– Typically3to6weeks(havebeenshrinkingovertime)
§ RFPdocumentscontains10sofdocument,each100sofpagesdescribingvariousaspectsoftherequirements
§ InitialRFPReadingandUnderstandingisTimeConsuming§ AnalyzingtheRFPisChallengingandTimeConsuming§ Technicalchallenges
– Therearehundreds ofrequirements statedineachRFPthatneedtobeidentified andanalyzed, includingwhoseresponsibility (serviceproviderorcustomer) istoperformthem• Usingdifferentdocument structure,languagestructures,wordings,fileformats,etc.• Consistency,ambiguity,understandingthemfromSO’sofferingsperspective
– Identificationofwhatconstitutearequirement isverychallenging– Identificationofkeyrequirements (amongall),andtheirrelationships toIBMofferings isakey– Customerinstructionsontheformatandresponserequirementsneedtobeunderstood
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Requirementsexpressedindifferentformandstructures:tablesandtext
InSection3(ITServiceManagement- ServiceRequirements)
ASubsection
Sub-requirements
SP’sRequirementIndicators
SPRequirements(Extractthese!)
ARequirement
Titleofthetable,potentiallyTopic
[Customer]
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RFPCogforCognitiveRequirementsUnderstandingandServiceSolutionGeneration
RFP Documents
RequirementsIdentification
Service Catalogs
ITIL
Cognitive Service Composition
Requirements-driven Technical Solutions Composition
SolutionPatternsCustomer
Service Vocabulary
SolutionsTaxonomy
ProviderOffering Taxonomy
What are client requirement statements?
How client works?
What services offerings/services would meet these requirements?
ServiceRequestIdentificationandGrouping
What are in-scope and out-of-scope service?
workflows
Instructions/Procedures
HamidR.MotahariNezhad,JuanM.Cappi,TaigaNakamura,MuQiao:RFPCog:Linguistic-Based Identification andMappingofServiceRequirements inRequestforProposals(RFPs)toITServiceSolutions. HICSS2016:1691-1700
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CognitiveServiceComposition
§ Knowledge-DrivenCapabilityDecomposition– Buildingaknowledgegraphofprovider servicecapabilitiesbybuilding onindustry taxonomy (ITIL)andproviderservicetaxonomies
§ KeyFactorsinCognitiveSolutionComposition:afitnessscoreforservicecapabilitiesinacomposition– SemanticmappingofRequirementsdescription toServiceCapabilityDescription
• Word2Vecbasedmapping,specificallytrainedforITservicesdomain• LDA-basedmatchingofservicerequestwithservicecapabilities
– Ontology-drivenmapping servicerequirements toservicecapabilities– Structurally-awaremapping ofrequirements toservicecapabilities
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RFPCog PoC – RequirementsMarking,andRequirements- ServiceMappings
HumanInteractionandFeedbackEnablement:• Requirements– NotaRequirement• Requirements– MissedaRequirement• Matching– IncorrectMatching• Matching–MissedaMatching
Feedbackisfedbackintothesystemtore-trainitadaptively,andinanonline/offlinemanner
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ExperimentalResults
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ML-based Topic Classification Performance (TP Rate)
0.9518 0.87330.7587
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
SVM Logistic Regression Naïve Bayes
TP R
ate
Support Vector Machine (SVM) Performance Details
TP Rate FP Rate Precision Recall FMeasure ROC Area Class
0.986 0.232 0.958 0.986 0.972 0.877 F
0.768 0.014 0.908 0.768 0.832 0.877 T
Weighted Avg. 0.952 0.198 0.951 0.952 0.950 0.877
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eAssistant for Cognitive Process Assistance
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Watson (& BigInsight NLP) Apps and Services on BlueMix
Colla
bora
tion
Tool
s
Enterprise Repositories, Applications and Data Sources
FeedsRepositories
Document collections
…
eAssistant Apps
Process Knowledge
Graph Builder
Conversation Analytics, Auto-Response,
Prioritization
Calendar and Scheduling Assistant
Cognitive Process Learning
Process Assistant
Cognitive Work Assistant APIs
Semantic Role
LabelingPOS tagging Dependency
AnalysisCo-reference
resolutionNamed Entity
Recognition
Knowledge GraphBuilder
HamidR.MotahariNezhad,CognitiveAssistanceatWork,inAAAIFallSymposium2015.
RichardHull,HamidR.MotahariNezhad:RethinkingBPMinaCognitiveWorld:TransformingHowWeLearnandPerformBusinessProcesses.BPM2016:3-19
Furtherdetails:
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CompositionofDigitalWorkforce,APIs&peopleinofferingEnterpriseServices
§ IncurrentHybridcomposition/mashup (People,Services)methods:– ServicesarerepresentedwithAPIcalls– PeopleareintegratedwithHumanTasks(GUIistheinteractionparadigm)
– Composition methods arefindingdeterministicmodels ofinteractions, defined apriori
§ Wewillbemovingtowardsdynamiccompositionofcogsandhumaninwhich– CogsareparticipatinginNLconversations– Humanareapproached throughmessagingandnaturallanguage
– Composition areperformeddynamicallyduringtheconversation, requirenon-deterministic models, definedinonline andon-demandmodel
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Weather Cog
Health Agent
PersonalityInsight Cog.
ProviderCogs
TravelCog1
TravelCog2
PlanningaVacationTrip
Consideringpreferences,experience,conditions,cost,Availability,etc.
MediatedandfacilitatedbyCogs
Human-Coginteraction
Cog-CoginteractionNaturalLanguage
NaturalLanguage,CCL,(ACL,KQML,etc.)?
ACL:AgentCommunicationLanguage,KQML,etc.
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Summary&Conclusion
§ TheFutureofComputingis….
§ TheFutureofWorkis….
§ TheFutureofServicesis….
§ TheFutureofWorkProcessesis….
§ Ahuge,unprecedentedopportunity fortheresearchcommunitytoadvanceourunderstanding,methodsandtechnologyunderpinningthesetransformationsanddisruptions!
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CognitiveCognitiveComputing
CognitiveAssistance
CognitiveServices
CognitiveBPM