Cognitive Enterprise Services

© 2015 IBM Corporation

Hamid R. Motahari-Nezhad

IBM Almaden Research Center

San Jose, CA

The Journey to Cognitive Enterprise IT Services: A Framework for Cognitive Services and Business Processes

Talk at University of New South Wales, Sydney, Australia. Nov. 29, 2016


Major Technology Trends Impacting Enterprise Business

2

Mobile Social

Cloud

Internet of Things

20162000


We are here

44 zettabytes

unstructured data

2010 2020

structured data

Data is the world’s new natural resource! (Ginni Rometti, IBM Shareholders Report, 2014)

We are here

Sensors & Devices

VoIP

Enterprise Data

Social Media

5


Mega Trends: Data, Cloud, and Mobile

4

80%of the world’s data

today is unstructured

90% of the world’s data was created in the

last two years

1 Trillionconnected devices

generate 2.5 quintillion bytes

data / day

3M+Apps on leading

App stores

By 2017The collective computing and storage capacity of smartphones will surpass all worldwide servers

48% of enterprises are moving to the cloud to replace on-premise, legacy technology today

72% of enterprises have at least one application running in the cloud, growing from 57% in 2012

The average enterprise uses 738 cloud services.


A new computing paradigm is emerging

Tabulating Systems Era

Programmable Systems Era

CognitiveSystems Era


Intelligent Assistance and Machine Learning - Landscape

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IPSoft’sAmelia


Cognitive Era

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Discovery & RecommendationProbabilisticBig DataNatural Language as the InterfaceIntelligent Options


Towards Computing-At-Scale as the Shared Characteristic of Recent Advances

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Scalable Computing overMassive Commodity Hardware

Building Stronger Super Computers

Cloud Computing

Crowd Computing

Advanced individual algorithms

Mass computing applied to AI Complex array of algorithms applied to make sense of data, and offer cognitive assistance

Big Data

Individual ML Algorithm

Cognitive Computing


Understands natural language and human communication

Adapts and learnsfrom user selections and responses

Generates and evaluatesevidence-based hypothesis

Cognitive System

1

2

3 Cognitive Systems do actively discover, learn and act

A Cognitive System offers computational capabilities typically based on Natural Language Processing (NLP), Machine Learning (ML), and reasoning chains, on large amount of data, which provides cognition powers that augment and scale human knowledge and expertise

Watson


ENTERPRISE SERVICES

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Enterprise Services

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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.”

Major Types of Service (provider perspective):• Computational/technology services• Business/Enterprise services• People Services

Service Offerings Definition &

Design

Service Sales Pursuit

Transition and Transformation

Service Delivery & Operation

Lifecycle of Enterprise (IT)

Services


Information Technology Service Models

Client ManagedProcure, Own, Install & Manage [CAPEX]

Vendor Managed in the CloudOn-Demand as a Pay as You Go (PAYG) price [OPEX]

Applications

Data

Runtime

Middleware

O/S

Virtualization

Servers

Storage

Networking

TraditionalIT

Applications

Data

Runtime

Middleware

O/S

Virtualization

Servers

Storage

Networking

IaaS

Infrastructureas a Service

Applications

Data

Runtime

Middleware

O/S

Virtualization

Servers

Storage

Networking

Applications

Data

Runtime

Middleware

O/S

Virtualization

Servers

Storage

Networking

Managed IaaSManaged

Infrastructureas a Service

Applications

Data

Runtime

Middleware

O/S

Virtualization

Servers

Storage

Networking

PaaS

Platformas a Service

Applications

Data

Runtime

Middleware

O/S

Virtualization

Servers

Storage

Networking

SaaS

Softwareas a Service

Customization, higher costs, slower time to valueStandardization, lower costs, faster time to valueStandardization, lower costs, faster time to value

Clie

nt M

anag

ed

Vendor Managed in the Cloud

Local, Dedicated Public

Workforce Perspective

StaffBody x Price x

Utilization

OutsourceBody x Price x

Utilization

DigitalWorkforce

(Bots + Body) x Price x Utilization

Clie

nt M

anag

ed …

….…

Vend

or M

anag

ed


Managed Information Services: From RFP to Transition and Delivery

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Opportunity Deal Deal Deal Checkpoints/ContractT&TSteady-StateRenewalIdentification Validation Qualification PursuitQA/RiskAnalysis Delivery

EngagementTransition&

Transformation RenewalSteady-StateDelivery

BusinessDevelopment

RFP Receipt

Week 1

• Team Formation, and assignment

• Control Matrix Preparation• Window of opportunity to ask

questions from client

Week 2-x RFP ResponseDeadline

Solution &Approvals in Place

• Proposal Writing• Client Presentation Preparation• RFP Response Items

…

• Detailed SOW Analysis• Baselines • SRM

• Solutioning• Reviews • Approvals

Control Matrix

SRM

FRM

Baselines

SOW

Solutioning• Proposal• Client Presentation• Attachments/schedules

Reviews and approvals

CSE PM

Transition and Transformation Plan

• Contract Writing • Contract Analysis

Service Pursuit Demystified: From RFP to Contract


Cognitive Enterprise IT Services Framework

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Prior Deals ServiceOfferings

Guidelines, methodologies

People Profiles

Lessons Learned

ServiceDelivery Data

Opportunity Deal Deal Deal Checkpoints/ContractT&TSteady-StateRenewalIdentification Validation Qualification PursuitQA/RiskAnalysis Delivery

EngagementTransition&

Transformation RenewalSteady-StateDelivery

BusinessDevelopment

Current DealsPipeline

Revenue & FinanceInformation

IntegrateandMaketheDataAvailableUsing Interfaces(APIs)Deal Information Management

Enable Reusing Deal Artifacts and Sharing KnowledgeDeal Team Analytics Find Expertise and Recommend Them

Deal Competitive Assessment Analyze Competitiveness based on Cost/Price

Deal Win Prediction Analytics to provide deal win prediction, and pipeline ranking

Sales Pipeline Revenue Prediction

Cognitive RFP, Proposal and Contract Analyzing RFPs to extract requirements, and author RFP Response, and Contract Drafts

Cognitive Solutioning Compose the set of service offerings that meets clients requirements


COGNITIVE RFP, RESPONSE AND CONTRACT

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Hamid R. Motahari Nezhad, Juan M. Cappi, Taiga Nakamura, Mu Qiao: RFPCog: Linguistic-Based Identification and Mapping of Service Requirements in Request for Proposals (RFPs) to IT Service Solutions. HICSS 2016: 1691-1700

©2010IBMCorporation©2016IBMCorporation

Inputandproblemstatement

§ RFPDocumentsaretextualdocumentssentbyservicerequestersdescribingtherequirementsforITservices– Therequirements arestated innatural language,withavariedformatingeneral

§ RFPpackagecontains10sor100sofdocument,eachwith100sofpagesdescribingvariousaspectsofexistingITenvironment(detailbaseline),andfuturestaterequirements

§ TherearehundredsofrequirementsstatedforeachITserviceineachRFPthatneedtobeidentifiedandanalyzed,includingwho’sresponsibility(serviceproviderorcustomer)istoperformeach

§ Differentclientsorganizethedocumentsandcontentdifferently,andusedifferentvocabularyandterminologytorefertoITservicesandrequirements

§ Identificationofwhatconstitutearequirementisverychallenging– Thestructure(organization)ofthedocument,thelanguageconstructofsentencesandalsoclientvocabularydiffers– Naturallanguagebydefinitioncanbeambiguous,documentshaveincompleteinformation,andexpertiseneededininterpretingandunderstandingrequirement


ExampleITServiceRequirements


ITServiceRequirementsAnalysis:theneedforameta-model

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“Service provider shall provide onsite Desktop Services dispatching resources on 24 hour a day, 7 day a week basis, for Supported Equipment and Supported Devices at all Client’s Service Locations, which locations may be modified from time to time by Client in accordance with the applicable Change Control Procedure”.

Responsible Party: Service ProviderVerb phrase: shall provideTopic/Service: Onsite Desktop ServicesSLA needs: 24 hour a day, 7 day a weekServices for: Supported Equipment and DevicesLocations: All Client’s Service Locations

Duration of service: <Contract term>


Requirementsexpressedindifferentformandstructures

ASubsection

Sub-requirements

SP’sRequirementIndicators

SPRequirements(Extractthese!)

ARequirement

Titleofthetable,potentiallyService Topic

[Customer]


Research Problems

§ Requirementsidentification–WhatstatementsconstitutearequirementinRFPdocuments?– Requirementsvssub-requirements?

§ Requirementstopicidentification(ITservices)–WhichITservicestheyaretalkingabout?

§ ServiceOfferingMapping- Solutioning–WhichITServiceOfferingsmeettheclientrequirements?

§ ContinueslearningthroughHumanfeedback– Howtomanagehumaninteractions,feedbackandadaptivelearning?

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FromRFP(RequestforProposal)toProposal:MethodologyOverview

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RFPDocumentsProcessing

RequirementsExtraction

ProviderOfferingMatching

SolutionComposition

ProposalResponse

AutomationPastRFPResponseMatching

Extracting requirement statements

from an RFPMatching past RFP

Responses for Reuse


RFPCog forCognitiveRFPAnalysis:Overview

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RFP DocumentsContract Documents

RequirementsIdentification

Service Catalogs

ITIL

Requirements-Driven Offerings Composition

Requirements-driven Technical Solutions Composition

SolutionPatternsCustomer

Service Vocabulary

SolutionsTaxonomy

ProviderOffering Taxonomy

What are client requirement statements?

What services offerings/solutions these requirements map to?

RequirementsTopicIdentificationandGrouping

What are in-scope and out-of-scope service?


RFP Docs Structure Analysis

Pattern-based

Requirement Candidate

Identification

NLP-based Deep

Learning for Requirement Identification

Machine Learning-

Based Topic Identification

Document

Table

Section

Paragraph

Sentence

Cell

In what section of what document is the requirement from?

Boundary Identification

Requirement Patterns

How does clients state requirements?

Patterns:•( [Subject] + (shall | must | is required to | … ) ) + Action Verb + …•[Subject] is/are responsible for …

• Where does a requirement start and end?

è What is a requirement span?

è Req., and Sub-req. identification

Recognize noun (phrases), verb (phrases), …

Requirement Features

Apply NLP techniques for recognition of

Who does what?

Word Dependencies and Implicit Feature

Identification

Topic/Service

What is the requirement about?• Linguistic-based

Requirement Focus Identification

• Topic-related Feature Extraction

Use Domain Knowledge • Provide Service Taxonomy• Information Technology Infrastructure Library (ITIL)• Customer Vocabulary extracted from Documents

Apply Supervised Learning using• Support Vector Machine• Logistic Regression

RFPCog: MethodStepsforRequirementsandTopicIdentification


CognitiveSolutioning - RequirementstoServiceOfferingsMapping

§ Foragivenrequirement(orrequirementgroup),thefocusistoidentifyserviceelements(atmultiplelevelofhierarchies)thatmaptotherequirements,andtheirsub-requirements– ITServiceCatalog-awarePhraseMatching– Consideringthebodytext,concepthierarchythroughastatistically-builtsemanticmodeltoidentifymatching

§ NovelMethodformatchingnounphrasesinrequirementsandofferings:a modified LongestCommonSequence(LCS)termmatcher.– OnemaindifferencewithothersimilaritymetricssuchCosineandJaccard isthattheLCSpreservestheorderoftokensinmatching,whileotherdon’t.

– Missingkeywordsinthetwophrasearepenalizedbasedontheimportanceofthekeyword

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Based_Similarity_Score=#LCS/Weighted_Denominator,whereWeighted_DenominatorisdefinedastheweightedsumofthenumberofmissingwordsintheE_Seq.

Final_Similarity_Score = Based_Similarity_Score * (1 – net_distance/C),in which C is a constant for the maximum length of noun phrases in the population,and NetDistance is the absolute difference in tokens order difference of the LCS inNP_Seq and E_Seq (caters for additional terms in between)

“Storagemanagementsolution”and“managementsolution”,keywords:storage,missingwords


ITRequirementstoCatalogMapping– InteractiveandExplorativeVisualization

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Experimental Results– RequirementsTopicIdentification

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ML-based Topic Classification Performance (TP Rate)

0.9518 0.87330.7587

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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


RelatedWork

§ TemplatedInformationextractionfromtext– StevenBird,EwanKlein,andEdwardLoper,NaturalLanguageProcessingwithPython,http://www.nltk.org/book/,visitedJuly2015.

– Ana-MariaPopescu,InformationExtractionfromUnstructuredWebText,PhDThesis,Uni.Washington,2007.

§ Extractionofrequirementsfromtextualsoftwaredescriptions(Concepts,andModelsaccordingtoSVBR-SemanticBusinessVocabularyandRules- ,andOPM- Object-ProcessMethodology,orLTL- linear-timetemporallogic)– Ashfa Umber,ImranSarwar Bajwa,M.AsifNaeem,NL-BasedAutomatedSoftwareRequirementsElicitationandSpecification,AdvancesinComputingandCommunications.CommunicationsinComputerandInformationScienceVolume191,Springer.2011,pp30-39.

– Dov Dori,NahumKorda,Avi Soffer,ShalomCohen,SMART:SystemModelAcquisitionfromRequirementsText,BusinessProcessManagement(BPM).LNCS.Vol.3080,2004,pp179-194.

– Shalini Ghosh,DanielElenius,Wenchao Li,PatrickLincoln,NatarajanShankar,Wilfried Steiner,ARSENAL:AutomaticRequirementsSpecificationExtractionfromNaturalLanguage,SRIINTERNATIONAL,14July2014.

§ ThisworkisthefirsttoinvestigatetheproblemofrequirementextractionfromnaturaltextinRFPdocuments,andspecificallythosefromservicesdomain– Evidence-basedtopicidentification– Novelconcept-based,andcognitivesimilaritymeasureforrequirements-offerings

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PREDICTIVE ANALYTICS FOR IT SERVICES DEALS

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Hamid R. Motahari Nezhad, Daniel B. Greenia, Taiga Nakamura, Rama Akkiraju:Health Identification and Outcome Prediction for Outsourcing Services Based on Textual Comments. IEEE SCC 2014: 155-162

Daniel B. Greenia, Mu Qiao, Rama Akkiraju (and Hamid R. Motahari Nezhad):A Win Prediction Model for IT Outsourcing Bids. SRII Global Conference 2014: 39-42

Peifeng Yin, Hamid R. Motahari Nezhad, Aly Megahed, Taiga Nakamura:AProgress Advisor for IT Service Engagements. SCC 2015: 592-599

Aly Megahed, Peifeng Yin, Hamid Reza Motahari Nezhad:An Optimization Approach to Services Sales Forecasting in a Multi-staged Sales Pipeline. SCC 2016: 713-719


Outsourcing Service Opportunities - Pipeline Management

§Service providers maintain and manage a pipeline of service opportunities to pursue.

§Service pursuit management is a very elaborative, time-consuming and resource-demanding process (for large deals, $10M+)

§ Effective pipeline management (pipeline prioritization) and maintaining a pipeline of healthy opportunities are key for service providers

–Opportunity win prediction–Opportunity health analysis

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Objective:BuildapredictivemodelforestimatingtheprobabilityofwinningstrategicITservicedeals,andrankingdealsinthepipeline


Sales Opportunity Data

§Quantitative information about the deal (categorical, and numerical)–Hundreds of numerical and categorical information about deals including client name, deal size (contract value), sales stage , sector, deal complexity, market analysis, quality and risk assessment, etc.

§Deal comments made by the sales team and also by technical solutioning team–Comments are made at time intervals (often weekly)–Comments are short, sometimes cryptic, with specific jargons–Often do not include full English sentences, sentences are connected (no punctuation), etc.

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Business and Technical Problems

§Predicting the outcome of an engagement by devising a predictive model that uses both quantitative and textual comments, and analyzing them to find predictive features.

–Predicting the outcome of the engagement based on quantitative and comments

–How early we can predict and with what accuracy–Pipeline ranking

§ Identifying the health of an engagement by looking at the textual comments that made by the sales team

–Engagement health: understanding the current status of the engagement by looking at the comments

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Win Prediction Model: Combined Quantitative and Qualitative Model

HistoricalQuantitative

Data

Scoreeachdealandproduceaprioritizedlistof

deals

Salesexecutivesreceiveprioritized

list

1)Deal12)Deal2

3)Deal3…

n)DealN

Currentpipelinedata

LogisticRegression&BayesianModel

HistoricalDealcomments

Comment-basedPredictionModel

Cmment-basedscores

Quantitative-basedscores

CombinePredictions

Extensivefeatureengineeringwithdefining

derivedfeatures

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Prioritization Performance Evaluation

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TheWinPredictionrankedlistisfrontloadedwithdealsthatarelikelytowin:70%ofwinsareintop40%ofthelist.

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Cum

. Fra

ctio

n of

Win

s

Cum. Fraction of Data

Randomly Prioritized Win Probability PrioritizedTCV prioritized Expected Revenue Prioritized


Deal Win Prediction using Comments

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Textual comment Pre-processing, andKey n-gram Selection

Sentiment-based Tag extractions

Correlation Analysis of Extracted tagsWith outcomes

Sentiment-basedTags

tags-basedOutcome Prediction

Model

Textual Features

(key n-grams)

Weighted Combined Outcome Prediction

Text-based Prediction Model Builder

Tag-basedPrediction Model Builder

Textual Feature (n-gram)SelectionTe

rm E

xtra

ctor

Sentiment-based Tag Extractor

Feature Preparation and Selection Module

Text-basedOutcome Prediction

Domain Vocabulary and Types

projectComments

New (open) project comments

projectComments(Training)

projectComments(Training)

CombinedPredicted Outcome

Sentiment-based Outcome Prediction

Hamid R. Motahari Nezhad, Daniel B. Greenia, Taiga Nakamura, Rama Akkiraju:Health Identification and Outcome Prediction for Outsourcing Services Based on Textual Comments. IEEE SCC 2014: 155-162


Illustration of the approachSentiment-based

Tag ExtractionComment

Text

Vocabulary

SPInternal BU

Partner

Competition

Customer

New tag computation, and tag-based Outcome Prediction

The set of terms identified as frequently appearing terms in from Loss Reason fields: Proposal, Price, Solution, Cost, … .

Phrase-Entity Relationship <subject, phrase: sentiment, object>: new sentiment

C1 C2 … … Cn

Text pre-processing, comment subset selection, text feature selection

C1 C2 … … Cn

…Prediction (Weighted)

Tag-basedPredictor

Sentiment-based features

ProjectEntities

Text-basedPredictor

Text features(n-gram)

Final Predicted Outcome

Comments score = ∑ s(i)* w_c(i), i is phrase with a sentiment in the updates(i): sentiment score of I, w_c(i): class memebership to

indicative terms

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Experiments

§ 4,105 historical engagement data over 3 years as the training set

§ Close to 500 in-flight engagement deals as the testing set

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Experiment Overall Accuracy

Win Prediction Accuracy

Win Prediction

Recall

Loss Prediction Accuracy

Loss Prediction

Recall

Free-form text 61.5% 72% 60% 51% 76%Text with Concept-

based Features70% 85% 61% 55% 81%

Text with Concept-based and Sentiment-

based Features

72.5% 87% 62.5% 58% 84%

© 2013 IBM Corporation37

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Predictive Value of the Number of comments - Win Outcome

Total Comments Predictive Comment

Evaluatinghowearly(#ofcomments,here)thepredictionmatchesthefinaloutcome:between1/3andhalfofthecomments

Afollowupanalysisshowsthatonlyin11%ofcasesthepredictionmaychangeasnewcommentsbecomeavailable


Combining Quantitative and Qualitative Analyses

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QuantitativeModelReliesonhistoricalattributesforhistoricaldeals

Comment-basedModelLeveragesdealteam“local”insightstogaugethetrajectoryofthecurrentdeal(microview).

Prob.OfWinning=Weight1 xQuantScore+Weight2 xQual Score

Quant Model

QualModel

Historical Sales Data

Current Deal logs

Modeloutputiscombinedusingweights(logisticregression).


Sentiment-based Deal Health Analysis

Historicalcomments

Breakthecommenttextintosentences

Week 1 Week 2 Week 3 … Week n

S1 S2 … Sm

Sentence-level Annotation

Comment-level Annotation

Comment-level Annotation

Comment-level Annotation …

Deal-levelHealthStatus

Win, Promising, Progress,NeutralWarning, Troubled, Loss

Weighted aggregation of scores

Mapping each labels to a score between -1 .. 0 .. +1

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Opportunity Health Analysis based on comments

§ Mapping each opportunity comment to a health status– “Promising”, “Progressing”, “Neutral”, “Warning”, “In-Jeopardy”

§ Examples– Price needs to be approved by WW– Customer has asked for some changes to the proposal– Client requirements are to be confirmed [early stages]– Agreement to proceed w/ Provider1 & Provider2– ABB accepted the proposal from Competitor– The issues with Partner has been resolved

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DealHealthAnalyticsToolOffersfunctionsforMonitoringthestatusofDealsasSellersCommentsarriveduringthequarter.


DEAL PROGRESS MONITORING

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P. Yin, H.R. Motahari-Nezhad, A. Megahed, T. Nakamurra, A Progress Advisor for IT Service Engagements. IEEE SCC 2015 (to appear).


Problem Definition and Objective

§ Limitations of the Win Prediction Model:Prediction is for eventual win or loss, not for the event of the deal being rolled over to the next quarter– Prediction is for eventual win or loss, not for the event of the deal being rolled over to the next quarter– There is no prediction capability for the outcome and timeline of milestones (key to deal success)– There is no idea on when key events (such as win or loss) would happen

§ Objective:– Building a model that gives analytical insights about the key events and milestones as well as the timeframe within which

they happen

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Analysis

§ Analysis shows that distribution of time intervals for the occurrence of key events and milestones decays exponentially

§ Longer time interval of no activity (event progression) leads to a higher chance of losing

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Time Unit

Em

piric

al P

roba

bilit

y

(c) Probability of Loss w.r.t. time unit

Geometric distribution


Methodology

§ Devise a Bernoulli based deal-specific process for the prediction of event time intervals– It identifies the probability of the occurrence of events and thus helps in understanding how fast or slow a deal is moving

forward– This model is used to learn the weights of deal attributes to compute the parameter of a geometric distribution for the next

event occurrence time interval

§ Bernoulli-Dirichlet Generative Process: models the type of occurred events: win, loss, Milestone update– It is trained to learn the weights of deal attributes to compute the parameters of a stochastic process that models the type

of next occurring event

§ Prediction– The model estimates the probability of different event types given the deal attributes X, and time interval T, i.e.,

probability that the given event may happen within the time interval T

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DEAL COMPETITIVENESS ASSESSMENT

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The basic premise to be used throughout the Deal Competitive Assessment is to be able to compare a given ‘CompareFrom’ source to available “Compare To” data sources through a standard method of peer selection, and to present the outputin a standard way globally

Tower/Service Scope

PeerCriteria

Peer Selection Criteria Compare To Sources

Bid Data

Market Data

Delivery Data

Dim

inis

hing

N

umbe

r of

Dat

a Sa

mpl

es

X1

X2

X3

X4

X5

Local Sources

CompareFromSources

Deal

Metric

Standard Global Representation

StandardModel

Offering

Standard Models

Offerings

Contract Prices Pricing

Deal Competitiveness Assessment

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Approach to Assessing Competitiveness

§Mine ‘similar’ prior deals and market benchmark data

§Determine the upper and lower bounds on unit costs and unit prices for each of the service involved in an IT service solution.

§Add things up to get upper and lower bounds, and assess the percentile of the given case.

§Create a case management solution, where:–Users can edit/add/remove services involved.–Users can see/change/add peer deals

§The key challenge is in determining ‘similarity’ among complex IT service solutions. We present an approach to derive close comparables in this effort

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Peer-Selection Filtering§ Boolean: Has global resources or not

§ Geographical: Where it was

§ Categorical: Won, lost, or either

§ Numerical: Quantity of services

§ Unstructured text: Attributes with long text descriptions, images, etc.

§ Timing: recent enough.

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Tuple: {service, # of units requested, $unit cost, $ unit price, geo deliver-from, geo deliver-to}

D1s1, 200, $44s2, 300, $2.88s3, 2000, $555s4, 1000, $674cs1, N/A, 10%cs2, N/A, 20%

D2s2, 200, $3.50s4, 3000, $500cs1, N/A, 12%cs2, N/A, 18%

D3s1, 500, $40s3, 1,500, $450cs1, N/A, 15%cs2, N/A, 22%

D4s2, 200, $3.50s4, 1500, $620cs1, N/A, 12%cs2, N/A, 18%


The System View of IT Service Solution Price Competitiveness Analysis

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Sales Pipeline Revenue Prediction Methodology Overview

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HistoricalWinConversion&

GrowthData

What future opportunities would come into the pipeline that will be

won by the end of the period (Growth)?

Wou

ld w

e w

in th

ese

oppo

rtuni

ties

(Con

vers

ion)

?

Non-LinearOptimization

Model

LinearOptimization

Model

OptimalWeights

OptimalNo.of

HistoricalPeriodstoUse(N)

CurrentPipeline

RevenuePrediction(Conversion&Growth

ApplyWeightsonNHistorical

ConversionandGrowthRates

ApplyRatestoCurrentPipeline

Objective: Predicting the revenue of sales pipeline for different sales stages

Aly Megahed, Peifeng Yin, Hamid Reza Motahari Nezhad:AnOptimization Approach to Services Sales Forecasting in a Multi-staged Sales Pipeline. SCC 2016: 713-719


FROM SERVICES TO COGS, AND TO COGNITIVE BPM

What advances in AI and Machine Learning mean for Service Computing and BPM?

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Service Computing: From API to CCL

§ The End of using API for Programming Business Logic– APIs will be used to initiate Cogs (Intelligent Bots)– The Business Transaction to be performed in Conversations with Cogs

§ Cogs representing Providers/Consumers, spanning over a spectrum:– From Cogs taking over the interface of existing Apps– To Cogs codifying and understanding the business logic and engaging in

conversations to transact

§ Cog Conversation Language (CCL)– CCL should provide support for defining a rich natural language conversations for a

Cog to deliver business functionalities to the users (other Cogs, and Humans)• The Language to Program Cogs• An initial example is Watson Dialog Services Template Language

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Source: blog.cloudsecurityalliance.org


The notion of Service/People Composition to be Re-Defined

§ In current Hybrid composition/mashup (People, Services) methods:

– Services are represented with API calls– People are integrated with Human Tasks (GUI

is the interaction paradigm)– Composition methods are finding deterministic

models of interactions, defined apriori

§ We are moving towards dynamic composition of cogs and human in which

– Cogs are participating in NL conversations– Human are approached through messaging

and natural language– Composition are performed dynamically during

the conversation, require non-deterministic models, defined in online and on-demand model

53

Weather Cog

Health Agent

PersonalityInsight Cog.

ProviderCogs

Travel Cog 1

Travel Cog 2

Planning a VacationTrip

Considering preferences, experience, conditions, cost, Availability, etc.

Mediated and facilitated by Cogs

Human-Cog interaction

Cog-Cog interactionNatural Language

Natural Language, CCL,(ACL, KQML, etc.)?

ACL: Agent Communication Language, KQML, etc.


The App Composition (Mashup) is already moving away from explicit API calls

§ Implicit Data Sharing with the notion of Central Shared Context on Mobile Platforms

– Events– Notifications– Metadata descriptions

§ Google Now on Tap (implicit integration)– Central Shared Context

§ Apple Proactive

54


Process Automation Stages in Enterprise & in IT Services

Humans (Manual)

Program/ Workflow

Robotics (RPA)

Cognitive

55

Issues Current Enterprises facing• High volume of manual processes• With high variability• Involving unstructured data

“85% of a typical firm’s 900+ processescan be automated.”

High Cost of Automation using Traditional

Approaches (to go from 50% to 85%)


Historical and Future Perspectives on BPM

56

Databases

Back

end

\Sy

stem

sLa

yer

Self-Generating Integration

SAP usingjava

API

WebService

API

Excel using com

API

MSMQ usingcom or java

API

Databases usingjdbc

API

Busi

ness

Rule

sLa

yer

Production Business Level

Objects

Business Level Objects

Inv oicesBusiness Lev el

Obj ects

AFE’sBusiness Level

ObjectsAnything


Proc

ess

Laye

r

Any Process

General Workflow System and User InteractionsCalculation

Inte

rface

Laye

r Web Service

Presentation Presentation

XML

API

Back

end

\Sy

stem

sLa

yer


SAP usingjava

API

SAP usingjava

API

WebService

API

WebService

API

Excel using com

API

Excel using com

API


API


API

Databases usingjdbc

API

Databases usingjdbc

API

Busi

ness

Rule

sLa

yer


Objects



Obj ects


ObjectsAnything


Proc

ess

Laye

r

Any Process


Inte

rface

Laye

r Web Service

PresentationPresentation PresentationPresentation

XML

API

XML

API

BPMS

TQM

General WorkflowBPR

BPM

time

ERP

WFMEAI

‘85 ‘90 ‘95 ‘05‘00‘98

IT Innovations

Management Concepts

DatabasesDatabases

Back

end

\Sy

stem

sLa

yer


SAP usingjava

API

WebService

API

Excel using com

API


API

Databases usingjdbc

API

Busi

ness

Rule

sLa

yer


Objects



Obj ects


ObjectsAnything


Proc

ess

Laye

r

Any Process


Inte

rface

Laye

r Web Service


XML

API

Back

end

\Sy

stem

sLa

yer


SAP usingjava

API

SAP usingjava

API

WebService

API

WebService

API

Excel using com

API

Excel using com

API


API


API

Databases usingjdbc

API

Databases usingjdbc

API

Busi

ness

Rule

sLa

yer


Objects



Obj ects


ObjectsAnything


Proc

ess

Laye

r

Any Process


Inte

rface

Laye

r Web Service


XML

API

XML

API

BPMS

Back

end

\Sy

stem

sLa

yer


SAP usingjava

API

WebService

API

Excel using com

API


API

Databases usingjdbc

API

Busi

ness

Rule

sLa

yer


Objects



Obj ects


ObjectsAnything


Proc

ess

Laye

r

Any Process


Inte

rface

Laye

r Web Service


XML

API

Back

end

\Sy

stem

sLa

yer


SAP usingjava

API

SAP usingjava

API

WebService

API

WebService

API

Excel using com

API

Excel using com

API


API


API

Databases usingjdbc

API

Databases usingjdbc

API

Busi

ness

Rule

sLa

yer


Objects



Obj ects


ObjectsAnything


Proc

ess

Laye

r

Any Process


Inte

rface

Laye

r Web Service


XML

API

XML

API

BPMS

TQMTQM

General WorkflowBPR

General WorkflowBPR

BPMBPMBPM

time

ERPERP

WFMWFMEAIEAI

‘85 ‘90 ‘95 ‘05‘00‘98

IT Innovations

Management Concepts

Ref: Ravesteyn, 2007

‘16

Social BPM

iBPMS: Business Process Analytics

‘2021

The Future of BPM is also Cognitive

Dark Data

Cognitive BPM

CognitiveAnalytics

CognitiveProcesses

Interact

LearnEnact

Cognitive Capabilities


Dark Data: digital footprint of people, systems, apps and IoT devices

§ Handling and managing work (processes) involves interaction among employees, systems and devices

§ Interactions are happing over email, chat, messaging apps, and

§ There are descriptions of processes, procedures, policies, laws, rules, regulations, plans, external entities such as customers, partners and government agenies, surrounding world, news, social networks, etc.

§ The need for activities over interactions of people, systems, and IoT devices to be coordinate

57

Citizens

Assistant

BusinessEmployees/

agents

Plans

Rules

PoliciesRegulations

TemplatesInstructions/Procedures

ApplicationsSchedulesCommunications such as email, chat, social media, etc.

Organization

Dark Data: Unstructured Linked InformationIoT Devices and Sensors


Spectrum of Work: Processes and Cognitive

58

Structured Processes

Unstructured ProcessesKnowledge-based

Routine

Existing Technology

Dark Data: Mobile, Social, Communication (email, voice, video), Documents, Notes, Sensors

BPMEngines

WorkflowEngines

CaseManagement

GroupwareKnowledge-Intensive

Processes

Email, Chat, MessagingAd-hoc, unstructured

Processes

Cognitive Process Management

Conversational Interface for Processes

Cognitive Process Learning

Cognitive Process Analytics

Cognitive Enactment


Cognitive BPM Systems

§ A Cognitive BPM system is a cognitive system that provides cognitive support in all phases of a process lifecycle over structured and unstructured information sources, and is able to continuously discover, learn and proactively act to support achieving a desired outcome

– It offers cognitive interaction and analytics support over structured processes

– For unstructured processes, it offers intelligent and integrated process (model) definition, reasoning and adaptation

• Process is not assumed apriori defined; but is discovered, learned and customized based on accumulated knowledge and experience

–It continually learns to improve the process

59


Cognitive BPM Lifecycle

60

CognitiveBPMS

Define

Enact

Monitor

Analyze

Next Steps, Adapt

Interact

Sense

Learn, Discover

To

Traditional BPMCognitive BPM


Cognitively-Enabled Processes: Shifting process lifecyclefrom Define-Execute-Analyze-Improve to Plan-Act-Learn

§ For each enactment of the overall process, many iterations around this loop

§ At a given time, multiple goals & sub-goals may be active– Numerous threads of activity– Each thread modeled essentially as a “case” as in Case Mgmt– Cf. [Vaculin et al, 2013]

§ As new information arrives the cycle might re-start for some or all threads

– Planning based on new info• New goal formulation• Planning to achieve those goals

§ “Cognitive Agent” helps by– Perform the planning– Learn from large volumes of structured/unstructured data– Over time, learn best practices and incorporate into planning

Plan / Decide

Act<<World Effect>>Learn

Richard Hull, Hamid R. Motahari Nezhad: Rethinking BPM in a Cognitive World: Transforming How We Learn and Perform Business Processes. BPM 2016: 3-19


Towards Cognitive BPM: Example Scenarios

62

Example (1): Integrate IBM BPM with IBM Watson

http://www.ibm.com/developerworks/bpm/library/techarticles/1501_mehra-bluemix/1501_mehra.html#N1009D

Email, Chat, and Calendaring apps are the most used channels for doing work in the enterprise

Addressing the work organization and management for Knowledge workers: monitoring communication channels (email, chat), and:

- Capturing, prioritizing and organizing work of a worker

- Identifying actionable statements (requests, commitments, questions) and track them over the course of conversations

Example (2): eAssistant for Knowledge Workers

© 2013 IBM Corporation63

Inbox - Verse Highlighting actionable statements Recommending fulfilment actions

IBM Insight 2015 – The session on “Given your collaboration tools a brain”

© 2013 IBM Corporation64 IBM Insight 2015 – The session on “Given your collaboration tools a brain”

Send File Action Archetype Send File Action Archetype Send File Action Archetype

© 2013 IBM Corporation65 IBM Insight 2015 – The session on “Given your collaboration tools a brain”

Invite/Calendar Action Archetype Automated Invite Parameters Extraction Calendar Entry Creation


eAssistant App and APIs

66

Watson (& BigInsight NLP) Apps and Services on BlueMix

Colla

bora

tion

Tool

s

Enterprise Repositories, Applications and Data Sources

FeedsRepositories

Document collections

…

eAssistant Apps

Personal Knowledge

Graph Builder

Conversation Analytics, Auto-Response,

Prioritization

Calendar and Scheduling Assistant

Cognitive Process Learning

To-do, Task and Process

Assistant

Cognitive Work Assistant APIs

Semantic Role Labeling POS tagging Dependency

AnalysisCo-reference

resolutionNamed Entity Recognition

Knowledge GraphBuilder

H. R. M. Nezhad. Cognitive assistance at work. In AAAI Fall Symposium Series. AAAI Publications, November, 2015.


Cognitive BPM: Selected research challenges§Cognitive process learning:

4Knowledge acquisition methods from unstructured information (text, image, etc.)4Combine with traditional process mining on logs4Building actionable knowledge graphs & executable code

§Cognitively enabled processes: Plan-Act-Learn4Blending of “model” and “instance”4Recognizing goals from digital exhaust and process history4Advances in planning research – incremental, multi-threaded activity, richer goal

languages, prioritized and soft goals, …4Enough uniformity to support reporting, identification of best practices

§Cognitive Assistants for business processes4Assist workers across numerous tasks, including process management & optimization4Interactive learning where cognitive agents ask process questions4Gradual learning through experience, and process improvement


Summary

§ The Future of Computing is ….

§ The Future of Work is ….

§ The Future of Services is ….

§ The Future of BPM is ….

§ A huge, unprecedented opportunity for the research community to advance our understanding, methods and technology underpinning these transformations and disruptions!

68

CognitiveCognitive Computing

Cognitive Assistance

Cognitive Services

Cognitive BPM


QUESTIONS? Thank You!

69


Model of Human Administrative Assistants: conceptual framework

70 T. Erickson, etc.: Assistance: The Work Practice of Human Administrative Assistants and their Implications for IT and Organizations, CSCW’08.

Blocking, Doing, Redirecting

Key to the performance of Assistants


Cognitive BPM in Cognitive Assistants/Agents

§ Goals– Increasing worker’s productivity, efficiency, and creativity (serendipity)

§ Current cognitive assistants are focused on personal space or virtual conversational agents

§ Cognitive Work Agent– Is process and work aware– Monitors worker’s input channels and interactions (emails, chats, social connections, external and

internal environment, knows rules, policies and processes)– Proactively acts on worker’s behalf and reacts to requests: becomes a copy of you in work environment

• Commands/requests - Responds to simple requests intelligently• Situational awareness – monitors the environments to overcome information overloading (selective).• Deep QA: process questions, how-tos, previous successful process experience

– Organizes and assists your work• Extract tasks/commitments, promises, commitments• Managed to-dos: status updates, over-dues, plans• Manages calendar, schedules, social contacts• Finds and present prior related interactions to a particular conversation

– Learns how work gets done, and can take care of them for their human subject

71


Cognitive Assistant

§ A software agent (cog) that – “augments human intelligence” (Engelbart’s definition1 in 1962)– Performs tasks and offer services (assists human in decision making and taking actions)– Complements human by offering capabilities that is beyond the ordinary power and reach of human (intelligence

amplification)

§ A more technical definition– Cognitive Assistant offers computational capabilities typically based on Natural Language Processing (NLP),

Machine Learning (ML), and reasoning chains, on large amount of data, which provides cognition powers that augment and scale human intelligence

§ Getting us closer to the vision painted for human-machine partnership in 1960:– “The hope is that, in not too many years, human brains and computing machines will be coupled together very

tightly, and that the resulting partnership will think as no human brain has ever thought and process data in a way not approached by the information handling machines we know today”

“Man-Computer Symbiosis , J. C. R. Licklider IRE Transactions on Human Factors in Electronics, volume HFE-1, pages 4-11, March 1960

72 1 Augmenting Human Intellect: A Conceptual Framework, by Douglas C. Engelbart, October 1962

Data & Analytics

Cognitive Enterprise Services