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 Trillion
connected devices
generate 2.5
quintillion bytes
data / day
3M+
Apps on leading
App stores
By 2017
The 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
Cognitive
Systems Era

Intelligent Assistance and Machine Learning - Landscape
6
IPSoft’s
Amelia

Cognitive Era
7
Discovery & Recommendation
Probabilistic
Big Data
Natural Language as the Interface
Intelligent Options

Towards Computing-At-Scale as the Shared Characteristic of Recent Advances
8
Scalable Computing over
MassiveCommodity 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
MLAlgorithm
Cognitive Computing

Understands
natural language
and human
communication
Adapts and learns
from user
selections and
responses
Generates and
evaluates
evidence-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
10

Enterprise Services
11
A. Service Provider
• Individual
• Institution
• Public or Private
C. Service Target: The reality to be
transformedor 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 of
Ownership Relationship
(B on C)
Forms of
Service Relationship
(A & B co-create value)
Forms of
Responsibility Relationship
(A on C)
Forms of
Service 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 Managed
Procure, Own, Install & Manage [CAPEX]
Vendor Managed in the Cloud
On-Demand as a Pay as You Go (PAYG) price [OPEX]
Applications
Data
Runtime
Middleware
O/S
Virtualization
Servers
Storage
Networking
Traditional
IT
Applications
Data
Runtime
Middleware
O/S
Virtualization
Servers
Storage
Networking
IaaS
Infrastructure
as a Service
Applications
Data
Runtime
Middleware
O/S
Virtualization
Servers
Storage
Networking
Applications
Data
Runtime
Middleware
O/S
Virtualization
Servers
Storage
Networking
Managed IaaS
Managed
Infrastructure
as a Service
Applications
Data
Runtime
Middleware
O/S
Virtualization
Servers
Storage
Networking
PaaS
Platform
as a Service
Applications
Data
Runtime
Middleware
O/S
Virtualization
Servers
Storage
Networking
SaaS
Software
as a Service
Customization, higher costs, slower time to value
Standardization, lower costs, faster time to valueStandardization, lower costs, faster time to value
ClientManaged
VendorManagedintheCloud
Local, Dedicated Public
Workforce Perspective
Staff
Body x Price x
Utilization
Outsource
Body x Price x
Utilization
Digital
Workforce
(Bots + Body) x
Price x Utilization
ClientManaged…….…VendorManaged

Managed Information Services: From RFP to Transition and Delivery
13
Opportunity Deal Deal Deal Checkpoints/ Contract T&T Steady-State Renewal
Identification Validation Qualification Pursuit QA/Risk Analysis Delivery
Engagement
Transition &
Transformation Renewal
Steady-State
Delivery
Business
Development
RFP
Receipt
Week 1
• Team Formation, and
assignment
• Control Matrix Preparation
• Window of opportunity to ask
questions from client
Week 2-x RFP Response
Deadline
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
14
Prior Deals Service
Offerings
Guidelines,
methodologies
People
Profiles
Lessons
Learned
Service
Delivery Data
Opportunity Deal Deal Deal Checkpoints/ Contract T&T Steady-State Renewal
Identification Validation Qualification Pursuit QA/Risk Analysis Delivery
Engagement
Transition &
Transformation Renewal
Steady-State
Delivery
Business
Development
Current Deals
Pipeline
Revenue & Finance
Information
Integrate and Make the Data Available Using Interfaces (APIs)
Deal Information Management
Enable Reusing Deal Artifacts and Sharing Knowledge
Deal 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
15
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

© 2010 IBM Corporation© 2016 IBM Corporation
Input and problem statement
§ RFP Documents are textual documents sent by service requesters describing the requirements for IT services
– The requirements are stated in natural language, with a varied format in general
§ RFP package contains 10s or 100s of document, each with 100s of pages describing various aspects of existing IT
environment (detail baseline), and future state requirements
§ There are hundreds of requirements stated for each IT service in each RFP that need to be identified and analyzed,
including who’s responsibility (service provider or customer) is to perform each
§ Different clients organize the documents and content differently, and use different vocabulary and terminology to
refer to IT services and requirements
§ Identification of what constitute a requirement is very challenging
– The structure (organization) of the document, the language construct of sentences and also client vocabulary differs
– Natural language by definition can be ambiguous, documents have incomplete information, and expertise needed in interpreting and
understanding requirement

Example IT Service Requirements

IT Service Requirements Analysis: the need for a meta-model
18
“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 Provider
Verb phrase: shall provide
Topic/Service: OnsiteDesktopServices
SLAneeds: 24 hour a day, 7 day a week
Services for: Supported Equipment and Devices
Locations:All Client’s Service Locations
Duration of service: <Contract term>

Requirements expressed in different form and structures
A Subsection
Sub-requirements
SP’s Requirement
Indicators
SP Requirements
(Extract these!)
A Requirement
Title of the table, potentially
Service Topic
[Customer]

Research Problems
§ Requirements identification
– What statements constitute a requirement in RFP documents?
– Requirements vs sub-requirements?
§ Requirements topic identification (IT services)
– Which IT services they are talking about?
§ Service Offering Mapping - Solutioning
– Which IT Service Offerings meet the client requirements?
§ Continues learning through Human feedback
– How to manage human interactions, feedback and adaptive learning?
20

From RFP (Request for Proposal) to Proposal: Methodology Overview
21
RFP
Documents
Processing
Requirements
Extraction
Provider
Offering
Matching
Solution
Composition
Proposal
Response
AutomationPast RFP
Response
Matching
Extracting
requirement
statements
from an RFP
Matching
past RFP
Responses
for Reuse

RFPCog for Cognitive RFP Analysis: Overview
22
RFP Documents
Contract Documents
Requirements
Identification
Service Catalogs
ITIL
Requirements-
Driven Offerings
Composition
Requirements-driven
Technical Solutions
Composition
Solution
Patterns
Customer
Service Vocabulary
Solutions
Taxonomy
Provider
Offering
Taxonomy
What are client
requirement
statements?
What services
offerings/solutions these
requirements map to?
Requirements
Topic
Identification and
Grouping
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 sectionof
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: Method Steps for Requirements and Topic Identification

Cognitive Solutioning - Requirements to Service Offerings Mapping
§ For a given requirement (or requirement group), the focus is to identify service elements (at multiple level of
hierarchies) that map to the requirements, and their sub-requirements
– IT Service Catalog-aware Phrase Matching
– Considering the body text, concept hierarchy through a statistically-built semantic model to identify matching
§ Novel Method for matching noun phrases in requirements and offerings: a modified Longest Common Sequence
(LCS) term matcher.
– One main difference with other similarity metrics such Cosine and Jaccard is that the LCS preserves the order of tokens in
matching, while other don’t.
– Missing keywords in the two phrase are penalized based on the importance of the keyword
24
Based_Similarity_Score= #LCS / Weighted_
Denominator, where Weighted_ Denominator is
defined as the weighted sum of the number of
missing words in the E_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 in
NP_Seq and E_Seq (caters for additional terms in between)
“Storage management solution”
and “management solution”,
keywords: storage, missing
words

IT Requirements to Catalog Mapping – Interactive and Explorative Visualization
25

Experimental Results – Requirements Topic Identification
26
ML-based Topic Classification Performance (TP Rate)
0.9518 0.8733
0.7587
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
TPRate
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

Related Work
§ Templated Information extraction from text
– Steven Bird, Ewan Klein, and Edward Loper, Natural Language Processing with Python, http://www.nltk.org/book/, visited July
2015.
– Ana-Maria Popescu, Information Extraction from Unstructured Web Text, PhD Thesis, Uni. Washington, 2007.
§ Extraction of requirements from textual software descriptions (Concepts, and Models according to SVBR -
Semantic Business Vocabulary and Rules- , and OPM - Object-Process Methodology, or LTL - linear-time
temporal logic)
– Ashfa Umber, Imran Sarwar Bajwa, M. Asif Naeem, NL-Based Automated Software Requirements Elicitation and Specification,
Advances in Computing and Communications. Communications in Computer and Information Science Volume 191, Springer.
2011, pp 30-39.
– Dov Dori, Nahum Korda, Avi Soffer, Shalom Cohen, SMART: System Model Acquisition from Requirements Text, Business
Process Management (BPM). LNCS. Vol. 3080, 2004, pp 179-194.
– Shalini Ghosh, Daniel Elenius, Wenchao Li, Patrick Lincoln, Natarajan Shankar, Wilfried Steiner, ARSENAL: Automatic
Requirements Specification Extraction from Natural Language, SRI INTERNATIONAL, 14 July 2014.
§ This work is the first to investigate the problem of requirement extraction from natural text in RFP documents,
and specifically those from services domain
– Evidence-based topic identification
– Novel concept-based, and cognitive similarity measure for requirements-offerings
27

PREDICTIVE ANALYTICS FOR IT
SERVICES DEALS
28
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:AProgressAdvisor 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
29
Objective: Build a predictive model for estimating the probability of winning strategic IT service deals,
and ranking deals in the pipeline

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.
13

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
14

Win Prediction Model: Combined Quantitative and Qualitative Model
Historical
Quantitative
Data
Score each deal
and produce a
prioritized list of
deals
Sales executives
receive prioritized
list
1) Deal 1
2) Deal 2
3) Deal 3
…
n) Deal N
Current pipeline
data
Logistic
Regression
& Bayesian
Model
Historical Deal
comments
Comment-based
Prediction Model
Cmment
-based
scores
Quanti
tative-
based
scores
Combine
Predictions
Extensive feature
engineering with defining
derived features
15

Prioritization Performance Evaluation
33
The Win
Prediction
ranked list is
frontloaded
with deals
that are
likely to win:
70% of wins
are in top
40% of the
list.
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Cum.FractionofWins
Cum. Fraction of Data
Randomly Prioritized Win Probability Prioritized
TCV prioritized Expected Revenue Prioritized

Deal Win Prediction using Comments
34
Textual comment
Pre-processing, and
Key n-gram Selection
Sentiment-based
Tag extractions
Correlation Analysis of
Extracted tags
With outcomes
Sentiment-based
Tags
tags-based
Outcome Prediction
Model
Textual
Features
(key n-grams)
Weighted Combined Outcome Prediction
Text-based Prediction
Model Builder
Tag-based
Prediction Model Builder
Textual Feature (n-gram)
Selection
TermExtractor
Sentiment-based
Tag Extractor
Feature Preparation and Selection Module
Text-based
Outcome Prediction
Domain
Vocabulary
and Types
project
Comments
New (open) project
comments
project
Comments
(Training)
project
Comments
(Training)
Combined
Predicted
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 approach
Sentiment-based
Tag Extraction
Comment
Text
Vocabulary
SP
Internal 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-based
Predictor
Sentiment-
based features
Project
Entities
Text-based
Predictor
Text features
(n-gram)
Final
Predicted
Outcome
Comments score = ∑ s(i)* w_c(i), i is phrase with a sentiment in the update
s(i): sentiment score of I, w_c(i): class memebership to
indicative terms
18

Experiments
§ 4,105 historical engagement data over 3 years as the training set
§ Close to 500 in-flight engagement deals as the testing set
36
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 Features
70% 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
Evaluating how early (# of
comments, here) the prediction
matches the final outcome:
between 1/3 and half of the
comments
A follow up analysis shows that
only in 11% of cases the prediction
may change as new comments
become available

Combining Quantitative and Qualitative Analyses
38
Quantitative Model
Relies on historical attributes for
historical deals
Comment-based Model
Leverages deal team “local” insights
to gauge the trajectory of the current
deal (micro view).
Prob. Of Winning = Weight1 x Quant Score + Weight2 x Qual Score
Quant
Model
Qual
Model
Historica
l Sales
Data
Current
Deal
logs
Model output is
combined using
weights (logistic
regression).

Sentiment-based Deal Health Analysis
Historical
comments
Break the comment
text into sentences
Week 1 Week 2 Week 3 … Week n
S1 S2 … Sm
Sentence-level
Annotation
Comment-level
Annotation
Comment-level
Annotation
Comment-level
Annotation …
Deal-level Health Status
Win, Promising, Progress,
Neutral
Warning, Troubled, Loss
Weighted aggregation of scores
Mapping each labels to a score between -1 .. 0 .. +1
23

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
40
Deal Health Analytics Tool Offers functions for Monitoring the status of Deals as Sellers Comments arrive during
the quarter.

DEAL PROGRESS MONITORING
41
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 eventualwin or loss, not for the event of the deal being rolled over to the next quarter
– Prediction is for eventualwin 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
42

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
43
Time Unit
EmpiricalProbability
(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
44

DEAL COMPETITIVENESS ASSESSMENT
45

The basic premise to be used throughout the Deal Competitive Assessment is to be able to compare a given ‘Compare
From’ source to available “Compare To” data sources through a standard method of peer selection, and to present the output
in a standard way globally
Tower/Service Scope
Peer
Criteria
Peer Selection Criteria Compare To Sources
Bid Data
Market Data
Delivery Data
DiminishingNumberofDataSamples
X1
X2
X3
X4
X5
Local Sources
Compare
From
Sources
Deal
Metric
Standard Global Representation
Standard
Model
Offering
Standard Models
Offerings
Contract Prices Pricing
Deal Competitiveness Assessment
30

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
47

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.
48
Tuple: {service, # of units requested, $unit cost,
$ unit price, geo deliver-from, geo deliver-to}
D1
s1, 200, $44
s2, 300, $2.88
s3, 2000, $555
s4, 1000, $674
cs1, N/A, 10%
cs2, N/A, 20%
D2
s2, 200, $3.50
s4, 3000, $500
cs1, N/A, 12%
cs2, N/A, 18%
D3
s1, 500, $40
s3, 1,500, $450
cs1, N/A, 15%
cs2, N/A, 22%
D4
s2, 200, $3.50
s4, 1500, $620
cs1, N/A, 12%
cs2, N/A, 18%

The System View of IT Service Solution Price Competitiveness Analysis
33

Sales Pipeline Revenue Prediction Methodology Overview
50
Historical
Win Conversion &
Growth Data
What future opportunities would
come into the pipeline that will be
won by the end of the period
(Growth)?
Wouldwewinthese
opportunities(Conversion)?
Non-Linear
Optimization
Model
Linear
Optimization
Model
Optimal
Weights
Optimal
No. of
Historical
Periods to
Use (N)
Current
Pipeline
Revenue
Prediction
(Conversion
& Growth
Apply Weights on
N Historical
Conversion and
Growth Rates
Apply
Rates to
Current
Pipeline
Objective: Predicting the revenue of sales pipeline for different sales stages
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

FROM SERVICES TO COGS, AND TO COGNITIVE
BPM
What advances in AI and Machine Learning
mean for Service Computing and BPM?
51

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
52
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
Personality
Insight Cog.
Provider
Cogs
Travel Cog 1
Travel Cog 2
Planning a Vacation
Trip
Considering preferences,
experience, conditions, cost,
Availability, etc.
Mediated and facilitated by Cogs
Human-Cog interaction
Cog-Cog interaction
Natural 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+ processes
can be automated.”
High Cost of Automation
using Traditional
Approaches (to go from
50% to 85%)

Historical and Future Perspectives on BPM
56
Databases
BackendSystems
Layer
Self-Generating Integration
SAP using
java
API
Web
Service
API
Excel using
com
API
MSMQ using
com or java
API
Databases using
jdbc
API
Business
Rules
Layer
Production
Business Level
Objects
Business Level Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
General Workflow System and UserInteractionsCalculation
Interface
Layer
Web
Service
Presentation Presentation
XML
API
BackendSystems
Layer
SAP using
java
API
SAP using
java
API
Web
Service
API
Web
Service
API
Excel using
com
API
Excel using
com
API
MSMQ using
com or java
API
MSMQ using
com or java
API
Databases using
jdbc
API
Databases using
jdbc
API
Business
Rules
Layer
Production
Business Level
Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
Interface
Layer
Web
Service
PresentationPresentation PresentationPresentation
XML
API
XML
API
BPMS
TQM
General Workflow
BPR
BPM
time
ERP
WFM
EAI
‘85 ‘90 ‘95 ‘05‘00‘98
IT Innovations
Management Concepts
DatabasesDatabases
BackendSystems
Layer
SAP using
java
API
Web
Service
API
Excel using
com
API
MSMQ using
com or java
API
Databases using
jdbc
API
Business
Rules
Layer
Production
Business Level
Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
Interface
Layer
Web
Service
XML
API
BackendSystems
Layer
SAP using
java
API
SAP using
java
API
Web
Service
API
Web
Service
API
Excel using
com
API
Excel using
com
API
MSMQ using
com or java
API
MSMQ using
com or java
API
Databases using
jdbc
API
Databases using
jdbc
API
Business
Rules
Layer
Production
Business Level
Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
Interface
Layer
Web
Service
XML
API
XML
API
BPMS
BackendSystems
Layer
SAP using
java
API
Web
Service
API
Excel using
com
API
MSMQ using
com or java
API
Databases using
jdbc
API
Business
Rules
Layer
Production
Business Level
Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
Interface
Layer
Web
Service
XML
API
BackendSystems
Layer
SAP using
java
API
SAP using
java
API
Web
Service
API
Web
Service
API
Excel using
com
API
Excel using
com
API
MSMQ using
com or java
API
MSMQ using
com or java
API
Databases using
jdbc
API
Databases using
jdbc
API
Business
Rules
Layer
Production
Business Level
Objects
Inv oices
Business Lev el
Obj ects
AFE’s
Business Level
Objects
Anything
Business Level
Objects
Process
Layer
Any Process
Interface
Layer
Web
Service
XML
API
XML
API
BPMS
TQMTQM
General Workflow
BPRGeneral Workflow
BPR
BPMBPMBPM
time
ERPERP
WFMWFM
EAIEAI
‘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
Cognitive
Analytics
Cognitive
Processes
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
Business
Employees/
agents
Plans
Rules
Policies
Regulations
TemplatesInstructions/
Procedures
ApplicationsSchedules
Communications such as
email, chat, social media,
etc.
Organization
Dark Data: Unstructured Linked Information
IoT Devices and Sensors

Spectrum of Work: Processes and Cognitive
58
Structured Processes
Unstructured Processes
Knowledge-based
Routine
Existing Technology
Dark Data: Mobile, Social, Communication (email, voice, video), Documents, Notes, Sensors
BPM
Engines
Workflow
Engines
Case
Management
Groupware
Knowledge-Intensive
Processes
Email, Chat, Messaging
Ad-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
Cognitive
BPMS
Define
Enact
Monitor
Analyze
Next Steps, Adapt
Interact
Sense
Learn,
Discover
To
Traditional BPM
Cognitive BPM

Cognitively-Enabled Processes: Shifting process lifecycle
from 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

Inbox - Verse Highlighting actionable statements Recommending fulfilment actions
IBM Insight 2015 – The session on “Given your collaboration tools a brain”

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

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

eAssistant App and APIs
66
Watson (& BigInsight NLP) Apps and Services on BlueMix
CollaborationTools
Enterprise Repositories, Applications and Data Sources
Feeds
Repositories
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
Analysis
Co-reference
resolution
Named Entity
Recognition
Knowledge
Graph
Builder
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 logs
4Building actionable knowledge graphs & executable code
§ Cognitively enabled processes: Plan-Act-Learn
4Blending of “model” and “instance”
4Recognizing goals from digital exhaust and process history
4Advances 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 processes
4Assist workers across numerous tasks, including process management & optimization
4Interactive learning where cognitive agents ask process questions
4Gradual 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
Cognitive
Cognitive Computing
Cognitive Assistance
Cognitive Services
Cognitive BPM

QUESTIONS?
Thank You!
69

Model of Human AdministrativeAssistants: 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 conversationalagents
§ 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 successfulprocess 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

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