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1Copyright © IBM 2016
Rethinking BPM in a Cognitive World:Transforming How We Learn and Perform Business Processes
Richard Hull Hamid R. Motahari NezhadIBM Research IBM ResearchYorktown Heights Almaden
Hamid R. Motahari Nezhad, Rama Akkiraju:
Towards Cognitive BPM as the Next Generation BPM
Platform for Analytics-Driven Business Processes.
Business Process Management Workshops 2014: 158-164
© 2016 IBM Corporation
COGNITIVE
The Future of Computing is …..
2
… and Cognitive Computing will dramatically
impact BPM in the coming decade
© 2016 IBM Corporation
Cognitive is emerging as a new computing paradigm
Tabulating Systems Era
Programmable Systems Era
CognitiveSystems Era
© 2016 IBM Corporation
Towards Computing-At-Scale as the Shared Characteristic of Recent Advances
4
Scalable Computing over
Massive 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
© 2016 IBM Corporation
Cognitive Era
5
Discovery & Recommendation
Probabilistic
Big Data
Natural Language as the Interface
Intelligent Options
© 2016 IBM Corporation
Understands
Conversational
Interface
Adapts and learns
Generates and
Synthesizes
learning techniques
Cognitive System
1
2
3 Cognitive Systems
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 capabilities that augment and scale human expertise
Watson
7Copyright © IBM 2016
Agenda
Emergence of the Cognitive Computing Era
Cognitive and BPM: Introduction
Cognitive Learning Processes
From Process Learning to Executables
Cognitive Enablement of Processes
Challenges, Open questions, Implications
8Copyright © IBM 2016
Kinds of Business Processes
Transaction-Intensive Processes
Judgment-Intensive Processes
Decision, Design & Strategy
Processes
Business ProcessHierarchy
Use Cases
Many “ancillary” processes
are performed in ad hoc ways,
spreadsheets, etc.
Case
Mgmt
Rules
Intensive
Knowledge-
intensive
Processes (KiP)
???
• Sales of ComplexIT Services
• Project mgmt.• E.g., Complex client
on-boarding• Commercial Financial
Services (e.g., Loans)
• (Mgmt of) Back-office processing, e.g., • Order-to-Cash
reconciliation• Payroll• . . .
• Enterprise Optimization• New Business Model• New Markets, Geo’s
• Merger/Acquisition• Build vs. Buy
Relevant ModelingApproaches
Challenges
(process-
centric)
BPM
Many “Judgement-Intensive”
processes are fairly simple,
but too expensive to automate
Due to rich flexibility needed,
KiP’s not supported
systematically
9Copyright © IBM 2016
Source of these challenges -- “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 happening over email, chat, messaging apps, …
Many text-based descriptions of:
Processes, procedures, policies, laws, rules, regulations, plans, external entities such as customers, partners, government agencies, surrounding world, news, social networks, etc.
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 InformationIoT Devices and Sensors
10Copyright © IBM 2016
A conceptual framework for Cognitive BPM
Unstructured Data, IoT, Smart Devices, Sensors, etc.
Cognitive
Decision
Support
for
Processes
Cognitive
Interaction
with
Processes
Cognitive
Process
Learning
Cognitive
Process
Enablement
Human-Human, and Human-Machine Collaboration
Traditional BPM
and Case Management Abstractions
Cognitive Process
Abstractions
11Copyright © IBM 2016
IdentifySales
Owner
ProposeOfferings
Cognitive Decision Support for Processes
Cognitive Decision support is about the ability to digest a myriad of unstructured
information to assist in decision making at a given step in the process
Sales triage in a large corporation with broad array of offerings
Financial and
Technology related
Public data
Similar clients’ past
performance
Social media
data
Sellers’
conversations
Offer Owner
historic win
rates
Seller notes
on client
Seller rankings
according to
qualifications and
performance
12Copyright © IBM 2016
Cognitive Interaction with Processes
Today’s Interaction Paradigms
• UI/Screens
• Click
• Multiple apps
Cognitive BPM World
• Conversational
• Talk / Notification
• Integrated agent interface
Additional aspects:• Ability to guide a person through a process
• Ability to construct conversations for a process interaction dynamically
• Using multi-modal interaction models to complement user interface
• Ability to communicate cognitive/analytics results, in a human consumable manner
• Ability to explain learned process optimizations
13Copyright © IBM 2016
Agenda
Emergence of the Cognitive Computing Era
Cognitive and BPM: Introduction
Cognitive Learning Processes
From Process Learning to Executables
Cognitive Enablement of Processes
Challenges, Open questions, Implications
14Copyright © IBM 2016
Learning Process: A Myriad of Data Sources
Representative research work
Process Mining literature, e.g.,
[Process Mining Manifesto, 2011]
[Witschel, Nguyen, Hinkelmann, 2012]
[Friedrich, Mendling, Puhlmann 2011]
[Ehrig, Koschmider, Oberweis 2012]
[Aa, Leopold, Reijers 2015]
MailOfMine [Di Ciccio et al, 2011, 2012]
e-Mail Mining [Soares, Santoro, Baiao 2013]
eAssistant [Motahari et. al., in submission]
IT ticket mining
Digital Exhaust, e.g.,• Emails• Chat transcripts• Call-center
transcripts
Structured data• Event logs• System logs• Enterprise meta-data• …
Purpose-built documents• Process Descriptions• Training Manuals• Corp. Policies• Govt. Regulations
15Copyright © IBM 2016
Applied on Email, and Activities
eAssistant: Extracting process steps from emails [Motahari Nezhad et al, in submission]
Tool to extract process steps from emails and proactively advise knowledge workers
Extracts actionable statements, commitments, temporal aspects from emails
Includes user feedback, reinforcement learning, and customization to org’s and people
16Copyright © IBM 2016
eAssistant Architecture: Machine Learning over SystemT and Watson APIs
SystemT Librabies and Feature Extractors
POS tagging
Conversation and Calendar Sources
Messaging Data
Repositories
Document
collections
Dependency
Analysis
Actionable
Verb Learning
Action Pattern
Learning
Co-reference
resolution
Named Entity
Recognition
Process
Fragment
Discovery
Adaptive and
Personalized
Learning
Action Type Identification
and Template-based
Metadata Extraction
SystemT’s
Action API
(“Watson parser”)
Actionable Insight Components
eAssistant APIs
Conversation
Analytics
17Copyright © IBM 2016
Learning of Actionable Statements Identification Bootstrapping
Start with rules-and annotation-based training data generation
Learn from user feedback
Validation
>6K emails from Enron data set
>16K candidate actionable mentions
Hand-annotated for ground truth
After training,Precision: 87%Recall: 83%
Note: User feedback enables continued improvements
Model Learning – Training Phase: Classified Statements
Action Verb
Learning
Adaptive & Online Pattern
Learning
Actionable Statement
Identification Results + Type
Pattern-Based
Action
Prediction
is action verb
yes no
No
Pattern
ConstructionFiltering
Feedback
Prediction: Statement
Action
verbs
Ontology
Feature ExtractionPOS, NLP Tags, Verbs And Dependency Extraction
Feature ExtractionPOS, NLP Tags, Verbs And Dependency Extraction
Model <Adaptive Patterns>
verbs All features All features
is verb tagged
as actionable?
yes no
enclosed verb
yes no
verb can be
independent
verb is
dependent
Verb Independent ?
send true
prepare true
like false
have false
- - - - - -
18Copyright © IBM 2016
Agenda
Emergence of the Cognitive Computing Era
Cognitive and BPM: Introduction
Cognitive Learning Processes
From Process Learning to Executables
Human Resources example
Commercial Financial Processing example
Cognitive Enablement of Processes
Challenges, Open questions, Implications
19Copyright © IBM 2016
A key value from automatic learning of process
Today, people focus their attention & investments on high-volume processes
But in many industries, half or more of the expense is on a large number of low volume processes.
Cognitive Computing can bring the ability to automate, optimize & transform “long tail” processes
Long Tail of
Low-Volume Processes
Volu
me
… …
Automating the long tail
of Business Processes
Low volume, but
potentially high cost
20Copyright © IBM 2016
Human Resources Example:Based on a Business Process Outsourcing provider
WHY ARE THESE MANUAL ??
Long tail
Many such processes
Many variations by BPO client
Many variations by country
Each individual process is low volume
Employee & HR
Data Entry
•ERP system (e.g., SAP, PeopleSoft) Authoritative or “Golden” copy of employee and payroll data
•Govt. Systems •Payment
Systems
“Ancillary Processes”• New hire• Termination• Garnishment• …
Managed on spreadsheets
Described using MS Word
21Copyright © IBM 2016
Extending eAssistant to read Process Descriptions(Preliminary results)
eAssistant+
Process Fragments
Conditions (with Actions)
Purpose-built document about processing of terminations (across multiple countries)
22Copyright © IBM 2016
(Exploratory)Human-in-the-Loop
Enable users to examine, refine outputs at all stages
Mapping from process descriptions to executables:
DiscoverProcess
Fragments
ExtractBusiness Entities
AdaptiveBPM Engine
Entities,
Value phrases,
…
ProcessKnowledge
Graph Builder
TaskFlows
ReasoningLogic
ExecutableProcessBuilder
Actions, Rules,
Conditions, …
Chunks
DomainModelSpec.
KG RulesUnstructured
Input
DocumentProcessing
Mappings: Document ref’s Domain Models Database ref’s
Representative Pipeline
23Copyright © IBM 2016
(Exploratory)Human-in-the-Loop
Enable users to examine, refine outputs at all stages
Mapping from process descriptions to executables:
DiscoverProcess
Fragments
ExtractBusiness Entities
AdaptiveBPM Engine
Entities,
Value phrases,
…
ProcessKnowledge
Graph Builder
TaskFlows
ReasoningLogic
ExecutableProcessBuilder
Actions, Rules,
Conditions, …
Chunks
DomainModelSpec.
KG RulesUnstructured
Input
DocumentProcessing
Mappings: Document ref’s Domain Models Database ref’s
Extract Process constructs & fragments• Conditions• Actionable statements• Sequences• Scoping• . . .
Construct all-inclusive knowledge graph• Parse-tree for process
fragments
Targeted Domain Model• Employee attributes,
as occurring in different data sources
• Kinds of updates
Human-consumable abstract representation of executables, • E.g., handful of templates
corresponding to• Conditions• Actions• Conditional actions
Targeted Processing Model• E.g., for HR processing the
focus is on individual employees, and • Validate input data values• Series of updates if valid• Manual treatment of
exceptions
Mapping construction algorithm (self-tuning)
Research/Engineering ChallengesRepresentative Pipeline
24Copyright © IBM 2016
Commercial Financial Processing (e.g., loans, insurance, …)
Submission Analysis
Gathermore Data
Final Analysis & Pricing
In the long tail, typically manual and multi-day
Processing logic:• ~70% in manuals,
guidelines, …
• ~30% from experience
Learning the logic enables:• Substantial speed-up
• Improved consistency
• Improved customer satisfaction
• Opportunity for
continuous improvement
25Copyright © IBM 2016
Typical insurance rules (commercial sector)
Learned logic
forms hybrid
Decision Tree
http://calmutual.com/ComlUW%20Manual1212.pdf
26Copyright © IBM 2016
Human-in-the-LoopEnable users to examine, refine outputs at all stages
Mapping from process descriptions to executables:In the Commercial Financial Modeling case
DiscoverProcess
Fragments & Rules
ExtractBusiness Entities
AdaptiveBPM Engine
Entities,
Value phrases,
…
ProcessKnowledge
Graph Builder
TaskFlows
ReasoningLogic
ExecutableProcessBuilder
Actions, Rules,
Conditions, …
Chunks
DomainModelSpec.
KG RulesUnstructured
Input
DocumentProcessing
Mappings: Document ref’s Domain Models Database ref’s
Most business logic in form of• If-then rules• Exclusions
Specialized “meta-rules”, e.g., about treatment of exclusions
Domain Ontologies available for Financial Industries
Processing model centered around small process & large rule set
(Exploratory)Opportunity for Continuous Improvement
27Copyright © IBM 2016
Agenda
Emergence of the Cognitive Computing Era
Cognitive and BPM: Introduction
Cognitive Learning Processes
From Process Learning to Executables
Cognitive Enablement of Processes
Challenges, Open questions, Implications
28Copyright © IBM 2016
Typical sales engagement process for complex IT services deals
Multiple stakeholders in a “spiral model” to build RFP response with pricing
Many competing requirements; multi-objective optimization
Requirements & prioritizations evolve as new information received from client
Many text-based artifacts throughout the process
Multiple threads of activity, with new threads emerging throughout the process
Traditional BPM approaches, and even Case Mgmt, not flexible enough
Key process logic & best practices are hidden – challenging to apply statistical analytics approaches
RFP
Receipt
Requirements
ingested into
tool (Week 2)
Solutioning, Costing/Pricing,
Executive Reviews and
Approval Milestones
Negotiation, Refinements
On-boarding
RFPDeal Pursuit, Discovery,
Due DiligenceContract . . .
RFP
Response
Deadline
Final
Proposal
Customer Review,
Modifications
29Copyright © IBM 2016
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
Act on next steps of plan
Optionally perform Learning steps
Cf. KiF’s [Di Ciccio, Marrella, Russo 2015]
Also [Bucchiarone et al 2013], [Marrella, Mecella, Sardina 2014]
“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
30Copyright © IBM 2016
Key Abstractions for Cognitively Enabled Processes Knowledge, including constraints Knowledge at scale, including from unstructured data, is the fundamentally new element that
Cognitive Computing brings to BPM.
Goals/Subgoals Initial top-level goals may be specified in advance, and additional goals and sub-goals can be
formulated dynamically
Agents (Human & Machine) These agents will have varying intentions, roles, and specialties
Communication between agents may be captured and included into knowledge base
Decisions Based on information & knowledge acquires so far
The decision may lead to new (sub)goals and plans
Actions Atomic unit of work performed by an Agent; including ingestion/analysis of large data sets
Plans These may be partial, and may be revised as the process progresses
May be created and/or re-formulated frequently
Events These may arise from completed actions, new information acquisition, pro-active agents
31Copyright © IBM 2016
Planning research in BPM: Selected examples
Goal-driven Business Process Derivation [Ghose et al 2011]
Goals expressed as Boolean combinations of propositional variables
Tasks contribute towards achieving (sub-)goals
Single plan made (no iterations)
“SAP speaks PDDL” [Hoffmann, Weber, Kraft 2012]
Addresses challenge of how to map business objectives into PDDL framework
Focus is on SAP biz-level model for “Status & Application Mgmt (SAM)”
Key observation: SAM describes Business Objects that naturally map to cross-products of FSM’s
On-the-Fly Adaptation of Dynamic Service-Based Systems [Bucchiarone et al 2013]
Context is “service-based systems” – a continuously evolving “context”
Iterative re-planning as new events/data arrive
Interleaving of planning and solution execution
SmartPM [Marrella, Mecella, Sardina 2014]
Goals expressed using first-order logic predicates
Plans are formulated to achieve a next goal
Exceptional situations may occur, in which further plans attempt to remedy
External stimuli also provide new information and/or goals
32Copyright © IBM 2016
Plan and Act Cycle in SmartPM: an illustrative example Situation Calculus [Reiter 2001]
“Situations”: First-order logic term describing a state in terms of sequence of actions already performed
“Actions”: typically performed by services
“do(a,s)”: situation after action a done on situation s
“Fluents”: predicates describing state of the world in different situations
IndiGolog [De Giacomo et al 2009]
A logic-based programming language
Familiar constructs: conditionals, sequencing, loops, …
Includes a “lookahead search” operator (), which will find a plan that achieves
PDDL Planner [Edelkamp, Hoffmann 2004]
One of many planning systems
Given an initial state I and goal G, build a plan that will move you from I to a state that satisfies G
In practice, the outcome of the plan may have exceptional conditions, and not reach G after all
s22 = do( Move(John, loc166), s21)
Actions on Situations Fluents
s23 = do( Move(Robot1, loc166), s22)
s21 = do( TakePhoto(John, loc150), s20)Avail_Photos(loc120, s21)
Location(John, loc166, s22)
Location(Robot, loc277, s23)
Program fragments (simplified)
Framework & Axioms
Processing cycle (responses to new info)
If not Location(Robot1, loc166, s)
Then ( Location(Robot1, loc166) )
EndIf
• Constructs for services, service calls, locations, etc.
• Axioms for when can service be invoked, when released, …
• Top priority: exceptional condition Create/execute new plan
• Second priority: Continue to achieve specified goals
Create/execute new plan
• Third priority: Respond to new incoming event (service completion
or from external cause)
If Location(Robot1, loc164)
Then Move(Robot1, loc163);
Move(Robot1, loc164)
EndIf
33Copyright © IBM 2016
Human-in-the-LoopEnable users to examine, refine outputs at all stages
Mapping from process descriptions and artifacts into SmartPM:Illustration using IT Services Sales
DiscoverNew Goals,
Indicators ofProgress, …
ExtractEntities
SmartPMEngine
Entities, Roles,
Constraints, …
Domain ModelKnowledge
Graph Builder
FluentDefinitions
Actions/ Services:
Pre-cond’s,Post-cond’s
Logical Expressions
Builder
Goals, Change-of-
state, Priorities
Chunks
KG
Logic
SpecUnstructuredInput
(including on-the-fly)
DocumentProcessing
Mappings: Document ref’s Domain Models Database ref’s
Generic target framework provided by SmartPM
. . .
Domain StatusKnowledge
Graph BuilderFluent
UpdatesKG
update
As before, many opportunities to narrow the scope & fill out framework
Starting point: RFPCog tool [Motahari et al 2016] available to extract “goals” from Requirements docs
Key sub-domains include• IT Services• Pricing• Sales Mgmt• . . .
(Hypothesis)
Again,opportunity for continuous improvement
34Copyright © IBM 2016
Agenda
Emergence of the Cognitive Computing Era
Cognitive and BPM: Introduction
Cognitive Learning Processes
From Process Learning to Executables
Cognitive Enablement of Processes
Challenges, Open questions, Implications
35Copyright © IBM 2016
Cognitive will enable the next transformation in BPM
Focus on data relaxes rigidity of flows
Rules naturally refer to the data in cases
Activity-centric process models(BPMN)
Data-centric process models(CMMN)
Knowledge-intensive process models
Cognitively-enabled process models
Paradigm fits with the most tangible aspects of business operations
Focus on knowledge gives even richer flexibility
Analytics on unstructured data enables continuous monitoring & optimization
Tasks &
Sequencing
Data &
Decisions
Goals &
Plans
36Copyright © IBM 2016
Disruptions in Computer Science:From Web Services to Cognitive Assistants
From Waterfall Software Development to Agile Programming
From Classical BPM to Cognitive BPM
To
Cognitive BPM
Analyze
Monitor
Act
PlanNext Steps,
Adapt
Side-effect,
Interact
Probe,
Sense
Learn,
Discover
CognitiveBPM
Conventional BPM Lifecycle
ExecuteMonitor
Optimize
Define
Model
37Copyright © IBM 2016
Opportunities for several kinds of researchon top of generic framework process learning framework
DiscoverProcess
Fragments
ExtractBusiness Entities
AdaptiveBPM or
Planning Engine
Entities,
Value phrases,
…
ProcessKnowledge
Graph Builder
ExecutableProcessBuilder
Actions, Rules,
Conditions, …
ChunksKG Logic
Unstructured
Input
DocumentProcessing
Mappings: Document ref’s Domain Models Database ref’s
Info Extraction & Machine Learning
Planning
Ontologies
Human-in-the-LoopEnable users to examine, refine outputs at all stages
User Experience
Conceptual Modeling
38Copyright © IBM 2016
Cognitive BPM: Selected research challenges
Cognitive process learning:
Knowledge acquisition methods from unstructured information (text, image, etc.)
Combine with traditional process mining on logs
Building actionable knowledge graphs & executable code
Cognitively enabled processes: Plan-Act-Learn
Blending of “model” and “instance”
Recognizing goals from digital exhaust and process history
Advances in planning research – incremental, multi-threaded activity, richer goal languages, prioritized and soft goals, …
Enough uniformity to support reporting, identification of best practices
Cognitive Assistants for business processes
Assist workers across numerous tasks, including process management & optimization
Interactive learning where cognitive agents ask process questions
Gradual learning through experience, and process improvement
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