The document discusses the evolution of Business Process Management (BPM) towards Cognitive BPM, emphasizing the integration of analytics and intelligent systems to handle unstructured big data. It outlines how Cognitive BPM can enhance process decisions by leveraging analytics-driven insights, adaptive learning, and proactive responses to emerging data. Furthermore, it presents use cases demonstrating the application of Cognitive BPM in improving efficiency and supporting complex decision-making in various enterprise processes.

1. © 2014 IBM Corporation
TowardsCognitive BPMas a Platform for Smart Process Support over Unstructured Big Data
Hamid R. MotahariNezhad
IBM AlmadenResearch Center, USA
Leveraging information and analytics for smarter process decisions

2. © 2013 IBM Corporation
Processes in our life
A process refers to howwork gets done
Processes can be personal, or business processes
Processes can be repetitively performed (are programmable), or unique
They can formally defined, prescribed, described or simply done
They can be aprioridesigned, or created on the fly
People may converse about processes (over many communication channels)
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Ref: Motahar-Nezhad, Swanson, 2013, and Sandy Kemsley, Column2
Spectrum of Work

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Outline
Business Process Management
–Historical Perspective
–Business Process Analytics
Cognitive Systems
–Data generation
Cognitive BPM
–Vision
–Example Use Case
–Initial Work in Support of Cognitive BPM Vision
–Research Questions and Directions
Conclusions and Discussion
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4. © 2013 IBM Corporation
BPM: Evolution Timeline
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Databases
Back end Systems
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
Calculation General Workflow System and User Interactions
Interface
Layer
Web
Service
Presentation Presentation
XML
API
BPMS
TQM
General Workflow BPR
BPM
time
ERP
WFM
EAI
‘85 ‘90 ‘95 ‘98 ‘00 ‘05
IT Innovations
Management Concepts
Adapted from Ravesteyn, 2007 and graphics from K. Swenson
‘15
Social BPM
Business Process Analytics
Cognitive BPM

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Business Process Analytics (BPA)
All activities that are performed on process data (logs, events, social network, metadata, etc) to deliver process insights, monitor and optimize processes and recommend actions
Technically involves the application of machine learning, data mining, optimization and automation techniques on process(-related) data
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Ref: Muehlen, 2009
Ref: Forrester, 2010

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Different Types of Analytics
Existing BPA need to be designed, defined and programmed for a specific analytical result
Mostly reactive: not autonomous/learning, and proactive
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Discovery
Analytics
Ref: Gartner

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COGNITIVE SYSTEMS
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8. © 2012 International Business Machines Corporation
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Businesses are“dying of thirst in an ocean of data”
1 in 2
business leaders don’t have access to data they need
83%
of CIOs cited BI and analytics as part of their visionary plan
2.2X
more likely that top performers use business analytics
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

9. © 2012 International Business Machines Corporation
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Understands natural language and human communication
Adapts and learnsfrom user selections and responses
Generates and evaluatesevidence-based hypothesis
Cognitive System
1
2
3
Cognitive Systemsdo actively discover, learnand 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 expertise
Watson

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COGNITIVE BPM
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11. © 2013 IBM Corporation
Cognitive BPM: supporting process over unstructured information, a bottom-up approach
Traditional BPM and workflow systems define structured processes over structured information
Case management support human-guided flexible processes (top-down)
Cognitive BPM supports processes over flex-structured (big) data based on intelligent analytics (bottom up inherently, learning/directing makes it work both directions)
Understanding the (unstructured) information, people (worker/individual) and the environment in a process context
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Users
Assistant/
Agents
Customers
Employees/
Colleagues
Plans
workflows
Rules
Policies
Regulations
Templates
Instructions/
Procedures
Applications
Schedules
Conversations over Email,
chat, social media, etc.
Organization
Cog. BPM Agent
Unstructured Linked Information

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Cognitive BPM Systems
A Cognitive BPM systemoffers the computational capability of a cognitive system to provide analytical support for processes over structured and unstructured information sources, and continuously discovers, learnsand actsto achieve a process outcome
–Meets two pressing needs: supporting complex process decisions, and processing large amount of data
–Analytics-driven and integrated process (model) definition, reasoning and adaptation
•Process is not assumed aprioridefined; discovered, learned and customized based on accumulated knowledge and experience
–Analytics supporting the execution of process
•When, What action (how) and whom to contact
–The need for revisiting some basic abstractions of BPM
•Real-world course of actions
•New information availability changes course of actions in a plan
•Fluid tasks, notion of task completion, and process/plan adaptation
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Process Definition,
Discovery, Learning
Process Enactment
Process/ Environment Sensing
Process Analytics
Proactive/ Reactive Process Response/Adaptation
Cognitive BPM Lifecycle
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Environment Sensing
Data sources
Data Processing/ Analytics
Process Composition / Enactment Update
Process Monitoring/AnalyticsIoT

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Use Case 1: Knowledge-intensive Enterprise Processes
Human-Centric, knowledge-instensiveProcesses in IT Services Provider Environments (Sales Management Processes)
–Reference, descriptive processes are available, no (WFM) system supporting the process
–The need for a business-aware automation solution for human-centric processes
–Conversational, multiple interactions and facts impacting process decisions
–Inbox used a work management system, in addition to phone, chat and records in databases
–Changes to process guidelines and templates are commonplace and communicated through email
Cognitive BPM
–Providing automation support, and analytics over process
–Ability to process and link process information from unstructured sources over multiple channels
–Putting the business first (outcome), not the process
•Process should support more sales, through employing all analytics type: diagnostic, predictive, prescriptive
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15. © 2013 IBM Corporation
Research Supporting Cognitive BPM in Enterprise Processes
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Health Identification and Outcome Prediction for Outsourcing Services Based on Textual Comments
Hamid R. MotahariNezhad, Daniel B Green ia, Taiga Nakamura, and Rama Akkiraju, IEEE SCC 2014
A Win Prediction Model for IT Outsourcing Bids
Daniel Greenia, Rama Akkiraju, and Mu Qiao, IEEE SRII Global Conference 2014.

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Use case 2: Cognitive Assistant/Agents
Systems that reason, learn from experience, and accept guidance in order to provide effective, personalized assistance (Ref: DARPA PAL)
IBM’s Watson, Apple’s SIRI, SRI’s CALO, Google Now, Cortana, … .
Open Source
–Cougaar(http://www.cougaar.org/)
–Open Cog (http://opencog.org/)
–Open Advancement of Question Answering Systems (http://oaqa.github.io/)
–SolrSherlock(http://debategraph.org/SolrSherlock)
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$3B
Cognea

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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
–Isprocess and work aware
–Monitorsworker’s input channels and interactions (emails, chats, social connections, external and internal environment, knows rules, policies and processes)
–Proactively acts on worker’s behalfand 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
–Organizesand 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 17
Related article to Personal Assistants in work environments:
Erickson et al, Assistance: The Work Practices of Human Administrative Assistants and their Implications for IT and Organizations, CSCW 2008.

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Use Case 3: Work Assistant Example
Assume an executive admin is managing an event organization process for their department
–Step 1: sending invite to an event to employees in their department, through email and requests for RSVP
•Cognitive BPM (1): Q&A ability for the admin: How many have confirmed, how many pending, how many not answered
•Cognitive BPM (2): Predictive analytics: how many will eventually RSVP?
•Cognitive BPM (3): Diagnostic analytics: why some not accepted (customers in case of marketing case)?
–Step 2: Ordering place, food, transportation, etc
•Cognitive BPM (1): tracking of the process steps, which vendor have replied, which ones pending, have questions, etc.
•Cognitive BPM (2): keeping track of synchronization and consistency (dates, amounts, numbers, etc.) among different steps
–Step 3: Pre-event steps
•Reminding people who have RSVPed
•Compiling and sending logistic information (from different steps) 18

19. © 2013 IBM Corporation
Research in Support of Cognitive BPM in Work Assistant Space
Task, commitment and process extraction from workers interactions over email and chat
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Anup K. Kalia, Hamid R. MotahariNezhad, Claudio Bartolini, MunindarP. Singh: Monitoring Commitments in People-Driven Service Engagements. IEEE SCC 2013: 160-167

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Research Directions
Abstractions and models for Cognitive Processes
Cognitive Process Management System
–Analytics on unstructured information to support process understanding
–Analytics to support process adaptation, customization and configuration
–Proactive process adaptation
Cognitive Work Assistants
–Cognitive augmentation of workers in work environments, and in process management
Teaching processes to cognitive agents
–Interactive learning where cognitive agents ask process questions
–Gradual learning through experience, and process improvement
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21. © 2013 IBM Corporation
Conclusions and Discussions
We are in the beginning of a profound transformation of assisting workers with intelligent agents/assistant, and the BPM field, in general, enabled by advances in AI and Cognitive Computing
Major advances in business process analytics work so far, however, systems need prescriptions by humans
The vision of Cognitive BPM supports a self-learning, adaptive and analytics BPM systems that focuses on process outcome
–Analytics-driven, learning, and proactive adaptation
–Enabling systems and human work together to achieve better results
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QUESTIONS?
Thank You!
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COGNITIVE BPM: SMART PROCESS SUPPORT OVER UNSTRUCTURED BIG DATA