Towards Cognitive BPM as a Platform for Smart Process Support over Unstructured Big Data

© 2014 IBM Corporation

Towards Cognitive BPMas a Platform for Smart Process Support over Unstructured Big Data

Hamid R. Motahari Nezhad

IBM Almaden Research Center, USA

Leveraging information and analytics for smarter process decisions


Processes in our life

A process refers to how work 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 apriori designed, or created on the fly

People may converse about processes (over many communication channels)

2 Ref: Motahar-Nezhad, Swanson, 2013, and Sandy Kemsley, Column2

Spectrum of Work


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

3


BPM: Evolution Timeline

4

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time

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‘85 ‘90 ‘95 ‘05‘00‘98

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Management Concepts

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com

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com or java

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com

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Inte

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XML

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General WorkflowBPR

BPMBPMBPM

time

ERPERP

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EAIEAI

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Adapted from Ravesteyn, 2007 and graphics from K. Swenson

‘15

Social BPM

Business Process Analytics

Co

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itiv

e B

PM


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

5

Ref: Muehlen, 2009Ref: Forrester, 2010


Different Types of Analytics

Existing BPA need to be designed, defined and programmed

for a specific analytical result

Mostly reactive: not autonomous/learning, and proactive6

Discovery

Analytics

Ref: Gartner


COGNITIVE SYSTEMS

7

© 2012 International Business Machines Corporation8

Businesses are “dying of thirst in an ocean of data”

1 in 2business leaders

don’t have access to data they need

83%of CIOs cited BI and analytics as part of their visionary plan

2.2Xmore 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 Trillionconnected devices

generate 2.5 quintillion bytes

data / day

© 2012 International Business Machines Corporation9

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 expertise

Watson


COGNITIVE BPM

10


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

11

Users

Assistant/

Agents

CustomersEmployees/

Colleagues

Plansworkflows

Rules

Policies

Regulations

Templates

Instructions/

Procedures

ApplicationsSchedules

Conversations over Email,

chat, social media, etc.

Organization

Cog. BPM

Agent

Unstructured Linked Information


Cognitive BPM Systems

A Cognitive BPM system offers the computational capability of a cognitive

system to provide analytical support for processes over structured and

unstructured information sources, and continuously discovers, learns and

acts to 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 apriori defined; 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

12


Process Definition,

Discovery, Learning

Process Enactment

Process/ Environment

Sensing

Process Analytics

Proactive/ Reactive Process

Response/Adaptation

Cognitive BPM Lifecycle

13

Environment

Sensing

Data

sources

Data Processing/

Analytics

Process

Composition /

Enactment Update

Process

Monitoring/Analytics

IoT


Use Case 1: Knowledge-intensive Enterprise Processes

Human-Centric, knowledge-instensive Processes 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

14


Research Supporting Cognitive BPM in Enterprise Processes

15

Health Identification and Outcome Prediction for Outsourcing Services Based on Textual Comments

Hamid R. Motahari Nezhad, 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.


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)

16

$3B

Cognea


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

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.


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


Research in Support of Cognitive BPM in Work Assistant Space

Task, commitment and process extraction from workers interactions over

email and chat

19

Anup K. Kalia, Hamid R. Motahari Nezhad, Claudio Bartolini, Munindar P. Singh: Monitoring

Commitments in People-Driven Service Engagements. IEEE SCC 2013: 160-167


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

20


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

21


QUESTIONS?

Thank You!

22

COGNITIVE BPM: SMART PROCESS SUPPORT OVER UNSTRUCTURED BIG DATA

Technology

Towards Cognitive BPM as a Platform for Smart Process Support over Unstructured Big Data