From services to cogs and journey to cognitive bpm print version

© 2015 IBM Corporation

Hamid R. Motahari-Nezhad

IBM Almaden Research Center

San Jose, CA

The Future of Services and BPM: The Journey to Cogsand Cognitive BPM

Keynote atASSRI Symposium – Sydney 19 February 2016


COGNITIVEThe Future of Computing is …..

2


Cognitive is emerging as a new computing paradigm

Tabulating Systems Era

Programmable Systems Era

CognitiveSystems Era


Cognitive Era

4

Discovery & RecommendationProbabilisticBig DataNatural Language as the InterfaceIntelligent Options


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 expertise

Watson


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

6

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


The Future of Work is Cognitive

7

The Evolution of Collaboration Technology In the Enterprise

The Rise of Intelligent Personal Assistant


Intelligent Assistance and Related Technology – App Landscape

8

IPSoft’sAmelia


We have seen just the tip of the iceberg…

9

Gartner Technology Hype Cycle - 2015


Mega Trends in the Enterprise

§ Messaging Apps are becoming de facto communication mechanisms with the enterprise– Slack, Confide, TigerText, Eko, Red e App– Could be the new interface with interacting with Apps, in short term

10

Credit: James Martin/CNET

§ The End of Apps (Web Browsing), as we Know It– Interaction via natural language - In/out (Chat Bots, towards Cognitive Assistants)– Notifications on Mobile, which is an asynchronous outputs

Bot Platform

§ Dark Data: "the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes.“, Gartner

– Unstructured data – “dark data” – accounts for 80% of all data generated today, and

– By 2020, the amount of dark data is expected to grow to over 93%.


FROM SERVICES TO COGS, AND TO COGNITIVE BPM

What these transformations mean for Service Computing and BPM?

11


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

12

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

13

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

14


Historical and Future Perspectives on BPM

15

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

16

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

17

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 in Process Management

Cognitive Interface for

Process Engines

Cognitive Process Discovery and

Learning

Cognitive Process Analytics

Cognitive Process Automation and

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

18


Cognitive BPM Lifecycle

19

CognitiveBPMS

Define

Enact

Monitor

Analyze

Next Steps, Adapt

Interact

Sense

Learn, Discover

To

Traditional BPMCognitive BPM


Towards Cognitive BPM: Example Scenarios

20

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 Corporation21

Inbox - Verse Highlighting actionable statements Recommending fulfilment actions

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

© 2013 IBM Corporation22 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 Corporation23 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

24

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

Context-aware Information

Finder

To-do, Task and Process

Assistant

Cognitive Work Assistant APIs

Semantic Role Labeling POS tagging Dependency

AnalysisCo-reference

resolutionNamed Entity Recognition

Knowledge GraphBuilder

Hamid R. Motahari Nezhad, Adaptive Learning of Actionable Statements, In Press.


Cognitive BPM: Research Directions

§ Abstractions and models for Cognitive Processes

§ Cognitive process learning: knowledge acquisition methods from unstructured information (text, image, etc.) and building actionable knowledge graphs

§ Cognitive Work Assistants–Cognitive augmentation of workers in work environments, and in process management

§ Cognitive Process Management System–Analytics on unstructured information to support process understanding–Analytics to support process adaptation, customization and configuration–Proactive process adaptation

§ Learning and teaching tasks and processes to cognitive agents–Interactive learning where cognitive agents ask process questions–Gradual learning through experience, and process improvement

25


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!

26

CognitiveCognitive Computing

Cognitive Assistance

Cognitive Services

Cognitive BPM


QUESTIONS? Thank You!

27

Hamid [email protected]

From services to cogs and journey to cognitive bpm print version

Data & Analytics

Transcript of From services to cogs and journey to cognitive bpm print version