ManyWorlds - EpitureJetEye, Flickr, YouTube, etc. Document Management Documentum, Livelink, FileNet, etc. Collaborative Content Wikis, Blogs, etc. Learning Layer Epiture Web 0.0 Web

www.manyworlds.comNovember 4, 2010 © ManyWorlds, Inc Slide 1

ManyWorlds is an intellectual capital design company that conducts advanced R&D programs in fields that have extraordinary potential. We invest over 25% of our annual revenues in these R&D programs.

We are leaders in establishing IP in the emerging foundational areas of adaptive software systems and business processes, and we put this IP into practice with our Epiture® adaptive enterprise “learning layer” platform, and our leading practice business process methodologies.

Our R&D is generating significant value for our clients as the leading IT and media businesses, as well as business in general, accelerate their convergence toward what we call “the adaptive world™.”

R&D leaders in adaptive systems and processes

ManyWorlds


1950 1960 1970 1980 1990 2000 2010

The Evolution of IT Continued . . .Accelerating into the Era of Adaptation

Speed

Connectivity

Adaptation

Mainframes Web 1.0 “Web 2.0”PC’s

GoogleInformationTechnology

TrendsMicrosoft

IBM

= IT Market Downturns

= Technology Super Cycles

2020

= Dominant Themes


The New IT Imperative: Adaptive Architecture

System Users System UsersBehaviorPatterns

Traditional Adaptive

Socially Unaware Socially Aware


Putting the Adaptive Architecture to Work

System UsersBehaviorPatterns

Learning

Behavioral Information (about you or people like you) Navigation paths Referencing/tagging Referrals to others Subscriptions Explicit feedback & profiles Location/Environment Temporal patterns

Adaptive Delivery Personalized Search Personalized Recommendations Processes that Adapt


1950 1960 1970 1980 1990 2000 2010

The Adaptive World Delivered

Speed

Connectivity

Mainframes Web 1.0 “Web 2.0”PC’s

GoogleInformationTechnology

TrendsMicrosoft

IBM

= IT Market Downturns

= Technology Super Cycles

2020

= Dominant Themes InternetBroadbandWireless

MainframeMinicomputers PC’s

Adaptation

PersonalizationAdaptive ProcessesGeo-aware


The “Adaptive World” – General Enterprise Implications

Highly distributed intellectual capital. Leverage the best from anywhere in the world.

Learning will increasingly be built into systems. Capture of individual and collective usage behaviors while “just doing my job,” plus advanced data mining algorithms, enable enterprise learning.

Processes (including workflow) will adapt. “One size fits all” approach to enterprise software gives way to processes that adapt to specific business situations.

Transformation of intellectual capital-driven processes. Adaptive, distributed processes (e.g., R&D) yield the benefits of systematic approaches while enhancing creativity and innovation.

“The Business is the Network.” Acceleration of trend toward managing a network of capabilities complementary to the business’s core competencies.


Managing Intellectual Capital Networks

Example ManyWorlds Solution Implementation

Determine critical intellectual capital management areas

Develop key metrics and a Management Operating System for formally tracking benefits on a continuing basis

Implement adaptive learning layer solution spanning relevant internal organizations and external partners, and supported by an associated Management Operating System

ManyWorlds can provide auxiliary services to support overall enterprise knowledge lifecycle management as desired

Formally evaluate results after 6 months

Expand to other domains as benefits are realized

www.manyworlds.comNovember 4, 2010 © ManyWorlds, Inc

Learn More About the Era of Adaptation, Adaptive

Learning Platforms, and Applications . . .

The era of adaptation culminates in a fundamentally new enterprise system paradigm called “the learning layer”– a paradigm that makes the adaptive enterprise a reality!

See www.learninglayer.comFor more information on the best-selling book, The Learning Layer(Palgrave MacMillan 2010).

http://cts.vresp.com/c/?ManyWorldsPublishing/0ab44ccacd/TEST/60f61fad99/qs=7e29d953ba533f4d40538b76cd27418add5d06e87350b559651354032f6099c7

http://www.learninglayer.com/

Learning Layer-enabling Technology


Adaptive Knowledge Management Applications . . .

ManyWorlds’ adaptive knowledge network architecture builds learning into conventional content and systems. As an example, novice users may be automatically presented with different information than experts.

An adaptive knowledge network complements other IT systems and assets. It is a flexible and integrative learning layer that resides on top of other systems and assets. Therefore it is easy to implement.

Adaptive knowledge networks can easily and flexibly span internal organizations and external businesses and organizations.

A ManyWorlds adaptive knowledge network self-maintains itself, thereby reducing ongoing IT costs and continually maintaining optimal performance and value

Early implementations have included leading energy, technology, and consulting and pharmaceutical companies.


The Knowledge Network Paradigm

A Knowledge Network . . .

Is a “fuzzy” network of content objects that relate to each other by degree.

Content objects include any form of media including text, graphics, video,audio, and applications.

Content objects typically include meta-information and relationships with other content objects to form entities such as articles manuals, processes, toolkits and people. For example, a person object can be a bio content object plus a photo object(i.e., people, at least their representations, are objects too!).

Special content objects that have meaningprimarily by virtue of their relationship to other objects are called topic objects.


The Knowledge Network Structure

Object Affinities

Object affinity means the relative degree of relatedness among content objects ina knowledge network. Object affinitiescan be normalized as numbers between0 and 1, where 0 means no relationship,and 1 means very strong relationship.Affinities are directional, and are notnecessarily the same in each direction.

A pair of content objects may have morethan one type of affinity. And each ofthese multiple affinities may be derived from different sources. Affinity sources include direct assignment by people, or byautomated systems that assign or update affinities based on information within the object (e.g., text matching), and/or based on usage patterns associated with objects.

X

.4.9

.3

.3


Knowledge Networks That Learn

X

.4.9

.3

.3

.6

.2

Knowledge Network Adaptation

Knowledge networks can adapt to theirusage. Traffic patterns and usage statisticsat the community and personal levelscan influence the affinities among contentobjects. The topology of the knowledgenetwork changes accordingly.

The way the knowledge network presents itself for navigation by users changes in accordance with the changing topology. In other words, adaptive knowledge networksactually learn to be more and more useful over time.


Extensible Sub-Networks

Topical and Social Networks

Topic ATopic B

Topic C

Y

A sub-network of content objects that relateto a topic object is called a topic network.

The relationship between a content objectand a topic (object) may be by degree.A topic object may also relate to other topic objects by degree. Formation oftopical networks can continue withoutlimit, and relationships among topics,and among content and topics, may be adjusted at any time.

Objects representing people may beorganized in topical networks just like any other type of content objects. We may call such networks that comprise all or mostly people objects, social networks.


Seamlessly Integrating Content and Social Networks

Topic ATopic B

Topic C

Social Network

A

Social Network

B

Ownership Usage Patterns

Authorship Ownership Usage Patterns

Direct Interactions Interactions with

Owned/AuthoredContent

Permissions


Content and Social Networks Automatically Adapting

Topic ATopic B

Topic C

Social Network

A

Social Network

B

Ownership Usage Patterns

Authorship Ownership Usage Patterns

Direct Interactions Interactions with

Owned/AuthoredContent

Permissions

InferredInterests

ModifiedRelationships

New Networks

InferredInterests


Adaptive Knowledge Networks Delivered!

Topic ATopic B

Topic C

Social Network

A

Social Network

B

Seamless Content and Social Network Generation, Navigation, and Administration

Integrated and Automatic Intelligent LearningIncluding automatic content and people recommendations, detailed explanations

for the recommendations, and intelligent auto-maintenance functions


See Epiture in Action at www.manyworlds.com!


And More . . . Knowledge Network-based Processes

Business Process X

Task 2 Task 3Task 1

ProcessRelationship

ProcessRelationship

Special relationships between objects can be applied to extend adaptive knowledge networks to adaptive process networks

Process Network


Task 5Task 4

Plug and Play Process Networks that Adapt!

Process X

Task 2 Task 3Task 1

Process Y

Task 7

Process networks can not only adapt, butalso promote modularity and reuse . . .

Task 6

www.manyworlds.comNovember 4, 2010 © ManyWorlds, Inc

The Epiture-based Learning LayerDelivering Enterprise (and Extra-Enterprise) Social Computing

Pre-Social Computing:

Robust but Limited/InflexibleEarly Social Computing:

Quick & Dirty/Throw Away

Enterprise+

Social Computing

Key features covered by global patents/patents pending

Automatic generation of multiple web sites from content

Easy for anyone to contribute/manage content

Enterprise compliant technologies

Easy to create/manage automatically generated site(s)

Management of all types of documents/multi-media/apps

Multi-level, content-based security controls

Advanced fuzzy tagging functions (tags by degree)

Basic social network functions

Knowledge/Web Site Management

Easily manage knowledge across business networks

Document/Digital Asset Management

AI-based auto-maintenance of content relationships

Fully integrated social network and content network functions

Adaptive recommendations of people as well as content

Built-in AI-based adaptive recommendations of content

Social Networking

Personal and collaborative taxonomy management

Folksonomies and Tagging

Enterprise Fit and Sustainability

Web Site

Management

& PortalsVignette,

Interwoven,

Plumtree, etc.

Social

NetworkingLinkedIn,

Facebook,

Xing, etc.

Tagging/

Distributed

ContentDigg, eSnips,

JetEye, Flickr,

YouTube, etc.

Document

ManagementDocumentum,

Livelink,

FileNet, etc.

Collaborative

ContentWikis,

Blogs, etc.

Learning

LayerEpiture

Web 0.0 Web 1.0 Web 2.0 Web 3.0

AI-based explanations of recommendations

AI-based interest and influence mapping & analysis

Fuzzy network-based 3-D Navigation

Documents

ManyWorlds - EpitureJetEye, Flickr, YouTube, etc. Document Management Documentum, Livelink, FileNet, etc. Collaborative Content Wikis, Blogs, etc. Learning Layer Epiture Web 0.0 Web