Landing the Raven: Positioning the Knowledge

Discovery System in the Enterprise

Wendi Pohs, Iris Associateswendi_pohs@iris.com

ContentsWhat is the Knowledge Discovery System?

Knowledge Management Architectures

Content stores: SpidersInformation warehouse: The K-mapClassification: K-map BuilderRetrieval: K-map IndexerPresentation: K-stationAssociation: MetricsTales from the trenches

– information, task aggregation– selection and display tools– people/place awareness– place creation and management

Can work together or independently

The Knowledge Discovery System Has Two Product

Components

1.

2.

3.

– search and browse – taxonomy generation, concept clustering– expertise profiling and location– metrics

4.

1.

2.

3.

4.

What does the K-station do?

Place Management–Personal and Shared places

May Include discussion forums, teamrooms, doc libraries, task lists, e-mail, MS Office integration

–Manage People: Directory integration, security, membership, online awareness, realtime communication

Integrates with the security and data model of Notes/Domino

A Customized K-station Place

K-station - Place-Based SameTime Awareness

Place-based awareness facilitates useful discussions

Instant messagingInstant teamroomsMembership

The Knowledge Discovery Server

Connects people with the right info at the right time

– Integrates People, Places, Things into a Knowledge Map

–Discovers relationships between People, Content and Categories to add context to information

Supports KM practices within organizations

–Respects user privacy –Enforces system security

What Does the Discovery Server Do ?

Out of the box Discovery Server will:– create a knowledge map– generate affinities– create expertise profiles– assign content value– index everything

– cluster and organizes documents– relationships b/t people and topics– mine skills, locate experts– based upon computed metrics– search for docs, people, topics, etc.

Discovery Server components constantly maintain and update themselves through a combination of automatic processes and administrative tools

Discovery Server K-map User Interface

A Vendor-neutral KM architecture

Mapping KDS to the Architecture

Discovery Server

Knowledge Map

Browsable/Searchable Topic map of People,

Places, and Documents

SolutionsApplication templates + Methodologies + Services

K-station PortalOrganize and manage personal and community assets

Metrics

Content Spiders: –Lotus Notes/Domino, Domno.doc, QuickPlace, Filesystem, Web (HTML),

Directory Spiders: –LDAP Server V2 or V3, Domino Directory/databases

E-Mail Spider: Notes

Enterprise Data Spiders: –Domino/Notes Spider with DECS and Lotus Connectors

–Content Spider SDK

Content stores: Spiders

People/Partners

B2B ebus

ApplicationsData

LegacyStructuredUnstructured

Enterprise

Type text

Type text Type text

Type text Type text Type text

Type textType textType text Type text

Type text

Type text Type text

Type text Type text Type text

Type textType textType text Type text

EnterpriseCommercial & External Feeds

"Write only" memory

Unshared tacit knowledge

"Write only" memory

Unshared tacit knowledge

TRADITIONAL ENTERPRISE

"Write only" memory

Unshared tacit knowledge

RELATIONSHIPS - ACTION

"Write only" memory

Unshared tacit knowledge

People related to Content

Content hierarchy

Categories Expertise

Information warehouse: Content Catalog

Search Content

Valuation

Search Expertise

Search Hot Lists

Information Warehouse: The K-map

Search Search

Catalog

Metrics

Search Search

Search Communities Search Content

Valuation

Search Expertise

K-map

Pets & Animals

Veterinary Help

Clubs & Associations

Plants & Ponds

AquariumsAquarium Keeping

Fish & Livestock

Traveling with Pets

Health & Vet Help

Cats

Birds

Unusual Pet Animals

Products & ServicesSaltwater Fish

Zoos & Aquariums

Horses

Fish & Aquariums

Advice & Guides

Category Labeling - Applies a human readable tag to a category

Clustering/Categorization - creates categories of similar documents and moves new documents into the appropriate categories (IBM Research technologies)

Classification: K-map Builder

Pets & Animals

Saltwater FishVeterinary Help

Clubs & AssociationsPlants & Ponds

Aquariums

Aquarium Keeping

Fish & Livestock

Traveling with PetsHealth & Vet Help

Cats

Birds

Unusual Pet Animals

Products & ServicesZoos & Aquariums

Horses

Fish & Aquariums

Advice & Guides Kmap Editor - Manages

relationships between documents and categories

Affinities - Matches people with categories based on their interaction with the documents in the categories

Metrics - Calculates value of documents and strength of affinities based on use

Classification: People

Retrieval: K-map Indexer

Search content across the information warehouse

Scope your searches, find only what you need

– Everything About– Documents About– Documents Authored By– People Named– People Who Know About– People Whose Profile Contains– Places About– Categories About

Presentation: K-station

Portal with common structureCreate shared places from templatesPut information in contextReuse places as templates

Presentation: K-station portlets

Association: Metrics

Metrics–Collects "Digital Breadcrumbs"

Statistics about information flow–No additional burden on users–Leverages document meta data–Analyses trends, relationships, and patterns

Authorship - documents created by personLinkage - number of links to/from a document

Messages - number of messages between two people, number of links forwarded

Activity of document or database - frequency of change, volume of change

Activity of person - frequency of system use

Association: Basic Metrics

Association: Advanced Metrics

Advanced Metrics are calculated using basic metrics and relationships between entities

Person to Topic Affinity - based on documents in the topic and the people who authored, contributed, distributed, and read

Value of Document - based on activity of document, linkage

Value of Topic - sum of Value of Documents in Topic

Reports Content and Usage Activity:–Most Active K-map Categories–Highest Document Values–Most Active Authors–Most Active/Linked To/From Documents–Most Active/Read Documents

Monitors Activity Trends over Time

Association: Metrics Reports

Tales from the trenches

Set appropriate expectations–Map to a known business process–Determine access to content stores in advance

–Anticipate some effort to create and maintain the taxonomy

–Look at existing meta-data If creating user profiles and affinities, consider privacy issues

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