Sharpening your collaborative edge

Sharpening Your
Collaborative Edge

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File:Machete_Bolo.JPG

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© 2011 Jorn Bettin Unported (CC BY-SA 3.0)

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Knowledge generates power

Converting raw data and tacit knowledge into
Relevant Information and Explicit Knowledge

Step 1: High quality information

Step 2: Validated Knowledge

Step 3: Shared Understanding

Step 4: Actions & Results

Quality
http://commons.wikimedia.org/wiki/File:JonWoodApril2007Texas.jpg

Relevance ...

http://commons.wikimedia.org/wiki/File:Administrative_burden.JPG

Value of Knowledge

is hard to File:Cloud_computing_icon.svg

communicate
• It’s not tangible

• It’s not raw data

• Much of it is tacit

Measuring
Quality of Information
Relevant dimensions
1. Accuracy
2. Currency
3. Completeness
4. Security
5. Reliability
6. Unambiguity
7. Findability
8. Traceability
9. Simplicity
10. Usability

Accuracy
Why does it matter?
• Information is used for operational
and strategic decision making
• It must be trustworthy

How is it measurable?
• Define acceptable tolerance intervals

How can it be improved?
• Focus on relevant information and eliminate
irrelevant information

Currency
Why does it matter?
• It must be timely

• Define acceptable temporal delays

• Increase the level of automated system
integration
• Invest in adequate computing and network
infrastructure

Completeness
Why does it matter?
• It must be sufficiently free of gaps

• Specify the sources of each piece of
information
• Distinguish between mandatory and optional
information for decision making

• Focus on relevant information and
eliminate irrelevant information

Security
Why does it matter?
• To enforce information ownership
• To ensure compliance with privacy legislation
• To prevent theft of information

• Strength of authentication mechanisms
• Strength of encryption mechanisms
• Level of alignment between role based access
control and job descriptions

• Introduce stronger authentication and
encryption
• Remove ambiguities from job descriptions

Reliability
Why does it matter?
• To avoid outages
• To prevent disasters

• Definine the acceptable minimum availability
of each information source

• Use software designs that tolerate temporary
outages of required/external services
• Invest in system and data centre
replication technology

Unambiguitity
Why does it matter?
• To minimise communication errors
• To prevent wrong decisions

• Count the homonyms in each
role-specific context

• Establish a comprehensive
registry of concepts
• Use concepts names that are tailored to the
role-specific context
• Use semantic identities instead of names
when communicating information

Finadability
Why does it matter?
• To enable users to find relevant information
• To speed up decision making

• Count how often users need to talk to
colleagues to find information that is stored in
an information system

• Provide advanced support for queries
• Make the query engine aware of the role-
specific context
• Allow query by information category, by
container, by name, and by semantic identity

Traceability
Why does it matter?
• To speed up root cause analysis of errors
• To speed up the learning curve for newcomers
• To meet legal & regulatory compliance needs

• Count how often users need to talk to
colleagues or need to resort to ad-hoc search
for tracing the source of an error

• Consistent use of information categories and
containers
• Automatic tagging of information with temporal
& spacial meta data
• Adherance to retention constraints

Simplicity
Why does it matter?
• To accommodate human cognitive limits
• To prevent wrong decisions

• Collect artefact complexity metrics

• Intuitive representations that are developed in
collaboration with domain experts
• As needed, role-specific representations
• Provide an explicit modularisation mechanism
for all artefacts

Usability
Why does it matter?
• Intuitive user/system interaction
• Device independent information access
• To discourage use of non-compliant tools

• Validation by average users

• Consistency of representations across devices
• Use of high-quality icons that are developed in
collaboration with domain experts
• Ensure adequate reliability

Accelerating the Evolution ...

of Knowledge

File:Wentletrap_001.jpg

Examples Septers
AMS datastore
bisupport for
role based
access control
criel

transSate
SelerequmAdequ
ate support for
role based
Surce template/control
access

support for role
based access
control

A language artefact is a non-hardware artefact
• information content of pheromones
• information content of body language
• live music
• live speech
• information content in traditional symbolic notations
• program/diagram/hypertext/database content
• information content of recorded sound/pictures/videos
• information content of genetic material

File:Photo_with_histogram.JPG

Definition Septers
AMS datastore
bisupport for
role based
access control
criel

transSate
SelerequmAdequ
ate support for
role based
Surce template/control
access

support for role
based access
control

A language artefact
• is a container of information
• is instantiated by a specific actor (human or system)
• is consumed by at least one actor (human or system)
• represents a natural unit of work (for the instantiating & consuming actors)
• may contain links to other artefacts
• has a state and a lifecycle

Definition
Septers
AMS datastore
Septers
bisupport for
role AMS datastore
basedSepters
SelerequmAdequ
access control for ate support for
bisupport
role AMS datastore
criel
based role SelerequmAdequ
Septers based
access control for atecontrol for
Surce template/ support
bisupport access SelerequmAdequ
role AMS datastore
criel
based
transSate role based
access for template/control for
Surce role ate support
bisupport for
supportcontrol access SelerequmAdequ
role based
criel
transSate role based
based access template/control for
access for role ate support
supportcontrol
Surce
control transSate
criel
access
role based
based access template/control
Surce access
support for role
control transSate
based access
support for role
control
based access
control

Software is an arbitrary set of
language artefacts

Septers
AMSSepters Selerequ
datastor mAdequ
AMSSepters Selerequ

Software Producers
e datastorate Selerequ
AMSSepters
criel mAdequ
bisuppor supportSelerequ
e Surce ate
datastor mAdequ
AMS
criel
bisuppor support
e Surce ate
datastor
template role mAdequ
criel
for
/bisuppor support
e Surce ate
template role
criel
for
/bisuppor role
transSat forsupport
template
Surce
transSat for role
/ template
transSat
/
transSat

software
developers

software systems
& other humans

Septers
AMSSepters Selerequ
datastor mAdequ
AMSSepters Selerequ

1st-Level Categorisation
AMSSepters
criel mAdequ
e Surce ate
datastor mAdequ
AMS
criel
bisuppor support
e Surce ate
datastor
criel
for
/bisuppor support
e Surce ate
template role
criel
for
/bisuppor role
transSat forsupport
template
Surce
transSat for role
/ template
transSat
/
transSat

meta data

operational data

Septers
AMSSepters Selerequ
datastor mAdequ
AMSSepters Selerequ

Definitions
AMSSepters
criel mAdequ
e Surce ate
datastor mAdequ
AMS
criel
bisuppor support
e Surce ate
datastor
criel
for
/bisuppor support
e Surce ate
template role
criel
for
/bisuppor role
transSat forsupport
template
Surce
transSat for role
/ template
transSat
/
transSat

the categories (= meta data) must
be relevant to the organisation

Data, Information, Knowledge
• uncategorised data has very little value
• categorised data is valuable information
• information combined with an understanding
of its usage context is valuable knowledge

Value

produce

produce
consume
B
Chain
Selection criteria for a metadata Selection criteria for a metadata Selection criteria for a metadata
repository repository repository
Adequate support for CR compatible Adequate support for CR compatible Adequate support for CR compatible
versioning, branching, locking versioning, branching, locking versioning, branching, locking
requirements requirements requirements
Support for interfaces with current Support for interfaces with current Support for interfaces with current
commercial products (eg ERWin) commercial products (eg ERWin) commercial products (eg ERWin)
Metamodelling capability and ideally Metamodelling capability and ideally Metamodelling capability and ideally
an extensible metametamodel an extensible metametamodel an extensible metametamodel

A B C
Support for development of adapters Support for development of adapters Support for development of adapters
Adequate support for generalisation/ Adequate support for generalisation/ Adequate support for generalisation/

produce consume
specialisation specialisation specialisation
Support for multiple terminologies/ Support for multiple terminologies/ Support for multiple terminologies/
jargons jargons jargons
Integration with open source Integration with open source Integration with open source
template/transformation languages template/transformation languages template/transformation languages
RDBMS datastore binding (to support RDBMS datastore binding (to support RDBMS datastore binding (to support
referential integrity) referential integrity) referential integrity)
Support for information ownership Support for information ownership Support for information ownership
Adequate support for role based Adequate support for role based Adequate support for role based
access control access control access control

A C
me Selection criteria for a metadata Selection criteria for a metadata Selection criteria for a metadata

onsu
repository repository repository

c

D E F
an extensible metametamodel
Support for development of adapters
Adequate support for generalisation/
specialisation
specialisation
specialisation
consume

produce template/transformation languages
RDBMS datastore binding (to support
referential integrity)
Support for information ownership
template/transformation languages
template/transformation languages

D F
consume

produce

produce

EF

Learning

Elements of knowledge acquisition
• Collaboration
• Exploration
• Observation
• Validation
• Abstraction
• Modularisation
• Representation

Collaboration

“We are smarter than me”
Jean-Marie Favre
Software Anthropologist

Exploration

Raw data acquired by
exploration is essential for
understanding an unknown
domain
• Data can be analysed and categorised
• Lack of data only leads to speculation

Observation

Connecting the dots – building a mental model
• Associating information with time,
space, and other attributes of origin
Tacit
• Noticing possible associations
between different pieces of information

File:Knowledge,_observation_and_reality.svg

Validation

Confirming observations
• Using the scientific method
• By comparing with observations from
others
• By involving domain experts
from related disciplines
• Remember: we are smarter than me!

Abstraction

Look for Commonalities
• Avoid repetition
• Identify patterns
• Remember: KISS!
Photographer Kurt Salzmann -
www.salzmaenner.ch

Modularisation

Modules preserve Simplicity
• Rely on role-based
separation of concerns
• Modules must correspond to a natural http://commons.wikimedia.org/wiki/
File:Modular_origami.jpg
unit of work
• Roles and modular artefacts represent the
building blocks of value chains
• Optimise within the
organisational context of customers,
suppliers, and available skills

Representation

Modelling is about clarity
• Balancing act between simplicity
and not compromising the desired intent
• Focus is on human cognitive abilities & limits
• As needed use multiple syntax elements
(visual containers, symbols, text,
mathematical expressions)
• Borrow syntax from established languages,
or design syntax in close collaboration
with the user community

Code

All models are code
a system of symbols used for
• identification
• classification in the sense of grouping
a system of signals used to send messages
a set of conventions governing behaviour

Modelling is meta coding
to improve clarity of code

Examples
Class : Mammal
dateOfBirth


Class : Dog Class : Cat
isPoliceDog [2] [2]

[*] [*]

Dog : Jack Cat : Coco
{1/5/03, yes} {4/3/07}

Dog : Susie Cat : Peter
{1/2/00, no} {10/9/98}

Communication Costs

Not all code is a model
• a system of signals that includes a
translation of messages to deal with someone else’s syntax
• a system of symbols used for
classification in the sense of obfuscation or encryption

http://commons.wikimedia.org/wiki/File:Encryption_-_decryption.svg

Today

Software suffers from the
same problems as way back
when natural language evolved to enrich the
exchange between humans

Increasingly the artefacts exchanged between
humans are neither hardware nor natural
language (encoded in speech or symbolic
notation)

All language artefacts share the probems of
natural language: unanticipated interpretations

Minimising Unanticipated
Interpretation

Requires collaboration and
good will between artefact
producers & all consumers
Associating information with its usage context

Respecting the notational and terminological
preferences of all parties

Assigning a unique semantic identity to each File:Discussion.jpg
piece of information (= concept)

Semantic Modelling

C
A

B

Semantic Modelling
1. Identification of concepts
from a specific view point and
assigment of semantic identities
x t
ne

2. Modelling

ne
xt

3. Naming of concepts in as many
terminologies as required by
artefact producers and consumers

Models Semantic Domains

Semantics = Meaning
• Based on the mathematics of
model theory & denotational semantics
• Constitutes a solid foundation for
information engineering & knowledge activation
• Not the same as modelling with the
Resource Description Framework (Semantic Web)
• Not the same as
classical entity-relationship modelling
• Not the same as
object-oriented modelling

Models Sets of Meaning

Sets of Meaning = Understanding

• Focuses on the
meaning of information in a concrete usage context
• Converts tacit knowledge into shared understanding
for use by humans and software tools
• The Resource Description Framework
only partially implements denotational semantics
• Entity-relationship schemas
lack a mechanism for modularity
• Object-oriented models are
limited to one level of instantiation

Models Understanding

Model Theory
Without delving into the formal mathematical details, the significance of model theory is
best appreciated intuitively by considering the following observations:
• Formal lingustics as pioneered by Noam Chomsky in the 1950s and 1960s can be
expressed as a special case of model theory.
• The work of model theorists goes back to the beginning of the 20th century, and
was motivated by mathematicians who were concerned about potential logical
inconsistencies in the mathematical symbol system and the conventions governing
its use.
• The resulting research into symbol systems has led to a mathematical theory that
can be used to formalise any symbol system, not limited to the languages invented
by humans, and including the genetic code.
• The pictures produced on flip charts and white boards constitute domain specific
languages as well, and with the help of their authors, sets of pictures can easily be
formalised mathematically, using a specialised software tool for semantic modelling.

A B C

Semantic Domains

D EF F

Modular Models
separation of
concerns Selection criteria for a metadata
repository
Adequate support for CR compatible
versioning, branching, locking
requirements
Support for interfaces with current
Selection criteria for a metadata
repository
requirements
repository
requirements

A B C

Modules preserve Simplicity

• Roles and modular artefacts represent Selection criteria for a metadata
repository
repository
repository

the building blocks of value chains

D E F

•

Optimise within the

organisational context of customers,

suppliers, and available skills

role based unit of work

Connected
Semantic Domains

A B C

D E F

= Shared
Understanding

repository
requirements
ab Selection criteria for a metadata
repository
requirements
repository
requirements

ac

ad Shared Language

repository
requirements
de Selection criteria for a metadata
repository
requirements
repository
requirements

df

Jargon = Words + Symbols

ef de df ad cf ac bc ab

Perspective

Jargon
df
D F

View Point

Jargons signal Shared Understanding

Reflexive Jargon

DSML
f
F F

View Point

DSML = Domain Specific Modelling Language

Jargons evolve on top of
Shared Semantic Subdomains
ab ac bc

A B C
ad cf
D E F

de df ef

Evolution

Software continuously evolves,
whether we like it or not
Software shapes us
and we attempt to shape software
A dynamic system
with increasingly fast feedback loops

More Information
Knowledge Activation &
http://jornbettin.com
Risk Management

Gmodel Team Blog the-software-artefact.blogspot.com

The Role of Artefacts tiny.cc/artefacts

From Muddling to Modelling tiny.cc/muddleToModel

Model Oriented Domain
tiny.cc/domainanalysis
Analysis

Thank you
Jorn Bettin jbettin @ ibrs.com.au
+61 424 758 540 www.ibrs.com.au

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