Multi-Tenancy in Data Lakes are on the rise. When looking at multi-tenancy from the lens of data governance, a lot is changing the landscape, and the way we have been operating with respect to the governance model probably needs a rethink. It is time to think of Governance and its various entities as a first-class citizen in data architecture and bake it as part of the platform. We will look at the various aspects of governance, extending to accommodate the growing compliance and regulatory requirements and suggestive architectural approaches to realize the same.
Multi-Tenancy in Data Lakes are on the rise. When looking at multi-tenancy from the lens of data governance, a lot is changing the landscape, and the way we have been operating with respect to the governance model probably needs a rethink. It is time to think of Governance and its various entities as a first-class citizen in data architecture and bake it as part of the platform. We will look at the various aspects of governance, extending to accommodate the growing compliance and regulatory requirements and suggestive architectural approaches to realize the same.
Data Mesh is the decentralized architecture where your units of architecture is a domain driven data set that is treated as a product owned by domains or teams that most intimately know that data either creating it or they are consuming it and re-sharing it and allocated specific roles that have the accountability and the responsibility to provide that data as a product abstracting away complexity into infrastructure layer a self-serve infrastructure layer so that create these products more much more easily.
Mobile Visual Search (MVS) is a fascinating research field with many open challenges and opportunities, which have the potential to impact the way we organize, annotate, and retrieve visual data (images and videos) using mobile devices.
This talk is structured in four parts:
1. Opportunities: where I present recent and relevant numbers of the mobile computing market, particularly in the field of photography apps, social networks, and mobile search.
2. Basic concepts: where I explain the basic MVS pipeline and discuss the three main MVS scenarios and associated challenges.
3. Technical aspects: where I briefly cover topics such as feature extraction, indexing, descriptor matching, and geometric verification, discuss the state of the art in these fields, and comment on open problems and research opportunities.
4. Examples and applications: where I show representative examples of academic research and commercial apps in this field.
Glitches can occur in even the best run IT operations. In this session, Accelrys support experts will share tips and tricks for proactively managing the performance of your ELN and detailed strategies for troubleshooting issues when they arise. Discussions will draw from real-world experience and will provide you with detailed strategies to leverage Accelrys support and minimize the time required to diagnose an issue.
Prov-O-Viz is a visualisation service for provenance graphs expressed using the W3C PROV vocabulary. It uses the Sankey-style visualisation from D3js.
See http://provoviz.org
Combining Data Lake and Data Wrangling for Ensuring Data Quality in CRISAnastasija Nikiforova
This presentation is a supplementary material for the "Combining Data Lake and Data Wrangling for Ensuring Data Quality in CRIS" presented at 15th International Conference on Current Research Information Systems (CRIS2022) - Linking Research Information across data spaces. It provides an insight on the ongoing study of combining data lake as a data repository and data wrangling seeking for an increased data quality in CRIS systems, although the proposed approach is domain-agnostic and can be used not only within CRIS.
Read the article here -> Azeroual, O., Schöpfel, J., Ivanovic, D., & Nikiforova, A. (2022, May). Combining Data Lake and Data Wrangling for Ensuring Data Quality in CRIS. In CRIS2022: 15th International Conference on Current Research Information Systems --> https://hal.archives-ouvertes.fr/hal-03694519/
From collective insanity to organisational learning 2019 03 11 brisbane bus...Jorn Bettin
From collective insanity to organisational learning, by caring about the health of feedback loops at all levels of scale and all the places where they are needed. The talk will address the question of how, when, and why feedback loops may fail or get corrupted, and will explore how feedback loops relate to the creation of learning organisations and the design of better products and services.
From artificially intelligent systems towards real thinking tools and human s...Jorn Bettin
From artificially intelligent systems towards real thinking tools and human scale models that improve both human and machine learning
--
In an increasingly software and data-intensive human world, the objective of human-scale computing is to improve filtering, collaboration, thinking, and learning:
1. between humans,
2. between humans and software systems,
3. and between software systems.
This objective is another way of stating the goal of developing a 'language and interaction style' that is better than any formal or informal language reliant on linear syntax.
--------------------------------------
Jorn Bettin is a Partner at S23M and loves building and working with high-performance teams. Jorn works with top-level subject matter experts and transdisciplinary teams to uncover and activate deep domain knowledge.
Jorn has a background in mathematics and his experience covers the following industries: logistics, industrial automation, healthcare, insurance, banking, legal and accounting, telecommunications, electricity, and government.
S23M’s MODA + MODE thinking tools complement Kaizen and agile techniques, enabling people and software systems to interact in the simplest possible way. MODA + MODE techniques create bridges of understanding between disciplines and organisational silos.
Jorn is passionate about open innovation and about addressing challenges that go beyond the established framework of research in industry, government and academia via the quarterly CIIC unconference. He is a co-author of a number of books on model driven product line engineering, is an expert on semantic interoperability, and has worked in methodology leadership roles at IBM in the 1990s.
Jorn is also part of Autistic Collaboration – a mutual support hub for neurodivergent individuals and ventures, and advises clients on the creation of inclusive cultures of innovation and knowledge sharing.
Data Mesh is the decentralized architecture where your units of architecture is a domain driven data set that is treated as a product owned by domains or teams that most intimately know that data either creating it or they are consuming it and re-sharing it and allocated specific roles that have the accountability and the responsibility to provide that data as a product abstracting away complexity into infrastructure layer a self-serve infrastructure layer so that create these products more much more easily.
Mobile Visual Search (MVS) is a fascinating research field with many open challenges and opportunities, which have the potential to impact the way we organize, annotate, and retrieve visual data (images and videos) using mobile devices.
This talk is structured in four parts:
1. Opportunities: where I present recent and relevant numbers of the mobile computing market, particularly in the field of photography apps, social networks, and mobile search.
2. Basic concepts: where I explain the basic MVS pipeline and discuss the three main MVS scenarios and associated challenges.
3. Technical aspects: where I briefly cover topics such as feature extraction, indexing, descriptor matching, and geometric verification, discuss the state of the art in these fields, and comment on open problems and research opportunities.
4. Examples and applications: where I show representative examples of academic research and commercial apps in this field.
Glitches can occur in even the best run IT operations. In this session, Accelrys support experts will share tips and tricks for proactively managing the performance of your ELN and detailed strategies for troubleshooting issues when they arise. Discussions will draw from real-world experience and will provide you with detailed strategies to leverage Accelrys support and minimize the time required to diagnose an issue.
Prov-O-Viz is a visualisation service for provenance graphs expressed using the W3C PROV vocabulary. It uses the Sankey-style visualisation from D3js.
See http://provoviz.org
Combining Data Lake and Data Wrangling for Ensuring Data Quality in CRISAnastasija Nikiforova
This presentation is a supplementary material for the "Combining Data Lake and Data Wrangling for Ensuring Data Quality in CRIS" presented at 15th International Conference on Current Research Information Systems (CRIS2022) - Linking Research Information across data spaces. It provides an insight on the ongoing study of combining data lake as a data repository and data wrangling seeking for an increased data quality in CRIS systems, although the proposed approach is domain-agnostic and can be used not only within CRIS.
Read the article here -> Azeroual, O., Schöpfel, J., Ivanovic, D., & Nikiforova, A. (2022, May). Combining Data Lake and Data Wrangling for Ensuring Data Quality in CRIS. In CRIS2022: 15th International Conference on Current Research Information Systems --> https://hal.archives-ouvertes.fr/hal-03694519/
From collective insanity to organisational learning 2019 03 11 brisbane bus...Jorn Bettin
From collective insanity to organisational learning, by caring about the health of feedback loops at all levels of scale and all the places where they are needed. The talk will address the question of how, when, and why feedback loops may fail or get corrupted, and will explore how feedback loops relate to the creation of learning organisations and the design of better products and services.
From artificially intelligent systems towards real thinking tools and human s...Jorn Bettin
From artificially intelligent systems towards real thinking tools and human scale models that improve both human and machine learning
--
In an increasingly software and data-intensive human world, the objective of human-scale computing is to improve filtering, collaboration, thinking, and learning:
1. between humans,
2. between humans and software systems,
3. and between software systems.
This objective is another way of stating the goal of developing a 'language and interaction style' that is better than any formal or informal language reliant on linear syntax.
--------------------------------------
Jorn Bettin is a Partner at S23M and loves building and working with high-performance teams. Jorn works with top-level subject matter experts and transdisciplinary teams to uncover and activate deep domain knowledge.
Jorn has a background in mathematics and his experience covers the following industries: logistics, industrial automation, healthcare, insurance, banking, legal and accounting, telecommunications, electricity, and government.
S23M’s MODA + MODE thinking tools complement Kaizen and agile techniques, enabling people and software systems to interact in the simplest possible way. MODA + MODE techniques create bridges of understanding between disciplines and organisational silos.
Jorn is passionate about open innovation and about addressing challenges that go beyond the established framework of research in industry, government and academia via the quarterly CIIC unconference. He is a co-author of a number of books on model driven product line engineering, is an expert on semantic interoperability, and has worked in methodology leadership roles at IBM in the 1990s.
Jorn is also part of Autistic Collaboration – a mutual support hub for neurodivergent individuals and ventures, and advises clients on the creation of inclusive cultures of innovation and knowledge sharing.
From artificially intelligent systems towards real thinking tools and human s...Jorn Bettin
In an increasingly software and data-intensive human world, the objective of human-scale computing is to improve filtering, collaboration, thinking, and learning:
1. between humans,
2. between humans and software systems,
3. and between software systems.
This objective is another way of stating the goal of developing a 'language and interaction style' that is better than any formal or informal language reliant on linear syntax.
From project to product mindset and onwards to product platform architecturesJorn Bettin
Is it possible to stay innovative and economically manage many hundreds or even thousands of products or product variants?
Organisations interested in benefiting from a product line and product platform approach must adopt values and organisational principles that encourage the development of deep domain expertise. This includes a deep understanding of the forces that continuously change the environment of the product line. These forces can then be harnessed as part of the architectural foundation for the product line.
The pervasive digitisation of services and the desire to create and operate platforms that can support large digital service ecosystems that include many organisations, have put the spotlight on design principles for product lines, product platforms, and related organisational structures.
These slides relate to a talk at ProductTank Auckland (https://www.meetup.com/ProductTank-Auckland/events/252496542/). The video recording is available at https://twitter.com/pmauckland/status/1021272934416109568.
Advanced modelling made simple with the Gmodel metalanguageJorn Bettin
Introductory slides on semantic modelling. Not necessarily self explanatory without sound track. See http://mdi2010.lcc.uma.es/proceedings/MDI2010-Proceedings.pdf and http://semanticmodelling.blogspot.com/ for further details.
At Techbox Square, in Singapore, we're not just creative web designers and developers, we're the driving force behind your brand identity. Contact us today.
3.0 Project 2_ Developing My Brand Identity Kit.pptxtanyjahb
A personal brand exploration presentation summarizes an individual's unique qualities and goals, covering strengths, values, passions, and target audience. It helps individuals understand what makes them stand out, their desired image, and how they aim to achieve it.
Implicitly or explicitly all competing businesses employ a strategy to select a mix
of marketing resources. Formulating such competitive strategies fundamentally
involves recognizing relationships between elements of the marketing mix (e.g.,
price and product quality), as well as assessing competitive and market conditions
(i.e., industry structure in the language of economics).
Personal Brand Statement:
As an Army veteran dedicated to lifelong learning, I bring a disciplined, strategic mindset to my pursuits. I am constantly expanding my knowledge to innovate and lead effectively. My journey is driven by a commitment to excellence, and to make a meaningful impact in the world.
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Recruiting in the Digital Age: A Social Media MasterclassLuanWise
In this masterclass, presented at the Global HR Summit on 5th June 2024, Luan Wise explored the essential features of social media platforms that support talent acquisition, including LinkedIn, Facebook, Instagram, X (formerly Twitter) and TikTok.
Improving profitability for small businessBen Wann
In this comprehensive presentation, we will explore strategies and practical tips for enhancing profitability in small businesses. Tailored to meet the unique challenges faced by small enterprises, this session covers various aspects that directly impact the bottom line. Attendees will learn how to optimize operational efficiency, manage expenses, and increase revenue through innovative marketing and customer engagement techniques.
Business Valuation Principles for EntrepreneursBen Wann
This insightful presentation is designed to equip entrepreneurs with the essential knowledge and tools needed to accurately value their businesses. Understanding business valuation is crucial for making informed decisions, whether you're seeking investment, planning to sell, or simply want to gauge your company's worth.
9. Value of Knowledge
http://commons.wikimedia.org/wiki/
is hard to File:Cloud_computing_icon.svg
communicate
• It’s not tangible
• It’s not raw data
• Much of it is tacit
11. 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
12. Currency
Why does it matter?
• Information is used for operational
and strategic decision making
• It must be timely
How is it measurable?
• Define acceptable temporal delays
How can it be improved?
• Increase the level of automated system
integration
• Invest in adequate computing and network
infrastructure
13. Completeness
Why does it matter?
• Information is used for operational
and strategic decision making
• It must be sufficiently free of gaps
How is it measurable?
• Specify the sources of each piece of
information
• Distinguish between mandatory and optional
information for decision making
How can it be improved?
• Focus on relevant information and
eliminate irrelevant information
14. Security
Why does it matter?
• To enforce information ownership
• To ensure compliance with privacy legislation
• To prevent theft of information
How is it measurable?
• Strength of authentication mechanisms
• Strength of encryption mechanisms
• Level of alignment between role based access
control and job descriptions
How can it be improved?
• Introduce stronger authentication and
encryption
• Remove ambiguities from job descriptions
15. Reliability
Why does it matter?
• To avoid outages
• To prevent disasters
How is it measurable?
• Definine the acceptable minimum availability
of each information source
How can it be improved?
• Use software designs that tolerate temporary
outages of required/external services
• Invest in system and data centre
replication technology
16. Unambiguitity
Why does it matter?
• To minimise communication errors
• To prevent wrong decisions
• To prevent disasters
How is it measurable?
• Count the homonyms in each
role-specific context
How can it be improved?
• 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
17. Finadability
Why does it matter?
• To enable users to find relevant information
• To speed up decision making
• To prevent disasters
How is it measurable?
• Count how often users need to talk to
colleagues to find information that is stored in
an information system
How can it be improved?
• 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
18. 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
How is it measurable?
• Count how often users need to talk to
colleagues or need to resort to ad-hoc search
for tracing the source of an error
How can it be improved?
• Consistent use of information categories and
containers
• Automatic tagging of information with temporal
& spacial meta data
• Adherance to retention constraints
19. Simplicity
Why does it matter?
• To accommodate human cognitive limits
• To prevent wrong decisions
• To prevent disasters
How is it measurable?
• Collect artefact complexity metrics
How can it be improved?
• Intuitive representations that are developed in
collaboration with domain experts
• As needed, role-specific representations
• Provide an explicit modularisation mechanism
for all artefacts
20. Usability
Why does it matter?
• Intuitive user/system interaction
• Device independent information access
• To discourage use of non-compliant tools
How is it measurable?
• Validation by average users
How can it be improved?
• Consistency of representations across devices
• Use of high-quality icons that are developed in
collaboration with domain experts
• Ensure adequate reliability
23. 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
http://commons.wikimedia.org/wiki/
File:Photo_with_histogram.JPG
24. 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
26. 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
27. 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
28. Septers
AMSSepters Selerequ
datastor mAdequ
AMSSepters Selerequ
1st-Level Categorisation
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
meta data
operational data
29. Septers
AMSSepters Selerequ
datastor mAdequ
AMSSepters Selerequ
Definitions
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
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
30. 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
Adequate support for CR compatible Adequate support for CR compatible Adequate support for CR compatible
c
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
D E F
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
Support for development of adapters
Adequate support for generalisation/
specialisation
an extensible metametamodel
Support for development of adapters
Adequate support for generalisation/
specialisation
an extensible metametamodel
Support for development of adapters
Adequate support for generalisation/
specialisation
consume
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
produce template/transformation languages
RDBMS datastore binding (to support
referential integrity)
Support for information ownership
template/transformation languages
RDBMS datastore binding (to support
referential integrity)
Support for information ownership
template/transformation languages
RDBMS datastore binding (to support
referential integrity)
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
D F
consume
produce
produce
EF
31. Learning
Elements of knowledge acquisition
• Collaboration
• Exploration
• Observation
• Validation
• Abstraction
• Modularisation
• Representation
33. 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
34. 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
http://commons.wikimedia.org/wiki/
File:Knowledge,_observation_and_reality.svg
35. 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!
37. 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
38. 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
39. 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
40. Examples
Class : Mammal
dateOfBirth
http://commons.wikimedia.org/wiki/
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}
41. 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
42. 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
43. 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
http://commons.wikimedia.org/wiki/
Assigning a unique semantic identity to each File:Discussion.jpg
piece of information (= concept)
45. 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
46. 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
47. 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
48. 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.
50. 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
Adequate support for CR compatible
versioning, branching, locking
requirements
Support for interfaces with current
Selection criteria for a metadata
repository
Adequate support for CR compatible
versioning, branching, locking
requirements
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/
specialisation specialisation specialisation
Support for multiple terminologies/ Support for multiple terminologies/ Support for multiple terminologies/
jargons jargons jargons
Modules preserve Simplicity
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
• Roles and modular artefacts represent Selection criteria for a metadata
repository
Selection criteria for a metadata
repository
Selection criteria for a metadata
repository
the building blocks of value chains
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
D E F
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
•
Support for development of adapters Support for development of adapters Support for development of adapters
Optimise within the
Adequate support for generalisation/ Adequate support for generalisation/ Adequate support for generalisation/
specialisation specialisation specialisation
Support for multiple terminologies/ Support for multiple terminologies/ Support for multiple terminologies/
jargons jargons jargons
organisational context of customers,
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
suppliers, and available skills
Adequate support for role based Adequate support for role based Adequate support for role based
access control access control access control
role based unit of work
52. Selection criteria for a metadata
repository
Adequate support for CR compatible
versioning, branching, locking
requirements
ab Selection criteria for a metadata
repository
Adequate support for CR compatible
versioning, branching, locking
requirements
Selection criteria for a metadata
repository
Adequate support for CR compatible
versioning, branching, locking
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
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/
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
ac
access control access control access control
ad Shared Language
Selection criteria for a metadata
repository
Adequate support for CR compatible
versioning, branching, locking
requirements
Support for interfaces with current
de 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
Adequate support for CR compatible
versioning, branching, locking
requirements
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
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/
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
df
58. 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