Modeling Symbolic Representations

March 16, 2010

System modeling is all too often a flight for abstraction, when business analysts should instead look for the proper level of representation, ie the one with the best fit to business concerns.

Modeling is synchronic: contexts must be mapped to representations (Velazquez, “Las Meninas”).

Caminao’s blog (see Topics Guide) will try to set a path to Architecture Driven System Modelling. The guiding principle is to look at systems as sets of symbolic representations and identify the core archetypes defining how they must be coupled to their actual counterparts. That would provide for lean (need-to-know specs) and fit (architecture driven) models, architecture traceability, and built-in consistency checks.

This blog is meant to be a work in progress, with the basic concepts set open to suggestions or even refutation:

All examples are taken from ancient civilizations in order to put the focus on generic problems of symbolic architectures, disregarding technologies.

Symbolic representation: a primer

Original illustrations by Albert (http://www.albertdessinateur.com/) allow for concrete understanding of requirements, avoiding the biases associated with contrived textual descriptions.

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Focus: Requirements Reuse

February 22, 2018

Preamble

Requirements is what to feed engineering processes. As such they are to be presented under a wide range of forms, and nothing should be assumed upfront about forms or semantics.

What is to be reused: Sketches or Models  ? (John Devlin)

Answering the question of reuse therefore depends on what is to be reused, and for what purpose.

Documentation vs Reuse

Until some analysis can be carried out, requirements are best seen as documents;  whether such documents are to be ephemeral or managed would be decided depending on method (agile or phased), contents (business, supporting systems, implementation, or quality of services), or purpose (e.g governance, regulations, etc).

What is to be reused.

Setting apart external conditions, requirements documentation could be justified by:

  • Traceability of decision-making linking initial requests with actual implementation.
  • Maintenance of deliverables during their life-cycle.

Depending on development approaches, documentation could limited to archives (agile development models) or managed as intermediate products (phased development models). In the latter case reuse would entail some formatting of requirements.

The Cases for Requirements Reuse

Assuming that requirements have been properly formatted, e.g as analysis models (with technical ones managed internally at system level), reuse could be justified by changes in business, functional, or quality of services requirements:

  • Business processes are meant to change with opportunities. With requirements available as analysis models, changes would be more easily managed (a) if they could be fine-grained. Business rules are a clear example, but that could also be the case for new features added to business objects.
  • Functional requirements may change even without change of business ones, e.g if new channels and users are introduced addressing existing business functions. In that case reusable business requirements (b) would dispense with a repeat of business analysis.
  • Finally, quality of service could be affected by operational changes like localization, number of users, volumes, or frequency. Adjusting architecture capabilities would be much easier with functional (c) and business (d) requirements properly documented as analysis models.

Cases for Reuse

Along that perspective, requirements reuse appears to revolve around two pivots, documents and analysis models. Ontologies could be used to bind them.

Requirements & Ontologies

Reusing artifacts means using them in contexts or for purposes different of native ones. That may come by design, when specifications can anticipate on shared concerns, or as an afterthought, when initially unexpected similarities are identified later on. In any case, reuse policies have to overcome a twofold difficulty:

  • Visibility: business and functional analysts must be made aware of potential reuse without having to spend too much time on research.
  • Overheads: ensuring transparency, traceability, and consistency checks on requirements (documents or analysis models) cannot be achieved without costs.

Ontologies could help to achieve greater visibility with acceptable overheads by framing requirements with regard to nature (documents or models) and context:

With regard to nature, the critical distinction is between document management and model based engineering systems. When framed as ontologies, the former is to be implemented as thesaurus targeting terms and documents, the latter as ontologies targeting categories specific to organizations and business domains.

should be managed separately

With regard to context the objective should be to manage reusable requirements depending on the kind of jurisdiction and stability of categories, e.g:

  • Social: No authority, volatile, continuous and informal changes.
  • Institutional: Regulatory authority, steady, changes subject to established procedures.
  • Professional: Agreed upon between parties, steady, changes subject to established procedures.
  • Corporate: Defined by enterprises, periodic, changes subject to internal decision-making.
  • Personal: Customary, defined by named individuals (e.g research paper).

Combining contexts of reuse with architectures layers (enterprise, systems, platforms) and capabilities (Who,What,How, Where, When).

Combined with artificial intelligence, ontology archetypes could crucially extend the benefits of requirements reuse, notably through the impact of deep learning to visibility.

On a broader perspective requirements should be seen as a source of knowledge, and their reuse managed accordingly.

Further Reading

Healthcare: Tracks & Stakes

February 8, 2018

Preamble

Healthcare represents at least a tenth of developed country’s GDP, with demography pushing to higher levels year after year. In principle technology could drive costs in both directions; in practice it has worked like a ratchet: upside, innovations are extending the scope of expensive treatments, downside, institutional and regulatory constraints have hamstrung the necessary mutations of organizations and processes.

Health Care Personal Assistant (Kerry James Marshall)

As a result, attempts to spread technology benefits across healthcare activities have dwindle or melt away, even when buttressed by major players like Google or Microsoft.

But built up pressures on budgets combined with social transformations have undermined bureaucratic barriers and incumbents’ estates, springing up initiatives from all corners: pharmaceutical giants, technology startups, healthcare providers, insurers, and of course major IT companies.

Yet the wide range of players’ fields and starting lines may be misleading, incumbents or newcomers are well aware of what the race is about: whatever the number of initial track lanes, they are to fade away after a few laps, spurring the front-runners to cover the whole track, alone or through partnerships. As a consequence, winning strategies would have to be supported by a comprehensive and coherent understanding of all healthcare aspects and issues, which can be best achieved with ontologies.

Ontologies vs Models

Ontologies are symbolic constructs (epitomized by conceptual graphs made of nodes and connectors) whose purpose is to make sense of a domain of discourse:

  1. Ontologies are made of categories of things, beings, or phenomena; as such they may range from simple catalogs to philosophical doctrines.
  2. Ontologies are driven by cognitive (i.e non empirical) purposes, namely the validity and consistency of symbolic representations.
  3. Ontologies are meant to be directed at specific domains of concerns, whatever they can be: politics, religion, business, astrology, etc.

That makes ontologies a special case of uncommitted models: like models they are set on contexts and concerns; but contrary to models ontologies’ concerns are detached from actual purposes. That is precisely what is expected from a healthcare conceptual framework.

Contexts & Business Domains

Healthcare issues are set across too many domains to be effectively fathomed, not to mention followed as they change. Notwithstanding, global players must anchor their strategies to different institutional contexts, and frame their policies as to make them transparent and attractive to others players. Such all-inclusive frameworks could be built from ontologies profiled with regard to the governance and stability of contexts:

  • Social: No authority, volatile, continuous and informal changes.
  • Institutional: Regulatory authority, steady, changes subject to established procedures.
  • Professional: Agreed upon between parties, steady, changes subject to established procedures.
  • Corporate: Defined by enterprises, periodic, changes subject to internal decision-making.
  • Personal: Customary, defined by named individuals (e.g research paper).

Ontologies set along that taxonomy of contexts could then be refined as to target enterprise architecture layers: enterprise, systems, platforms, e.g:

A sample of Healthcare profiled ontologies

Depending on the scope and nature of partnerships, ontologies could be further detailed as to encompass architectures capabilities: Who, What, How, Where, When. 

Concerns & Architectures Capabilities

As pointed above, a key success factor for major players would be their ability to federate initiatives and undertakings of both incumbents and newcomers, within or without partnerships. That can be best achieved with enterprise architectures aligned with an all-inclusive yet open framework, and for that purpose the Zachman taxonomy would be the option of choice. The corresponding enterprise architecture capabilities (Who,What, How, Where, When) could then be uniformly applied to contexts and concerns:

  • Internally across architecture layers for enterprise (business and organization), systems (logical structures and functionalities), and platforms (technologies).
  • Externally across context-based ontologies as proposed above.

The nexus between environments (contexts) and enterprises (concerns) ontologies could then be organised according to the epistemic nature of items: terms, documents, symbolic representations (aka surrogates), or business objects and phenomena.

Mapping knowledge to architectures capabilities

That would outline four basic ontological archetypes that may or may not be combined:

  • Thesaurus: ontologies covering terms, concepts.
  • Document Management: thesaurus and documents.
  • Organization and Business: ontologies pertaining to enterprise organization and business processes.
  • Engineering: ontologies pertaining to the symbolic representation (aka surrogates) of organizations, businesses, and systems.

Global healthcare players could then build federating frameworks by combining domain and architecture driven ontologies, e.g:

Building federating frameworks with modular ontologies designed on purpose.

As a concluding remark, it must be reminded that the objective is to federate the activities and systems of healthcare players without interfering with the design of their business processes or supporting systems. Hence the importance of the distinction between ontologies and models introduced above which would act as a guaranty that concerns are not mixed up insofar as ontologies remain uncommitted models.

Further Reading

External Links

Ontologies as Productive Assets

January 22, 2018

Preamble

An often overlooked benefit of artificial intelligence has been a renewed interest in seminal philosophical and cognitive topics; ontologies coming top of the list.

Ontological Questioning (The Thinker Monkey, Breviary of Mary of Savoy)

Yet that interest has often been led astray by misguided perspectives, in particular:

  • Universality: one-fits-all approaches are pointless if not self-defeating considering that ontologies are meant to target specific domains of concerns.
  • Implementation: the focus is usually put on representation schemes (commonly known as Resource Description Frameworks, or RDFs), instead of the nature of targeted knowledge and the associated cognitive capabilities.

Those misconceptions, often combined, may explain the limited practical inroads of ontologies. Conversely, they also point to ontologies’ wherewithal for enterprises immersed into boundless and fluctuating knowledge-driven business environments.

Ontologies as Assets

Whatever the name of the matter (data, information or knowledge), there isn’t much argument about its primacy for business competitiveness; insofar as enterprises are concerned knowledge is recognized as a key assets, as valuable if not more than financial ones, and should be managed accordingly. Pushing the comparison still further, data would be likened to liquidity, information to fixed income investment, and knowledge to capital ventures. To summarize, assets whatever their nature lose value when left asleep and bear fruits when kept awake; that’s doubly the case for data and information:

  • Digitized business flows accelerates data obsolescence and makes it continuous.
  • Shifting and porous enterprises boundaries and markets segments call for constant updates and adjustments of enterprise information models.

But assessing the business value of knowledge has always been a matter of intuition rather than accounting, even when it can be patented; and most of knowledge shapes up well beyond regulatory reach. Nonetheless, knowledge is not manna from heaven but the outcome of information processing, so assessing the capabilities of such processes could help.

Admittedly, traditional modeling methods are too stringent for that purpose, and looser schemes are needed to accommodate the open range of business contexts and concerns; as already expounded, that’s precisely what ontologies are meant to do, e.g:

  • Systems modeling,  with a focus on integration, e.g Zachman Framework.
  • Classifications, with a focus on range, e.g Dewey Decimal System.
  • Conceptual models, with a focus on understanding, e.g legislation.
  • Knowledge management, with a focus on reasoning, e.g semantic web.

And ontologies can do more than bringing under a single roof the whole of enterprise knowledge representations: they can also be used to nurture and crossbreed symbolic assets and develop innovative ones.

Ontologies Benefits

Knowledge is best understood as information put to use; accounting rules may be disputed but there is no argument about the benefits of a canny combination of information, circumstances, and purpose. Nonetheless, assessing knowledge returns is hampered by the lack of traceability: if a part of knowledge is explicit and subject to symbolic representation, another is implicit and manifests itself only through actual behaviors. At philosophical level it’s the line drawn by Wittgenstein: “The limits of my language mean the limits of my world”;  at technical level it’s AI’s two-lanes approach: symbolic rule-based engines vs non symbolic neural networks; at corporate level implicit knowledge is seen as some unaccounted for aspect of intangible assets when not simply blended into corporate culture. With knowledge becoming a primary success factor, a more reasoned approach of its processing is clearly needed.

To begin with, symbolic knowledge can be plied by logic, which, quoting Wittgenstein again, “takes care of itself; all we have to do is to look and see how it does it.” That would be true on two conditions:

  • Domains are to be well circumscribed. 
  • A water-tight partition must be secured between the logic of representations and the semantics of domains.

That could be achieved with modular and specific ontologies built on a clear distinction between common representation syntax and specific domains semantics.

As for non-symbolic knowledge, its processing has for long been overshadowed by the preeminence of symbolic rule-based schemes, that is until neural networks got the edge and deep learning overturned the playground. In a few years’ time practically unlimited access to raw data and the exponential growth in computing power have opened the door to massive sources of unexplored knowledge which is paradoxically both directly relevant yet devoid of immediate meaning:

  • Relevance: mined raw data is supposed to reflect the geology and dynamics of targeted markets.
  • Meaning: the main value of that knowledge rests on its implicit nature; applying existing semantics would add little to existing knowledge.

Assuming that deep learning can transmute raw base metals into knowledge gold, enterprises would need to understand, assess, and improve the refining machinery. That could be done with ontological frames.

Further Reading

External Links

Open Ontologies: From Silos to Architectures

January 1, 2018

To be of any use for enterprises, ontologies have to embrace a wide range of contexts and concerns, often ill-defined for environments, rather well expounded for systems.

Circumscribed Contexts & Crossed Concerns (Robert Goben)

And now that enterprises have to compete in open, digitized, and networked environments, business and systems ontologies have to be combined into modular knowledge architectures.

Ontologies & Contexts

If open-ended business contexts and concerns are to be taken into account, the first step should be to characterize ontologies with regard to their source, justification, and the stability of their categories, e.g:

  • Social: No authority, volatile, continuous and informal changes.
  • Institutional: Regulatory authority, steady, changes subject to established procedures.
  • Professional: Agreed upon between parties, steady, changes subject to established procedures.
  • Corporate: Defined by enterprises, periodic, changes subject to internal decision-making.
  • Personal: Customary, defined by named individuals (e.g research paper).

Assuming such an external taxonomy, the next step would be to see what kind of internal (i.e enterprise architecture) ontologies can be fitted into, as it’s the case for the Zachman framework.

The Zachman’s taxonomy is built on well established concepts (Who,What,How, Where, When) applied across architecture layers for enterprise (business and organization), systems (logical structures and functionalities), and platforms (technologies). These layers can be generalized and applied uniformly across external contexts, from well-defined (e.g regulations) to fuzzy (e.g business prospects or new technologies) ones, e.g:

Ontologies, capabilities (Who,What,How, Where, When), and architectures (enterprise, systems, platforms).

That “divide to conquer” strategy is to serve two purposes:

  • By bridging the gap between internal and external taxonomies it significantly enhances the transparency of governance and decision-making.
  • By applying the same motif (Who,What, How, Where, When) across the semantics of contexts, it opens the door to a seamless integration of all kinds of knowledge: enterprise, professional, institutional, scientific, etc.

As can be illustrated using Zachman concepts, the benefits are straightforward at enterprise architecture level (e.g procurement), due to the clarity of supporting ontologies; not so for external ones, which are by nature open and overlapping and often come with blurred semantics.

Ontologies & Concerns

A broad survey of RDF-based ontologies demonstrates how semantic overlaps and folds can be sort out using built-in differentiation between domains’ semantics on one hand, structure and processing of symbolic representations on the other hand. But such schemes are proprietary, and evidence shows their lines seldom tally, with dire consequences for interoperability: even without taking into account relationships and integrity constraints, weaving together ontologies from different sources is to be cumbersome, the costs substantial, and the outcome often reduced to a muddy maze of ambiguous semantics.

The challenge would be to generalize the principles as to set a basis for open ontologies.

Assuming that a clear line can be drawn between representation and contents semantics, with standard constructs (e.g predicate logic) used for the former, the objective would be to classify ontologies with regard to their purpose, independently of their representation.

The governance-driven taxonomy introduced above deals with contexts and consequently with coarse-grained modularity. It should be complemented by a fine-grained one to be driven by concerns, more precisely by the epistemic nature of the individual instances to be denoted. As it happens, that could also tally with the Zachman’s taxonomy:

  • Thesaurus: ontologies covering terms and concepts.
  • Documents: ontologies covering documents with regard to topics.
  • Business: ontologies of relevant enterprise organization and business objects and activities.
  • Engineering: symbolic representation of organization and business objects and activities.
KM_MetaOntos

Ontologies: Purposes & Targets

Enterprises could then pick and combine templates according to domains of concern and governance. Taking an on-line insurance business for example, enterprise knowledge architecture would have to include:

  • Medical thesaurus and consolidated regulations (Knowledge).
  • Principles and resources associated to the web-platform (Engineering).
  • Description of products (e.g vehicles) and services (e.g insurance plans) from partners (Business).

Such designs of ontologies according to the governance of contexts and the nature of concerns would significantly reduce blanket overlaps and improve the modularity and transparency of ontologies.

On a broader perspective, that policy will help to align knowledge management with EA governance by setting apart ontologies defined externally (e.g regulations), from the ones set through decision-making, strategic (e.g plate-form) or tactical (e.g partnerships).

Open Ontologies’ Benefits

Benefits from open and formatted ontologies built along an explicit distinction between the semantics of representation (aka ontology syntax) and the semantics of context can be directly identified for:

Modularity: the knowledge basis of enterprise architectures could be continuously tailored to changes in markets and corporate structures without impairing enterprise performances.

Integration: the design of ontologies with regard to the nature of targets and stability of categories could enable built-in alignment mechanisms between knowledge architectures and contexts.

Interoperability: limited overlaps and finer granularity are to greatly reduce frictions when ontologies bearing out business processes are to be combined or extended.

Reliability: formatted ontologies can be compared to typed programming languages with regard to transparency, internal consistency, and external validity.

Last but not least, such reasoned design of ontologies may open new perspectives for the collaboration between cognitive humans and pretending ones.

Further Reading

External Links

2018: Clones vs Octopuses

December 4, 2017

In the footsteps of robots replacing workmen, deep learning bots look to boot out knowledge workers overwhelmed by muddy data.

Cloning Knowledge (Tadeusz Cantor, from “The Dead Class”)

Faced with that , should humans try to learn deeper and faster than clones, or should they learn from octopuses and their smart hands.

Machine Learning & The Economics of Clones

As illustrated by scan-reading AI machines, the spreading of learning AI technology in every nook and cranny introduces something like an exponential multiplier: compared to the power-loom of the Industrial Revolution which substituted machines for workers, deep learning is substituting replicators for machines; and contrary to power looms, there is no physical limitation on the number of smart clones that can be deployed. So, however fast and deep humans can learn, clones are much too prolific: it’s a no-win situation. To get out of that conundrum humans have to put their hand on a competitive edge, e.g some kind of knowledge that cannot be cloned.

Knowledge & Competition

Appraising humans learning sway over machines, one can take from Spinoza’s categories of knowledge with regard to sources:

  1. Senses (views, sounds, smells, touches) or beliefs (as nurtured by the supposed common “sense”). Artificial sensors can compete with human ones, and smart machines are much better if prejudiced beliefs are put into the equation.
  2. Reasoning, i.e the mental processing of symbolic representations. As demonstrated by AlphaGo, machines are bound to fast extend their competitive edge.
  3. Philosophy which is by essence meant to bring together perceptions, intuitions, and symbolic representations. That’s where human intelligence could beat its artificial cousin which is clueless when purposes are needed.

That assessment is bore out by evolution: the absolute dominance established by humans over other animal species comes from their use of knowledge, which can be summarized as:

  1. Use of symbolic representations.
  2. Ability to formulate and exchange representations of contexts, concerns, and policies.
  3. Ability to agree on stakes and cooperate on policies.

On that basis, the third dimension, i.e the use of symbolic knowledge to cooperate on non-zero-sum endeavors, can be used to draw the demarcation line between human and artificial intelligence:

  • Paths and paces of pursuits as part and parcel of the knowledge itself. The fact that both are mostly obviated by search engines gives humans some edge.
  • Operational knowledge is best understood as information put to use, and must include concerns and decision-making. But smart bots’ ubiquity and capabilities often sap information traceability and decisions transparency, which makes room for humans to prevail.

So humans can find a clear competitive edge in this knowledge dimension because it relies on a combination of experience and thinking and is therefore hard to clone. Organizations should make sure that’s where smart systems take back and humans take up.

Organization & Innovation

Innovation being at the root of competitive edge, understanding the role played by smart systems is a key success factor; that is to be defined by organization.

As epitomized by Henry Ford, industrial-era thinking associated innovation with top-down management and the specialization of execution:

  • At execution level manual tasks were to be fragmented and specialized.
  • At management level analysis and decision-making were to be centralized and abstracted.

That organizational paradigm puts a double restraint on innovation:

  • On execution side the fragmentation of manual tasks prevents workers from effectively assessing and improving their performances.
  • On management side knowledge is kept in conceptual boxes and bereft of feedback from actual uses.

That railing between smart brains and dumb hands may have worked well enough for manufacturing processes limited to material flows and subject to circumscribed and predictable technological changes. It didn’t last.

First, as such hierarchies necessarily grow with processes complexity, overheads and rigidity force repeated pruning. Then, flat hierarchies are of limited use when information flows are to be combined with material ones, so enterprises have to start with matrix organization. Finally, with the seamless integration of digital and material flows, perpetuating the traditional line between management and execution is bound to hamstring innovation:

  • Smart tools may be able to perform a wide range of physical tasks without human supervision, but the core of innovation core as well as its front lines are where human and machines collaborate in processing a mix of material and information flows, both learning from the experience.
  • Hierarchies and centralized decision-making are being cut out from feeders when set in networked business environments colonized by smart bots on both sides of corporate boundaries.

Not surprisingly, these innovation trends seem to tally with the social dimension of knowledge.

Learning from the Octopus

The AI revolution has already broken all historical records of footprint (everything is affected) and speed (a matter of years). Given the length of human education cycles, appraising the consequences comes with some urgency, beginning with the disposal of two entrenched beliefs:

At individual level the new paradigm could be compared to the nervous system of octopuses: each arm gets its brain and neurons, and so its own touch of knowledge and taste of decision-making.

On a broader (i.e enterprise) perspective, knowledge should be supported by two organizational layers, one direct and innovation-driven between trusted co-workers, the other networked and knowledge-driven between remote workers, trusted or otherwise.

Further Reading

External Links

Flawed Code vs Model in the Loop

November 27, 2017

Preamble

Repeated announces of looming software apocalypse may take some edge off vigilance, but repeated systems failures should be taken seriously, if only because they  appear to be rooted in a wide array of causes, from wrongly valued single parameters (e.g 911 threshold or Apple’s free pass for “root” users) to architecture obsolescence (e.g reservation systems.)

Spreading hazardous digits (Mona Hatoum)

Yet, if alarms are not to be ignored, prognoses should go beyond syndromes and remedies beyond sticking plaster: contrary to what is suggested by The Atlantic’s article, systems are much more than piles of code, and programming is probably where quality has been taken the most seriously.

Programs vs Systems

Whatever programmers’ creativity and expertise, they cannot tackle complexity across space, time, and languages: today’s systems are made of distributed interacting components, specified and coded in different languages, and deployed and modified across overlapping time-frames. Even without taking into account continuous improvements in quality, apocalypse is not to loom in the particulars of code but on their ways in the world.

Solutions should therefore be looked for at system level, and that conclusion can only be bolstered by the ubiquity of digitized business flows.

Systems are the New Babel

As illustrated by the windfalls benefiting Cobol old timers, language is arguably a critical factor, for the maintenance of legacy programs as well as for communication between stakeholders, users, and engineers.

So if problems can be traced back to languages, it’s where solutions are to be found: from programming languages (for code) to natural ones (for systems requirements), everything can be specified as symbolic representations, i.e models.

Model in the Loop

Models are generally understood as abstractions, and often avoided for that very reason. That shortsighted mind-set is made up for by concrete employs of abstractions, as illustrated by the Automotive industry and the way it embeds models in engineering processes.

Summarily, the Automotive’s Model in Loop (MiL) can be explained through three basic ideas:

  • Systems are to be understood as the combination of physical and software artifacts.
  • Insofar as both can be implemented as digits, they can be uniformly described as models.
  • As a consequence, analysis, design and engineering can be carried out through the iterative building, simulating, testing, and adjusting various combinations of hardware and software.

By bringing together physical components and code into a seamless digitized whole, MiL brings down the conceptual gap between actual elements and symbolic representations, aka models. And that leap could be generalized to a much wider range of systems.

Models are the New Code

Programming habits and the constraints imposed by the maintenance of legacy systems have perpetuated the traditional understanding of systems as a building-up of programs; hence the focus put on the quality of code. But when large, distributed, and perennial systems are concerned, that bottom-up mind-set falls short and brings about:

  • An exponential increase of complexity at system level.
  • Opacity and discontinued traceability at application level between current use and legacy code.

Both flaws could be corrected by combining top-down modeling and bottom-up engineering. That could be achieved with iterative processes carried out from both directions.

Model in the Loop meets Enterprise Architecture

From a formal perspective models are of two sorts: extensional ones operate bottom-up and associate sets of individuals with categories, intensional ones operate top-down and specify the features meant to be shared by all instances of a type. based on that understanding, the former can be used to simulate the behaviors of targeted individuals depending on categories, and the latter to prescribe how to create instances of types meant to implement categories.

As it happens, Model-in-loop combines the two schemes at component level:

  • Any combination of manual and automated solution can be used as a starting point for analysis and simulation (a).
  • Given the outcomes of simulation and tests, the architecture is revisited (b) and corresponding artifacts (software and hardware) are designed (c).
  • The new combination of artifacts are developed and integrated, ready for analysis and simulation (d).

Model in the Loop

Assuming that MiL bottom-up approach could be tallied with top-down systems engineering processes, it would enable a seamless and continuous integration of changes in software components and systems architectures.

Further Reading

External Links

Focus: Business Analyst Booklet

November 6, 2017

Objective

Business analysts stand between unbounded and moving business landscapes on one hand, distinctive and steady enterprise organization and culture on the other hand.

How to align enterprise resources and business opportunities (Patrick Zachmann)

Assuming that BAs’ primary concern is to keep ahead of the competition, framing business undertakings into universal guidelines could be counterproductive. By contrast, harnessing together versatile business processes and reliable systems architectures will clearly enhance business agility; hence the benefits of lining up enterprise architects’ and business analysts’ conceptual toolboxes:

  1. Concepts : eight exclusive and unambiguous definitions provide the conceptual building blocks.
  2. Models: how the concepts are used to consolidate business requirements and convey them to enterprise architects and software engineers.
  3. Processes: how to harness organization and business objectives and align applications with business value.
  4. Architectures: how to contrive along time the continuity and consistency of business concepts and objectives, and their congruence with systems capabilities.
  5. Governance: assessment of business value and risks.

On that basis, the objective here is not to detail BAs’ tasks or methods but to focus on core issues to be addressed by business analysts.

Concepts

Whereas systems architecture is not their primary concern, business analysts should nonetheless share the same modeling paradigm:

  • Analysis models for business environments and objectives.
  • Design models for the architecture of systems and the specification of components.

Business objects and processes must be consistently identified (#) across business and system realms.

It is worth to remind that the distinction between descriptive (aka analysis) and prescriptive (aka design) models is not arbitrary but based on logic principles: the former are extensional as they classify actual instances of business objects and activities; in contrast, the latter are intensional as they define the features and behaviors of required system artifacts.

The distinction also brings organizational benefits as it tallies with BAs’ responsibility regarding the consistency and continuity of identities and semantics of actual objects and processes (business extensions) and their symbolic counterparts (system intensions):

Relevant categories at architecture level can be neatly and unambiguously defined.

  • Actual containers represent address spaces or time frames; symbolic ones represent authorities governing symbolic representations. System are actual realizations of symbolic containers managing symbolic artifacts.
  • Actual objects (passive or active) have physical identities; symbolic objects have social identities; messages are symbolic objects identified within communications. Power-types (²) are used to partition objects.
  • Roles (aka actors) are parts played by active entities (people, devices, or other systems) in activities (BPM), or, if it’s the case, when interacting with systems (UML’s actors). Not to be confounded with agents meant to be identified independently of their behavior.
  • Events are changes in the state of business objects, processes, or expectations.
  • Activities are symbolic descriptions of operations and flows (data and control) independently of supporting systems; execution states (aka modes) are operational descriptions of activities with regard to processes’ control and execution. Power-types (²) are used to partition execution paths.

While business analysts should only be tasked with the continuous and consistent mapping of business individuals to their system surrogates, and not with their implementations, that cannot be achieved without a full and unambiguous specification of the variants and abstractions for the business objects and processes to be represented.

Languages & Models

Being in charge of requirements, business analysts can be seen as the gate-keepers of the whole engineering process. To begin with, and depending on the nature of domains, BAs can capture requirements using formal (e.g for scientific domains), specific, or natural languages. Then, requirements analysis can be carried out:

  • Iteratively in unison with development and in collaboration with software engineers (agile approach). In that case models are not necessary as requirements are expressed in natural language (users’ stories), possibly combined with domain specific languages (DSLs) for development.
  • As phased undertakings carried out independently, using a dedicated modeling language (e.g BPMN).
  • As phased undertakings carried out jointly with system analysts using a general purpose modeling language (e.g UML).

Three ways to deal with requirements analysis: business oriented and phased (BPMN), system oriented and phased (use cases), or business driven and iterative (users’ stories).

These schemes are therefore best understood as tools whose employ may overlap or be combined:

  • BPMN and UML activity diagrams have much in common.
  • Class diagram can complement BPMN for business objects, and State diagrams for processes control.
  • Use cases can be seen as describing the part of users’ stories to be supported by systems.

How BAs will employ them is to depend on business processes and projects’ objectives.

Business & Development Processes

The responsibility of BAs is about business processes, the choice of development model being left to project managers; hence the need for business analysts to be familiar with basic options:

  • Agile: business analysts collaborate with software engineers in project teams and share responsibilities from requirements to delivery.
  • Phased: roles and responsibilities are defined specifically with regard to development tasks.

With agile schemes BAs share roles and responsibilities all along, with phased ones roles and responsibilities are defined with regard to tasks.

Agile or phased, the contribution of business analysts can be defined around three core issues, corresponding to three typical modus operandi:

  • Concepts associated to business objects and activities that are to be represented. Assuming that conceptual models are meant to be stable and shared across processes, they should be under the responsibility of business analysts independently of applications.
  • Actors (users, devices, or systems) and activities. Insofar as the impact on organization and system functional features can be localized (users interfaces) or circumscribed (business rules), business analysts can collaborate and share responsibility with software engineers all along an iterative process. Otherwise (changes in organization or business functions) business analysts will have to consolidate their work with enterprise architects.
  • Processes execution. Often labelled as non functional capabilities, they essentially deal with the different aspects of user’s experience and the synchronization of changes in business environments and supporting systems. For that purpose business analysts will have to check requirements against systems capabilities.

Business analysts core concerns and MO: conceptual model, activities, and processes.

While these issues are often interwoven, sorting them out can help to match development models with projects objectives and scope: agile for projects facing business users, phased for the ones dealing with architectures; that will also help to characterize the role of BAs depending on focus: business processes (BPM, use cases, users’ stories), functional architecture (services, conceptual models), or quality of services.

Business Analysis & Systems Architectures

When considering business opportunities, business analysts have to define requirements’ footprint with regard to system capabilities:

  • Confined: applications can be developed in collaboration with software engineers from users’ stories to code, without modeling. Assuming agile conditions about shared ownership and continuous delivery are met, that would be the default option.
  • Distributed: some modeling is needed for communication and consolidation purposes. But business processes modeling languages like BPMN make no distinction between processes’ details and the shared features of supporting systems. That puts a challenging toll on business analysts (complexity, ambiguity) with limited benefits (no easy mapping to system functions).

A primary concern for business analysts should therefore to frame projects accordingly: self-contained and business driven on one hand, shared and architecture driven on the other hand, with use cases set in between if and when necessary. For that purpose shared concerns will have to be clearly identified; taking BPMN for example:

BPEA_ArchiProc

Separation of concerns: architecture backbone and processes’ details

  • Containers for physical (locations) and logical (organizations and domains) objects have no BPMN explicit equivalents.
  • Active objects have no BPMN explicit equivalent.
  • Swimlanes and pool tally with roles (aka actors)
  • Data stores tally with entities (persistent representation of business objects).
  • Tasks, transactions, and sub-processes can be translated as activities description and processes execution.

Given backbones shared with enterprise architects, the next step is to flesh them out with specific details. Depending on methods and tools, that can be done using a domain specific language (DSL) with direct implementation, or through a generic subset of BPMN that could be unambiguously mapped to design constructs, for instance:

  • Anchors (#): instances (objects or activities) directly and consistently identified across businesses and system.
  • Collections (*): set of individuals with shared features.
  • Features: attributes or operations without identity of their own.
  • Structures (diamond): composition (black) for individual components (objects or activities) whose life-cycle is bound to their owner, i.e they have no identity of their own; aggregation (white) for components identified independently but used in the context of their owner.
  • Connectors: associate individuals; their semantics is set by context: communication channel, reference, data or control flow, transition. They can bear identification (#).
  • Power-types (2): define subsets of individuals objects or activities. Depending on context and modeling language, power-types correspond to classifications, extension points, gateways, branch and joins, etc.
  • Inheritance (triangle): contrary to structure and functional connectors that deal with instances, inheritance connectors are used to describe relationships between descriptors. Strong inheritance (black) is the counterpart of composition (inheritance of structural features), and weak inheritance (white) the counterpart of aggregation (inheritance functional features).

Separation of concerns: architecture backbone and anchors details

Using the same set of well accepted and unambiguous logical constructs for both objects and behaviors can greatly enhance the consistency of analysis models as well as their traceability to designs.

Business Analysis & Knowledge Architecture

As noted above, while business analysts may have to consolidate functional requirements or check the feasibility of non functional ones with enterprise architects, they should take responsibility for conceptual models, and more generally for enterprise knowledge architecture. Taking a leaf from Davis, Shrobe, and Szolovits, that will cover:

  1. Surrogates: description of symbolic counterparts (aka) of actual objects, events and relationships.
  2. Ontological commitments: statements about the categories of things that may exist in the domain under consideration.
  3. Fragmentary theory of intelligent reasoning: model of what the things can do or can be done with.
  4. Medium for efficient computation: knowledge understandable by computers.
  5. Medium for human expression: communication between specific domain experts on one hand, generic knowledge managers on the other hand.

Putting apart users interfaces (point 5), two typical approaches can be considered:

  • Domain Driven Design (DDD), which deals with domains representation and computation from a system perspective (point 4).
  • Ontologies, which put the focus on knowledge oriented languages independently of computation (points 1-3).

Besides their simplex orientation, both fall short of business analysts needs, the former being too technical, the latter too open-ended. Instead, a conceptual framework should combine bounded domains with a compact and unambiguous knowledge oriented language.

As it happens, mapping the symbolic footprint of business domains and knowledge into systems may be dictated by the generalization of networked environments and digital business flows. Along that reasoning, BAs will have to deal with knowledge from domains as well process perspectives.

With regard to domains, a distinction should be maintained between institutional (external, statutory), business specific (external, agreed), and enterprise specific (internal).

vvv

A conceptual approach to domain layers: institutional, business specific (e.g HR management) and enterprise specific (e.g supply, sales).

With regard to processes, knowledge must be understood as the dynamic and multi-faceted outcome of data analytics, production systems, and decision-making. Taking a (revised) leaf of Zachman’s framework, business and operational objectives would be reset as to cross architecture layers instead of being aligned. Using a pentagonal representation of enterprise architecture, Zachman’s sixth column (“Why” ) would be rounded as an outer range.

Knowledge: timely and multi-faceted information put to use

Embedding IT systems in business processes is to be decisive if business intelligence (BI) is to accommodate the ubiquity of digitized business processes and the integration of enterprises with their environments. That is to require a seamless integration of data analytics and decision-making:

Data analytics (sometimes known as data mining) is best understood as a refining activity whose purpose is to process raw data into meaningful information:

  • Data understanding gives form and semantics to raw material.
  • Business understanding charts business contexts and concerns in terms of objects and processes descriptions.
  • Modeling consolidates data and business understanding into descriptive, predictive, or operational models.
  • Evaluation assesses and improves accuracy and effectiveness with regard to objectives and decision-making.

Decision-making processes in open and digitized environment are best described with the well established OODA (Observation, Orientation, Decision, Action) loop:

  1. Observation: understanding of changes in business environments (aka territories).
  2. Orientation: assessment of the reliability and shelf-life of pertaining information (aka maps) with regard to current positions and operations.
  3. Decision: weighting of options with regard to enterprise capabilities and broader objectives.
  4. Action: carrying out of decisions within the relevant time-frame.

The integration of data analytics and decision-making would be a key benefit of enterprise architecture.

OKBI_BIDM

Seamless integration of data analytics and decision-making.

On a broader perspective data analytics and decision-making can be seen as the front-offices of business intelligence, and  knowledge management as its back-office.

Governance: Metrics, Quality, & Risks

As gate-keepers, business analysts have to rank projects with regard to business value, risks, and return on investment. Assuming that business value is set independently of supporting systems, projects’ assessment and ranking should be set according to the nature of problems:

  • Intrinsic business size and complexity: requirements can be estimated from individuals (objects and activities), features, relationships, and partitions.
  • Supporting systems functionalities: intrinsic business metrics are to be combined with what is expected from supporting systems: processes and transactions, triggering events, users and devices interfaces, etc.
  • Business and functional measurements can then be weighted by non-functional (aka Quality of Service) requirements.

Assessment should be aligned with problems: business, supporting systems, operations.

If returns on investment (ROI) and risks are to be assessed consistently and decision-making carried out accordingly, value, costs, quality, and hazards have to be set within the same framework, in particular for quality and risks management:

  • Business environment: risks are external and quality is to check for timely and relevant analysis models.
  • Engineering:  risks are internal and quality is to focus on processes maturity.
  • Technologies: risks are external and quality is to address versatility, plasticity, and effectiveness of solutions.

To conclude, whereas business risks remain the primary concern of business analysts, the fusion of business and systems processes means that they can no longer ignore engineering pitfalls and the importance of quality for risks management.

Further Reading

Focus: Enterprise Architect Booklet

October 16, 2017

Objective

Given the diversity of business and organizational contexts, and EA still a fledgling discipline, spelling out a job description for enterprise architects can be challenging.

hans-vredeman-de-vries-3b

Aligning business, organization, and systems perspectives (Hans Vredeman de Vries)

So, rather than looking for comprehensive definitions of roles and responsibilities, one should begin by circumscribing the key topics of the trade, namely:

  1. Concepts : eight exclusive and unambiguous definitions provide the conceptual building blocks.
  2. Models: how the concepts are used to analyze business requirements and design systems architectures and software artifacts.
  3. Processes: how to organize business and engineering processes.
  4. Architectures: how to align systems capabilities with business objectives.
  5. Governance: assessment and decision-making.

The objective being to define the core issues that need to be addressed by enterprise architects.

Concepts

To begin with, the primary concern of enterprise architects should be to align organization, processes, and systems with enterprise business objectives and environment. For that purpose architects are to consider two categories of models:

  • Analysis models describe business environments and objectives.
  • Design models prescribe how systems architectures and components are to be developed.

Enterprise architects must focus on individuals (objects and processes) consistently identified (#) across business and system realms.

That distinction is not arbitrary but based on formal logic: analysis models are extensional as they classify actual instances of business objects and activities; in contrast, design models are intensional as they define the features and behaviors of required system artifacts.

The distinction is also organizational: as far as enterprise architecture is concerned, the focus is to remain on objects and activities whose identity (#) and semantics are to be continuously and consistently maintained across business (actual instances) and system (symbolic representations) realms:

Relevant categories at architecture level can be neatly and unambiguously defined.

  • Actual containers represent address spaces or time frames; symbolic ones represent authorities governing symbolic representations. System are actual realizations of symbolic containers managing symbolic artifacts.
  • Actual objects (passive or active) have physical identities; symbolic objects have social identities; messages are symbolic objects identified within communications. Power-types (²) are used to partition objects.
  • Roles (aka actors) are parts played by active entities (people, devices, or other systems) in activities (BPM), or, if it’s the case, when interacting with systems (UML’s actors). Not to be confounded with agents meant to be identified independently of their behavior.
  • Events are changes in the state of business objects, processes, or expectations.
  • Activities are symbolic descriptions of operations and flows (data and control) independently of supporting systems; execution states (aka modes) are operational descriptions of activities with regard to processes’ control and execution. Power-types (²) are used to partition execution paths.

Since the objective is to identify objects and behaviors at architecture level, variants, abstractions, or implementations are to be overlooked. It also ensues that the blueprints obtained remain general enough as to be uniformly, consistently and unambiguously translated into most of modeling languages.

Languages & Models

Enterprise architects may have to deal with a range of models depending on scope (business vs system) or level (enterprise and system vs domains and applications):

  • Business process modeling languages are used to associate business domains and enterprises organization.
  • Domain specific languages do the same between business domains and software components, bypassing enterprise organization and systems architecture.
  • Generic modeling languages like UML are supposed to cover the whole range of targets.
  • Languages like Archimate focus on the association between enterprises organization and systems functionalities.
  • Contrary to modeling languages programming ones are meant to translate functionalities into software end-products. Some, like WSDL (Web Service Definition Language), can be used to map EA into service oriented architectures (SOA).

Scope of Modeling Languages

While architects clearly don’t have to know the language specifics, they must understand their scope and purposes.

Processes

Whatever the languages, methods, or models, the primary objective is that architectures support business processes whenever and wherever needed. Except for standalone applications (for which architects are marginally involved), the way systems architectures support business processes is best understood with regard to layers:

  • Processes are solutions to business problems.
  • Processes (aka business solutions) induce problems for systems, to be solved by functional architecture.
  • Implementations of functional architectures induce problems for platforms, to be solved by technical architectures.

Enterprise architects should focus on the alignment of business problems and supporting systems functionalities

As already noted, enterprise architects are to focus on enterprise and system layers: how business processes are supported by systems functionalities and, more generally, how architecture capabilities are to be aligned with enterprise objectives.

Nonetheless, business processes don’t operate in a vacuum and may depend on engineering and operational processes, with regard to development for the former, deployment for the latter.

EARdmap_XProcs

Enterprise architects should take a holistic view of business, engineering, and operational processes.

Given the crumbling of traditional fences between environments and IT systems under combined markets and technological waves, the integration of business, engineering, and operational processes is to become a necessary condition for market analysis and reactivity to changes in business environment.

Architecture

Blueprints being architects’ tool of choice, enterprise architects use them to chart how enterprise objectives are to be supported by systems capabilities; for that purpose:

  • On one hand they have to define the concepts used for the organization, business domains, and business processes.
  • On the other hand they have to specify, monitor, assess, and improve the capabilities of supporting systems.

In between they have to define the functionalities that will consolidate specific and possibly ephemeral business needs into shared and stable functions best aligned with systems capabilities.

MapsTerrits_Archis

The role of functional architectures is to map conceptual models to systems capabilities

As already noted, enterprise architects don’t have to look under the hood at the implementation of functions; what they must do is to ensure the continuous and comprehensive transparency between existing as well a planned business objectives and systems capabilities.

Assessment

One way or the other, governance implies assessment, and for enterprise architects that means setting apart architectural assets and business applications:

  • Whatever their nature (enterprise organization or systems capabilities), the life-cycle of assets encompasses multiple production cycles, with returns to be assessed across business units. On that account enterprise architects are to focus on the assessment of the functional architecture supporting business objectives.
  • By contrast, the assessment of business applications can be directly tied to a business value within a specific domain, value which may change with cycles. Depending on induced changes for assets, adjustments are to be carried out through users’ stories (standalone, local impact) or use cases (shared business functions, architecture impact).

Enterprise architects deal with assets, business analysts with processes.

The difficulty of assessing returns for architectural assets is compounded by cross dependencies between business, engineering, and operational processes; and these dependencies may have a decisive impact for operational decision-making.

Business Intelligence & Decision-making

Embedding IT systems in business processes is to be decisive if business intelligence (BI) is to accommodate the ubiquity of digitized business processes and the integration of enterprises with their environments. That is to require a seamless integration of data analytics and decision-making:

Data analytics (sometimes known as data mining) is best understood as a refining activity whose purpose is to process raw data into meaningful information:

  • Data understanding gives form and semantics to raw material.
  • Business understanding charts business contexts and concerns in terms of objects and processes descriptions.
  • Modeling consolidates data and business understanding into descriptive, predictive, or operational models.
  • Evaluation assesses and improves accuracy and effectiveness with regard to objectives and decision-making.

Decision-making processes in open and digitized environment are best described with the well established OODA (Observation, Orientation, Decision, Action) loop:

  1. Observation: understanding of changes in business environments (aka territories).
  2. Orientation: assessment of the reliability and shelf-life of pertaining information (aka maps) with regard to current positions and operations.
  3. Decision: weighting of options with regard to enterprise capabilities and broader objectives.
  4. Action: carrying out of decisions within the relevant time-frame.
OKBI_BIDM

Seamless integration of data analytics and decision-making.

Along that perspective data analytics and decision-making can be seen as the front-offices of business intelligence, and  knowledge management as its back-office.

More generally that scheme epitomizes the main challenge of enterprise architects, namely the continuous and dynamic alignment of enterprise organization and systems to market environment, business processes, and decision-making.

Further Reading

On The Holistic Nature of MBSE

October 1, 2017

Preamble

Interestingly, variants of MBSE/MDSE acronyms put the focus on the duality of the concept, software on one side, systems on the other.

MBSE is by nature a two-faced endeavor (Sand Painting Navajo Rug)

As that duality operates for models, systems, and organizations, MBSE offers a holistic view on enterprise architecture.

Models and Software

Models are symbolic representations of actual contexts in line with specific purposes: requirements analysis, simulation, software design, etc. Software is a subset of models characterized by target (computer code) and language (executable instructions). Based on that understanding, MBSE should not be limited to DSLs silos and code generation but employed to bring together and manage the whole range of concerns and artifacts.

Systems and Applications

The hapless track record of Waterfall and the parallel ascent of Agile have clouded the grounds for phased development processes. But whereas agile schemes are the default option when applications can be developed independently, external dependencies prevent their scaling up to system level. That’s when system engineering takes precedence on applications development, with MBSE introduced to manage shared models and support collaboration between teams.

Organization and Projects

As epitomized by agile development models, projects can be driven by specific business needs or shared architecture capabilities. Whereas the former are best carried out iteratively by autonomous teams sharing skills and responsibility, the latter entail collaboration between organizational units along time. MBSE provides the link between standalone projects, phased processes, and enterprise organization.

MBSE provides a holistic view of organisations and systems.

By providing a holistic view of changes in organizations, systems, and software, MBSE should be a key component of enterprise architecture.

Further Reading

 

Transcription & Deep Learning

September 17, 2017

Humans looking for reassurance against the encroachment of artificial brains should try YouTube subtitles: whatever Google’s track record in natural language processing, the way its automated scribe writes down what is said in the movies is essentially useless.

A blank sheet of paper was copied on a Xerox machine.
This copy was used to make a second copy.
The second to make a third one, and so on…
Each copy as it came out of the machine was re-used to make the next.
This was continued for one hundred times, producing a book of one hundred pages. (Ian Burn)

Experience directly points to the probable cause of failure: the usefulness of real-time transcriptions is not a linear function of accuracy because every slip can be fatal, without backup or second chance. It’s like walking a line: for all practical purposes a single misunderstanding can throw away the thread of understanding, without a chance of retrieve or reprieve.

Contrary to Turing machines, listeners have no finite states; and contrary to the sequence of symbols on tapes, tales are told by weaving together semantic threads. It ensues that stories are work in progress: readers can pause to review and consolidate meanings, but listeners have no other choice than punting on what comes to they mind, hopping that the fabric of the story will carry them out.

So, whereas automated scribes can deep learn from written texts and recorded conversations, there is no way to do the same from what listeners understand. That’s the beauty of story telling: words may be written but meanings are renewed each time the words are heard.

Further Reading


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