What To Expect From A Framework

Preamble

Frames are meant to bring light on artifacts, not to cover them with veils; on that account, frameworks’ primary justification should be their leverage on engineering practices.

expoGX
Frames are meant to bring light on artifacts (Y. Levin)

It ensues that enterprises frameworks should only be considered in relation to actual contents (organization, artifacts, processes, …), and their benefits for core governance issues, in particular measurements, processes integration, and economic intelligence.

Prerequisite: Measurements

The heterogeneity of metrics is clearly a major impediment to assessment at enterprise level due to the different yardsticks associated with business value, development costs, and processes maturity.

The least frameworks should do for enterprise architects is to provide common bearings at both application and enterprise levels:

  • Application level: a consolidated scale for the mapping of value points, function points, and development costs.
  • Enterprise level: a common basis for a reliable assessment of systems capabilities and processes maturity.

A detailed account of how a framework would support such bearings should therefore be a prerequisite.

Cornerstone: Business & Engineering Processes Integration

Except for the Zachman framework, providers haven’t much to say about how actual business and engineering processes are to be hung to the proposed framework, and more generally about the continuity with existing methods, tools, and practices.

Conversely, being specific about the hinges linking it to processes is to give credence to a framework leveraging impact:

  • Business: leveraged agility from the harnessing of users’ stories to business functions.
  • Engineering: leveraged transparency, traceability and reuse from model based systems engineering (MBSE).

As the cornerstone of any enterprise framework, integration is to support their ultimate purpose, namely economic intelligence.

Deal Breaker: Economic Intelligence

Economic intelligence is usually understood as a merge of data analytics, information architecture, business intelligence, and decision-making; as such it can be seen as a primary justification for enterprise architecture frameworks.

As a corollary, one would expect frameworks to be specific with regard to the integration of data analytics, information processing, and knowledge management:

  • To begin with, no framework should be considered without being explicit about the ways such integration could be achieved.
  • Then, integration schemes should aim at being neutral with regard to engineering or support environments deployed at architecture level.

When options are considered, that should be taken as a deal breaker.

Conclusion

Assuming that expectations about measurements and processes could be reasonably borne out, decisions would rest on economic intelligence strategies.

Depending on the weight of information systems legacy, full neutrality may deem a too ambitious strategy. In that case an enterprise architecture framework could be built bottom up from MBSE environments.

Given that proviso, a neutral framework designed on purpose would bring much more leverage due to the generalization of well-defined slots and interfaces for business and engineering platforms; that would also facilitate modernization and consolidate quality assurance across enterprise architectures.

Finally, for EA frameworks to ensure consistent metrics, processes integration, and economic intelligence, knowledge management must encompass a wide range of concerns (business, engineering, regulations, …), contexts (environments, enterprise, systems), and resources (texts, models, people, …); that can be best achieved with profiled ontologies.

Further Reading

 

GDPR Ontological Primer

Preamble

European Union’s General Data Protection Regulation (GDPR), to come into effect  this month, is a seminal and momentous milestone for data privacy .

Nothing Personal (Arthur Szyk)

Yet, as reported by Reuters correspondents, European enterprises and regulators are not ready; more worryingly, few (except consultants) are confident about GDPR direction.

Misgivings and uncertainties should come as no surprise considering GDPR’s two innate challenges:

  • Regulating privacy rights represents a very ambitious leap into a digital space now at the core of corporate business strategies.
  • Compliance will not be put under a single authority but be overseen by an assortment of national and regional authorities across the European Union.

On that account, ontologies appear as the best (if not the only) conceptual approach able to bring contexts (EU nations), concerns (business vs privacy), and enterprises (organization and systems) into a shared framework.

A workbench built with the Caminao ontological kernel is meant to explore the scope and benefits of that approach, with a beta version (Protégé/OWL 2) available for comments on the Stanford/Protégé portal using the link: Caminao Ontological Kernel (CaKe_GDPR).

Enterprise Architectures & Regulations

Compared to domain specific regulations, GDPR  is a governance-oriented regulation set across business concerns and enterprise organization; but unlike similarly oriented ones like accounting, GDPR is aiming at the nexus of business competition, namely the processing of data into information and knowledge. With such a strategic stake, compliance is bound to become a game-changer cutting across business intelligence, production systems, and decision-making. Hence the need for an integrated, comprehensive, and consistent approach to the different dimensions involved:

  • Concepts upholding businesses, organizations, and regulations.
  • Documentation with regard to contexts and statutory basis.
  • Regulatory options and compliance assessments
  • Enterprise systems architecture and operations

Moreover, as for most projects affecting enterprise architectures, carrying through GDPR compliance is to involve continuous, deep, and wide ranging changes that will have to be brought off without affecting overall enterprise performances.

Ontologies arguably provide a conclusive solution to the problem, if only because there is no other way to bring code, models, documents, and concepts under a single roof. That could be achieved by using ontologies profiles to frame GDPR categories along enterprise architectures models and components.

CakeGDPR_00.jpg
Basic GDPR categories and concepts (black color) as framed by the Caminao Kernel

Compliance implementation could then be carried out iteratively across four perspectives:

  • Personal data and managed information
  • Lawfulness of activities
  • Time and Events
  • Actors and organization.

Data & Information

To begin with, GDPR defines ‘personal data’ as “any information relating to an identified or identifiable natural person (‘data subject’)”. Insofar as logic is concerned that definition implies an equivalence between ‘data’ and ‘information’, an assumption clearly challenged by the onslaught of big data: if proofs were needed, the Cambridge Analytica episode demonstrates how easy raw data can become a personal affair. Hence the need to keep an ontological level of indirection between regulatory intents and the actual semantics of data as managed by information systems.

CakeGDPR_data
Managing the ontological gap between regulatory understandings and compliance footprints

Once lexical ambiguities set apart, the question is not so much about the data bases of well identified records than about the flows of data continuously processed: if identities and ownership are usually set upfront by business processes, attributions may have to be credited to enterprises know-how if and when carried out through data analytics.

Given that the distinctions are neither uniform, exclusive or final, ontologies will be needed to keep tabs on moves and motives. OWL 2 constructs (cf annex) could also help, first to map GDPR categories to relevant information managed by systems, second to sort out natural data from nurtured knowledge.

Activities & Purposes

Given footprints of personal data, the objective is to ensure the transparency and traceability of the processing activities subject to compliance.

Setting apart (see below for events) specific add-ons for notification and personal accesses,  charting compliance footprints is to be a complex endeavor: as there is no reason to assume some innate alignment of intended (regulation) and actual (enterprise) definitions, deciding where and when compliance should apply potentially calls for a review of all processing activities.

After taking into account the nature of activities, their lawfulness is to be determined by contexts (‘purpose limitation’ and ‘data minimization’) and time-frames (‘accuracy’ and ‘storage limitation’). And since lawfulness is meant to be transitive, a comprehensive map of the GDPR footprint is to rely on the logical traceability and transparency of the whole information systems, independently of GDPR.

That is arguably a challenging long-term endeavor, all the more so given that some kind of Chinese Wall has to be maintained around enterprise strategies, know-how, and operations. It ensues that an ontological level of indirection is again necessary between regulatory intents and effective processing activities.

Along that reasoning compliance categories, defined on their own, are first mapped to categories of functionalities (e.g authorization) or models (e.g use cases).

CakeGDPR_activ1
Compliance categories are associated upfront to categories of functionalities (e.g authorization) or models (e.g use cases).

Then, actual activities (e.g “rateCustomerCredit”) can be progressively brought into the compliance fold, either with direct associations with regulations or indirectly through associated models (e.g “ucRateCustomerCredit” use case).

CakeGDPR_activ2
Compliance as carried out through Use Case

The compliance backbone can be fleshed out using OWL 2 mechanisms (see annex) in order to:

  • Clarify the logical or functional dependencies between processing activities subject to compliance.
  • Qualify their lawfulness.
  • Draw equivalence, logical, or functional links between compliance alternatives.

That is to deal with the functional compliance of processing activities; but the most far-reaching impact of the regulation may come from the way time and events are taken into account.

Time & Events

As noted above, time is what makes the difference between data and information, and setting rules for notification makes that difference lawful. Moreover, by adding time constraints to the notifications of changes in personal data, regulators put systems’ internal events on the same standing as external ones. That apparently incidental departure echoes the immersion of systems into digitized business environments, making all time-scales equal whatever their nature. Such flattening is to induce crucial consequences for enterprise architectures.

That shift together with the regulatory intent are best taken into account by modeling events as changes in expectations, physical objects, processes execution, and symbolic objects, with personal data change belonging to the latter.

Gdpr events
Mapping internal (symbolic) and external (actual) events is a critical element of GDPR compliance

Putting apart events specific to GDPR (e.g data breaches), compliance with regard to accuracy and storage limitation regulations will require that all events affecting personal data:

  • Are set in time-frames, possibly overlapping.
  • Have notification constraints properly documented.
  • Have likelihood and costs of potential risks assessed.

As with data and activities, OWL 2 constructs are to be used to qualify compliance requirements.

Actors & Organization

GDPR introduces two specific categories of actors (aka roles): one (data subject) for natural persons, and one for actors set by organizations, either specifically for GDPR assignment, or by delegation to already defined actors.

Gdpr actors
GDPR roles can be set specifically or delegated

OWL 2 can then be used to detail how regulatory roles can be delegated to existing ones, enabling a smooth transition and a dynamic adjustment of enterprise organization with regulatory compliance.

It must be stressed that the semantic distinction between identified agents (e.g natural persons) and the roles (aka UML actors) they play in processes is of particular importance for GDPR compliance because who (or even what) is behind an actor interacting with a system is to remain unknown to the system until the actor can be authentically identified. If that ontological lapse is overlooked there is no way to define and deal with security, confidentiality or privacy regulations.

Conclusion

The use of ontologies brings clear benefits for regulators, enterprise governance, and systems architects.

Without shared conceptual guidelines chances are for the European regulatory orchestra to get lost in squabbles about minutiae before sliding into cacophony.

With regard to governance, bringing systems and regulations into a common conceptual framework is to enable clear and consistent compliance strategies and policies, as well as smooth learning curves.

With regard to architects, ontology-based compliance is to bring cross benefits and externalities, e.g from improved traceability and transparency of systems and applications.

Annex A: Mapping Regulations to Models (sample)

To begin with, OWL 2 can be used to map GDPR categories to relevant resources as managed by information systems:

  • Equivalence: GDPR and enterprise definitions coincide.
  • Logical intersection, union, complement: GDPR categories defined by, respectively, a cross, merge, or difference of enterprise definitions.
  • Qualified association between GDPR and enterprise categories.

Assuming the categories properly identified, the language can then be employed to define the sets of regulated instances:

  • Logical property restrictions, using existential and universal quantification.
  • Functional property restrictions, using joints on attributes values.

Other constructs, e.g cardinality or enumerations, could also be used for specific regulatory constraints.

Finally, some OWL 2 built-in mechanisms can significantly improve the assessment of alternative compliance policies by expounding regulations with regard to:

  • Equivalence, overlap, or complementarity.
  • Symmetry or asymmetry.
  • Transitivity
  • etc.

Annex B: Mapping Regulations to Capabilities

GDPR can be mapped to systems capabilities using well established Zachman’s taxonomy set by crossing architectures functionalities (Who,What,How, Where, When) and layers (business and organization), systems (logical structures and functionalities), and platforms (technologies).

Rules_GDPR
Regulatory Compliance vs Architectures Capabilities

These layers can be extended as to apply uniformly across external ontologies, 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).

Such mapping is to significantly enhance the transparency of regulatory policies.

Further Reading

External Links

EA: The Matter of Layers

As the world turns digital,traditional fences between social, businesses, and systems realms are progressively crumbling. That brings new challenges for enterprises governance, in particular when manifold business stakes and IT systems are concerned.

tonyCragg_bottles
Layers & labels (T. Cragg)

Supposedly, enterprise architecture would deal with the framing of enterprises and systems concerns into a single paradigm. Yet spirited controversies persist between bottom up and top down approaches, the former trying to upgrade the footprint of IT systems to enterprise level, the latter ready to downgrade these systems to equipment level. But dissent in that case means unfinished business: like diggers tunneling from opposite directions, both groups are to succeed together or fail together. For that to be achieved common sense dictates that both teams agree on target, with each one getting its specific orientation right.

What to look for

Issue (information systems) and circumstances (digitization of business environment) put the focus on the relationship between business processes and enterprises organization and how to capture, manage, and use information.

On that account, and not surprisingly, understandings differ between EA proponents:

  • Bottom-up approaches are focused on the distinction between processes, applications, and data, overlooking key enterprise architecture concerns (a).
  • Top-down approaches come with a better understanding of EA stakes but fall short of the conceptual bridge between organization and business environments (b) .
EASquare_persp
Bottom-up (a) and top-down (b) approaches to EA

These shortcomings can be mended and approaches made to converge.

How to get there

As already noted, EA can only succeed as a discipline if systems and enterprise perspectives can be crossed, i.e if bottom-up and top-down approaches can be joined. That cannot be achieved along the outdated Process/Application/Data layers:

To begin with, the distinction between application and data, inherited from traditional programming, goes against both object-oriented design and service oriented architectures; then, processes don’t describe architectures but the way they are used.

On a broader perspective, if the impact of digitized business environments on EA is to be taken into account, data and information are to be redefined in a new paradigm, the former associated with a raw input, to be mined from the business environment and processed into the latter. It ensues that (1) data becomes irrelevant for architecture concerns and, (2) information becomes a key asset for enterprise architecture.

Merging applications and data into a logical/functional layer between business and engineering processes also critically redefines the perspective: instead of a being a collection of applications, business processes become the nexus of the architecture.

EASquare_sys
Introducing a functional layer between business and engineering processes

With a bottom-up EA perspective focused on business and engineering processes, a top-down counterpart has to be set from enterprise perspective that would ensure a meeting of minds around business processes.

That can be readily achieved by keeping processes as pivot between business environments and objectives on one side, enterprise organization on the other side:

EASquare2_eam
Processes are the nexus of enterprise and engineering concerns.

Enterprise architects could then focus on the mapping of business functions to services, the alignment of quality of services with architecture capabilities, and the flows of information across the organization.

Why It Matters

A proper understanding of architecture layers is not an academic concern to be overlooked. As a matter of fact, what is at stake is the very practical purpose of EA: display of boxes and arrows or effective handling of the spindle between business processes and architectural assets. Whereas anything will do for the former, the latter cannot be achieved without a principled and effective coupling between enterprise models and systems engineering.

Further Reading

External Links

Focus: Requirements Reuse

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.
  • Acceptance.
  • 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.

Documents, models, and capabilities 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:

  • Institutional: Regulatory authority, steady, changes subject to established procedures.
  • Professional: Agreed upon between parties, steady, changes subject to accord.
  • Corporate: Defined by enterprises, changes subject to internal decision-making.
  • Social: Defined by usage, volatile, continuous and informal changes.
  • 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 for visibility.

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

Further Reading

Healthcare: Tracks & Stakes

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:

  • Institutional: Regulatory authority, steady, changes subject to established procedures.
  • Professional: Agreed upon between parties, steady, changes subject to accord.
  • Corporate: Defined by enterprises, changes subject to internal decision-making.
  • Social: Defined by usage, volatile, continuous and informal changes.
  • 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

Unified Architecture Framework Profile (UAFP): Lost in Translation ?

Synopsis

The intent of Unified Architecture Framework Profile (UAFP) is to “provide a Domain Meta-model usable by non UML/SysML tool vendors who may wish to implement the UAF within their own tool and metalanguage.”

Detached Architecture (Víctor Enrich)

But a meta-model trying to federate (instead of bypassing) the languages of tools providers has to climb up the abstraction scale above any domain of concerns, in that case systems architectures. Without direct consideration of the domain, the missing semantic contents has to be reintroduced through stereotypes.

Problems with that scheme appear at two critical junctures:

  • Between languages and meta-models, and the way semantics are introduced.
  • Between environments and systems, and the way abstractions are defined.

Caminao’s modeling paradigm is used to illustrate the alternative strategy, namely the direct stereotyping of systems architectures semantics.

Languages vs Stereotypes

Meta-Models are models of models: just like artifacts of the latter represent sets of instances from targeted domains, artifacts of the former represent sets of symbolic artifacts from the latter. So while set higher on the abstraction scale, meta-models still reflect the domain of concerns.

Meta-models takes a higher view of domains, meta-languages don’t.

Things are more complex for languages because linguistic constructs ( syntax and semantics) and pragmatic are meant to be defined independently of domain of discourse. Taking a simple example from the model above, it contains two kinds of relationships:

  • Linguistic constructs:  represents, between actual items and their symbolic counterparts; and inherits, between symbolic descriptions.
  • Domain specific: played by, operates, and supervises.

While meta-models can take into account both categories, that’s not the case for languages which only consider linguistic constructs and mechanisms. Stereotypes often appear as a painless way to span the semantic fault between what meta-models have to do and what languages use to do; but that is misguided because mixing domain specific semantics with language constructs can only breed confusion.

Stereotypes & Semantics

If profiles and stereotypes are meant to refine semantics along domains specifics, trying to conciliate UML/SysML languages and non UML/SysML models puts UAFP in a lopsided position by looking the other way, i.e towards one-fits-all meta-language instead of systems architecture semantics. Its way out of this conundrum is to combine stereotypes with UML constraint, as can be illustrated with PropertySet:

UAFP for PropertySet (italics are for abstract)

Behind the mixing of meta-modeling levels (class, classifier, meta-class, stereotype, meta-constraint) and the jumble of joint modeling concerns (property, measurement, condition), the PropertySet description suggests the overlapping of two different kinds of semantics, one looking at objects and behaviors identified in environments (e.g asset, capability, resource); the other focused on systems components (property, condition, measurement). But using stereotypes indifferently for both kind of semantics has consequences.

Stereotypes, while being the basic UML extension mechanism, comes without much formalism and can be applied extensively. As a corollary, their semantics must be clearly defined in line with the context of their use, in particular for meta-languages topping different contexts.

PropertySet for example is defined as an abstract element equivalent to a data type, simple or structured, a straightforward semantic that can be applied consistently for contexts, domains or languages.

That’s not the case for ActualPropertySet which is defined as an InstanceSpecification for a “set or collection of actual properties”. But properties defined for domains (as opposed to languages) have no instances of their own and can only occur as concrete states of objects, behaviors, or expectations, or as abstract ranges in conditions or constraints. And semantics ambiguities are compounded when inheritance is indifferently applied between a motley of stereotypes.

Properties epitomize the problems brought about by confusing language and domain stereotypes and point to a solution.

To begin with syntax, stereotypes are redundant because properties can be described with well-known language constructs.

As for semantics, stereotyped properties should meet clearly defined purposes; as far as systems architectures are concerned, that would be the mapping to architecture capabilities:

Property must be stereotyped with regard to induced architecture capabilities.
  • Properties that can be directly and immediately processed, symbolic (literal) or not (binary objects).
  • Properties whose processing depends on external resource, symbolic (reference) or not (numeric values).

Such stereotypes could be safely used at language level due to the homogeneity of property semantics. That’s not the case for objects and behaviors.

Languages Abstractions & Symbolic Representations

The confusion between language and domain semantics mirrors the one between enterprise and systems, as can be illustrated by UAFP’s understanding of abstraction.

In the context of programming languages, isAbstract applies to descriptions that are not meant to be instantiated: for UAFP “PhysicalResource” isAbstract because it cannot occur except as “NaturalResource” or “ResourceArtifact”, none of them isAbstract.

“isAbstract” has no bearing on horses and carts, only on the meaning of the class PhysicalResource.

Despite the appearances, it must be reminded that such semantics have nothing to do with the nature of resources, only with what can be said about it. In any case the distinction is irrelevant as long as the only semantics considered are confined to specification languages, which is the purpose of the UAFP.

As that’s not true for enterprise architects, confusion is to arise when the modeling Paradigm is extended as to include environments and their association with systems. Then, not only that two kinds of instances (and therefore abstractions) are to be described, but that the relationship between external and internal instances is to determine systems architectures capabilities. Extending the simple example above:

  • Overlooking the distinction between active and passive physical resources prevents a clear and reliable mapping to architecture technical capabilities.
  • Organizational resource lumps together collective (organization), individual and physical (person), individual and organizational (role), symbolic (responsibility), resources. But these distinctions have a direct consequences for architecture functional capabilities.
Abstraction & Symbolic representation

Hence the importance of the distinction between domain and language semantics, the former for the capabilities of the systems under consideration, the latter for the capabilities of the specification languages.

Systems Never Walk Alone

Profiles are supposed to be handy, reliable, and effective guides for the management of specific domains, in that case the modeling of enterprise architectures. As it happens, the UAF profile seems to set out the other way, forsaking architects’ concerns for tools providers’ ones; that can be seen as a lose-lose venture because:

  • There isn’t much for enterprise architects along that path.
  • Tools interoperability would be better served by a parser focused on languages semantics independently of domain specifics.

Hopefully, new thinking about architecture frameworks (e.g DoDAF) tends to restyle them as EA profiles, which may help to reinstate basic requirements:

  • Explicit modeling of environment, enterprise, and systems.
  • Clear distinction between domain (enterprise and systems architecture) and languages.
  • Unambiguous stereotypes with clear purposes
A simple profile for enterprise architecture

On a broader perspective understanding meta-models and profiles as ontologies would help with the alignment of purposes (enterprise architects vs tools providers), scope (enterprise vs systems), and languages (modeling vs programming).

Back to Classics: Ontologies

As introduced long ago by philosophers, ontologies are meant to make sense of universes of discourse. To be used as meta-models and profiles ontologies must remain neutral and support representation and contents semantics independently of domains of concern or perspective.

With regard to neutrality, the nature of semantics should tally the type of nodes (top):

  • Nodes would represent elements specific to domains (bottom right).
  • Connection nodes would be used for semantically neutral (aka syntactic) associations to be applied uniformly across domains (bottom left).

That can be illustrated with the simple example of cars:

KM_CaseRaw
RDF graphs (top) support formal (bottom left) and domain specific (bottom right) semantics.

With regard to contexts, ontologies should be defined according to the nature of governance and stability:

  • Institutional: Regulatory authority, steady, changes subject to established procedures.
  • Professional: Agreed upon between parties, steady, changes subject to accords.
  • Corporate: Defined by enterprises, changes subject to internal decision-making.
  • Social: Defined by usages, volatile, continuous and informal changes.
  • Personal: Customary, defined by named individuals (e.g research paper).

Ontologies set along that taxonomy could also be refined as to be aligned with enterprise architecture layers: enterprise, systems, platforms, e.g:

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

With regard to concerns ontologies should  focus on the epistemic nature of targeted items: terms, documents, symbolic representations, or actual objects and phenomena. That would outline four basic concerns that may or may not be combined:

  • Thesaurus: ontologies covering terms and concepts.
  • Document Management: ontologies covering documents with regard to topics.
  • Organization and Business: ontologies pertaining to enterprise organization, objects and activities.
  • Engineering: ontologies pertaining to the symbolic representation of products and services.
KM_OntosCapabs
Ontologies: Purposes & Targets

More generally, understanding meta-models and profiles as functional ontologies is to bring all EA business and engineering concerns within a comprehensive and consistent conceptual framework.

A workbench built with the Caminao ontological kernel is meant to explore the scope and benefits of that approach, with a beta version (Protégé/OWL 2) available for comments on the Stanford/Protégé portal using the link: Caminao Ontological Kernel (CaKe_).

Further Reading

Models
Architectures
Enterprise Architecture
UML

External Links

Squaring EA Governance

Preamble

Enterprise governance has to face combined changes in the way business times and spaces are to be taken into account. On one hand social networks put well-thought-out market segments and well planned campaigns at the mercy of consumers’ weekly whims. On the other hand traditional fences between environments and IT systems are crumbling under combined markets and technological waves.

Squaring Governance in Space and Time (Jasenka Tucan-Vaillant)

So, despite (or because of) the exponential ability of intelligent systems to learn from circumstances, enterprise governance is not to cope with such dynamic complexities without a reliable compass set with regard to key primary factors: time-frames of concerns; control of processes; administration of artifacts.

Concerns & Time-frames

Confronted to massive and continuous waves of stochastic data flows, the priority is to position external events and decision-making with regard to business and assets time-frames:

  • Business value is to be driven by market opportunities which cannot be coerced into predefined fixed time-frames.
  • Assets management is governed by continuity and consistency constraints on enterprise identity, objectives, and investments along time.
Governance Square and its four corners

Enterprises, once understood as standalone entities, must now be redefined as living organisms in continuous adaptation with their environment. Governance schemes must therefore be broadened to business environments and layered as to take into account the duality of time-frames: operational for business value, strategic for assets.

Control of processes and administration of artifacts can then be defined accordingly.

Time & Control: Processes

Architectures being by nature shared and persistent, their layers are meant to reflect different time-frames, from operational cycles to long-term assets:

  • At enterprise level the role of architectures is to integrate shared assets and align various objectives set along different time-frames. At this level it’s safe to assume some cross dependencies between processes, which would call for phased governance.
  • By contrast, business units are meant to be defined as self-governing entities pursuing specific objectives within their own time-frame. From a competitive perspective markets opportunities and competitors moves are best assumed unpredictable, and processes best governed by circumstances.
Enterprise Processes have to align business and engineering objectives

Processes can then be defined vertically (business or Systems) as well as horizontally (enterprise architecture or application development), and governance set accordingly:

  • At enterprise level processes are phased: stakeholders and architects plan and manage the development and deployment of assets (organization and systems).
  • At business units level processes are lean and just-in-time: business analysts and software engineers design and develop applications supporting users needs as defined by users stories or use cases.

Models are then to be introduced to describe shared assets (organization and systems) across the enterprise. They may also support business analysis and software engineering.

Spaces & Administration: Models and Artifacts

Whatever the targets and terminologies, architecture is best defined as a relationship between concrete territories (processes and systems) and abstract maps (blueprints or models).

Carrying on with the four corners of governance square:

  • Business analysts are to set users’ narratives (concrete) in line with the business plots (blueprints) set by stakeholders.
  • Software engineers designing applications (concrete) in line with systems functional architectures (blueprints).
Enterprise Architecture uses maps to manage territories

As for the overlapping of business and development time-frames, the direct mapping between concrete business and system corners (e.g though agile development) is to facilitate the governance of integrated actual and numeric flows across business and systems.

Conclusion: A Compass for Enterprise Architects

Behind turfs perimeters and jobs descriptions, roles and responsibilities involved in enterprise architecture can be summarized by four drives:

  • Business stakeholders (top left): adjust organization as to maximize the versatility and plasticity of architectures.
  • Business analysts (bottom left): define business processes with regard to broader objectives and engineering efficiency.
  • Software engineers (bottom right): maximize the value for users and the quality of applications.
  • Systems architects (top right): dynamically align systems with regard to business models and engineering processes.
Orientation should come before job descriptions

Whereas roles and responsibilities will generally differ depending on enterprise environment, business, and culture, such a compass would ensure that the governance of enterprise architectures hinges on reliable pillars and is driven by clear principles.

Further Reading