Archive for the ‘Business Process Modeling’ Category

Focus: Business Cases for Use Cases

February 27, 2017


As originally defined by Ivar Jacobson, uses cases (UCs) are focused on the interactions between users and systems. The question is how to associate UC requirements, by nature local, concrete, and changing, with broader business objectives set along different time-frames.


Cases, Kites, and Clouds (Sigmar Polke)

Backing Use Cases

On the system side UCs can be neatly traced through the other UML diagrams for classes, activities, sequence, and states. The task is more challenging on the business side due to the diversity of concerns to be defined with other languages like Business Process Modeling Notation (BPMN).

Use cases at the hub of UML diagrams

Use Cases contexts

Broadly speaking, tracing use cases to their business environments have been undertaken with two approaches:

  • Differentiated use cases, as epitomized by Alister Cockburn’s seminal book (Readings).
  • Business use cases, to be introduced beside standard (often renamed as “system”) use cases.

As it appears, whereas Cockburn stays with UCs as defined by Jacobson but refines them to deal specifically with generalization, scaling, and extension, the second approach introduces a somewhat ill-defined concept without setting apart the different concerns.

Differentiated Use Cases

Being neatly defined by purposes (aka goals), Cockburn’s levels provide a good starting point:

  • Users: sea level (blue).
  • Summary: sky, cloud and kite (white).
  • Functions: underwater, fish and clam (indigo).

As such they can be associated with specific concerns:

Cockburn’s differentiated use cases

  • Blue level UCs are concrete; that’s where interactions are identified with regard to actual agents, place, and time.
  • White level UCs are abstract and cannot be instanciated; cloud ones are shared across business processes, kite ones are specific.
  • Indigo level UCs are concrete but not necessarily the primary source of instanciation; fish ones may or may not be associated with business functions supported by systems (grey), e.g services , clam ones are supposed to be directly implemented by system operations.

As illustrated by the example below, use cases set at enterprise or business unit level can also be concrete:

Example with actors for users and legacy systems (bold arrows for primary interactions)

UC abstraction connectors can then be used to define higher business objectives.

Business “Use” Cases

Compared to Cockburn’s efficient (no new concept) and clear (qualitative distinctions) scheme, the business use case alternative adds to the complexity with a fuzzy new concept based on quantitative distinctions like abstraction levels (lower for use cases, higher for business use cases) or granularity (respectively fine- and coarse-grained).

At first sight, using scales instead of concepts may allow a seamless modeling with the same notations and tools; but arguing for unified modeling goes against the introduction of a new concept. More critically, that seamless approach seems to overlook the semantic gap between business and system modeling languages. Instead of three-lane blacktops set along differentiated use cases, the alignment of business and system concerns is meant to be achieved through a medley of stereotypes, templates, and profiles supporting the transformation of BPMN models into UML ones.

But as far as business use cases are concerned, transformation schemes would come with serious drawbacks because the objective would not be to generate use cases from their business parent but to dynamically maintain and align business and users concerns. That brings back the question of the purpose of business use cases:

  • Are BUCs targeting business logic ? that would be redundant because mapping business rules with applications can already be achieved through UML or BPMN diagrams.
  • Are BUCs targeting business objectives ? but without a conceptual definition of “high levels” BUCs are to remain nondescript practices. As for the “lower levels” of business objectives, users’ stories already offer a better defined and accepted solution.

If that makes the concept of BUC irrelevant as well as confusing, the underlying issue of anchoring UCs to broader business objectives still remains.

Conclusion: Business Case for Use Cases

With the purposes clearly identified, the debate about BUC appears as a diversion: the key issue is to set apart stable long-term business objectives from short-term opportunistic users’ stories or use cases. So, instead of blurring the semantics of interactions by adding a business qualifier to the concept of use case, “business cases” would be better documented with the standard UC constructs for abstraction. Taking Cockburn’s example:

Abstract use cases: no actor (19), no trigger (20), no execution (21)

Different levels of abstraction can be combined, e.g:

  • Business rules at enterprise level: “Handle Claim” (19) is focused on claims independently of actual use cases.
  • Interactions at process level: “Handle Claim” (21) is focused on interactions with Customer independently of claims’ details.

Broader enterprise and business considerations can then be documented depending on scope.

Further Reading

External Links

iStar and the Requirements Conundrum

December 12, 2016


Whenever software engineering problems are looked at, the blame is generally put on requirements, with each side of the business/system divide holding the other responsible.


iStar modeling put the focus on communication (N. Rockwell)

The iStar approach tries to tackle the problem with a conceptual language focused on interactions between business processes and supporting systems.


Conceptual approaches to requirements try to breach the dilemma between phased and agile development schemes: the former takes for granted that requirements can be fully and definitively set upfront; the latter takes a more pragmatic path and tries to reconcile business and system analysts through direct and continuous collaboration.

Setting apart frictions between specific methods, the benefits of agile principles and practices are now well-recognized, contingent on the limits of agile scope. Summarily, agile development is at its best when requirements capture and analysis can be weaved with development and tests. The question remains of what happens when requirements are to be dealt with separately.

The iStar’s answer shares with agile a focus on collaboration and doesn’t take side for business (e.g users’ stories) or systems (e.g use cases). Instead, iStar modeling language is meant to support a conceptual description of interactions between business processes and supporting systems in terms of actors’ goals and commitments, and the associated dependencies.

Actors & Goals

The defining aspect of the iStar modeling approach is to replace one-sided perspectives (business or system) by a systemic one focused on the interactions between agents. The interactive part of a requirement will therefore comprise three basic items:

  • A primary actor trigger an interaction in order to meet some goal; e.g a car owner want his car repaired.
  • Secondary actors may be involved during the ensuing exchanges: e.g body shop, appraiser, insurance company.
  • Functions to be performed: actual task; e.g appraise damages; qualification (soft goal), e.g fair appraisal; and resources, e.g premium payment.
Actors & dependencies

Actors & Dependencies

Dependencies Semantics

The factual description of interactions is both detailed and enriched by elements set within a broader scope:

  • Goal (strong) dependency: assertions about actual state of affairs: object, activity, or expectations.
  • Soft-goal dependency: assertions about expected outcomes.
  • Task dependency: organizational, functional, or technical constraints pertaining to the execution of activities.
  • Resource dependency: constraints or conditions on the availability of inputs, actual or symbolic.

It would be tempting to generalize the strong/soft distinction to dependencies as to make use of modal logic, strong dependencies associated with deontic rules, soft dependencies with alethic ones. That would .

iStar & Caminao

Since iStar modeling categories are directly aligned with UML Use Cases, they can easily mapped to core Caminao stereotypes for actors, objects, events, and activities.

Actors & dependencies

iStar with Caminao Stereotypes

Interestingly, the iStar strong/soft distinction could translate to the actual/symbolic one which constitute the conceptual backbone of the Caminao paradigm.


From the business perspective, iStar must be credited with two critical tenets:

  • The focus on interactions between agents is essential for business and system analysts to collaborate. Such benefits appear clearly for the definition of primary and secondary roles (aka actors), intents (business) and capabilities (supporting environments).
  • The distinction between strong and soft goals, even if the logical basis remains unexploited.

Yet, the system perspective lacks a functional dimension, e.g:

  • Architecture levels (enterprise and organization, systems and functionalities, platforms and technologies) are not taken into consideration, nor the nature of capabilities, e.g strategic and operational.
  • The strong/soft dependencies distinction is not explicitly associated with systems capabilities.

On the whole these pros and cons reflect iStar’s declared intent on conceptual modeling; as a corollary these flaws mark also the limits of conceptual modeling when it is detached from the symbolic description of supporting systems functionalities.

Nonetheless, as illustrated by the research quoted below, iStar remains a sound basis for the specification of interactions between users and systems, either as use cases or users’ stories.

Further Reading

External Links

Business Agility & the OODA Loop

November 21, 2016


The OOAD (Observation, Orientation, Decision, Action) loop is a real-time decision-making paradigm developed in the sixties by Colonel John Boyd from his experience as fighter pilot and military strategist.

(Moholy Nagy)

How to get inside opponent’s loop (Lazlo Moholy-Nagy)

The relevancy of OODA for today’s operational decision-making comes from the seamless integration of IT systems with business operations and the resulting merits of agile development processes.

Business: End of Discrete Time-Frames

Business governance was used to be phased: analyze the market, select opportunities, build capabilities, launch operations. No more. With the melting of the fences between actual and symbolic realms, periodic transitional events have lost most of their relevancy. Deprived of discrete and robust time-frames, the weaving of observed facts with business plans has to be managed on the fly. Success now comes from continuous readiness, quicker tempo, and the ability to operate inside adversaries’ time-scales, for defense (force competitor out of favorable position) as well as offense (get a competitive edge). Hence the reference to dogfights.

Dogfights & Agile Primacy

John Boyd train of thoughts started with the observation that, despite the apparent superiority of the soviet Mig 15 on US F-86 during the Korea war, US fighters stood their ground. From that factual observation it took Boyd’s comprehensive engineering work to demonstrate that as far as dogfights were concerned fast transients between maneuvers (aka agility) was more important than technical capabilities. Pushed up Pentagon’s reluctant ladders by Boyd’s sturdy determination, that conclusion have had wide-ranging consequences in the design of USAF fighters and pilots formation for the following generations. Its influence also spread to management, even if theories’ turnover is much faster there, and shelf-life much shorter.

Nowadays, with the accelerated integration of business processes with IT systems, agility is making a comeback from the software engineering corner. Reflecting business and IT convergence, principles like iterative development, just-in-time delivery, and lean processes, all epitomized by the agile software development model, are progressively mingling into business practices with strong resemblances to dogfights; and the resemblances are not only symbolic.

IT Systems & Business Competition

While some similarities between dogfights and business competition may seem metaphorical, one critical aspect is all too real, namely the increasing importance of supporting machines, IT systems or fighter jets.

Basically, IT systems, like fighters’ electronics, are tasked to observe environments, analyse changes in relation to position and objectives, and support decision-making. But today’s systems go further with two qualitative leaps:

  • The seamless integration of physical and symbolic flows let systems manage some overlapping between supporting decisions and carrying out actions.
  • Due to their artificial intelligence capabilities, systems can learn on-the-job and improve their performances in real-time feedback loops.

When combined, these two trends have drastic impact on the way machines can support human activities in real-time competitive situations. More to the point, they bring new light on business agility.

Business Agility

As illustrated by the radical transformation of fighter cockpits, the merging of analog and digital flows leaves little room for human mediation: data must be processed into information and presented instantly along two critical dimensions, one for decision-making, the other for information life-cycle:

  • Man/Machine interfaces have to materialize the merging of actual and symbolic realms as to support just-in-time decision-making.
  • The replacement of phased selected updates of environment data by continuous changes in raw and massive data means that the status of information has to be incorporated with the information itself, yet without impairing decision-making.

Beyond obvious differences between dogfights and business competition, that double exigence is to characterize business agility:

  1. Instant understanding of changes in business opportunities (Observation) .
  2. Simultaneous assessment of the reliability and shelf-life of pertaining information with regard to current positions and operations (Orientation).
  3. Weighting of options with regard to enterprise capabilities and broader objectives (Decision).
  4. Carrying out of decisions within the relevant time-span (Action).

That understanding of business agility is to be compared with its development and architecture cousins. Yet it doesn’t seem to add much to data analytics and operational decision-making. That is until the concept of orientation is reassessed.

Agility & Orientation: Task vs Tack

To begin with basics, the concept of Orientation comes with a twofold meaning, actual and symbolic:

  • Actual: a position with regard to external (e.g spacial) coordinates, possibly qualified with abilities to observe, move, or act.
  • Symbolic: a position with regard to internal (e.g beliefs or aims) references, possibly mixed with known or presumed orientation of other agents, opponents or associates.

When business is considered, data analytics is supposed to deal comprehensively and accurately with markets’ actual orientations. But the symbolic facet is left largely unexplored.

Boyd’s contribution is to bring together both aspects and combine them into actual practice, namely how to foretell the tack of your opponents from their actual tracks as well as their surmised plans, while fooling them about your own moves, actual or planned.

Such ambitions once out of reach, can now be fulfilled due to the combination of big data, artificial intelligence, and the exponential growth on computing power.

Further Readings


Business Problems shouldn’t sleep with IT Solutions

October 8, 2016


The often mentioned distinction between problem and solution levels may make sense from an analyst’s particular point of view, whether business or system.  But blending problems and solutions independently of their nature becomes a serious over simplification for enterprise architects considering that one of their prime responsibility is to keep apart business problems from IT solutions.

(Mircea Cantor)

Functional problem with technical solution (Mircea Cantor)

That issue is relevant from engineering as well as business perspective.

Engineering View: Problem Levels & Architecture Layers

As long as computers are used to solve problems the only concern is to find the best solution, and the only architecture of concern is software’s.

But enterprise architects have to deal with systems, not computers, namely how to best serve business objectives with corporate resources, across business units and along business cycles. For that purpose resources (financial, human, technical) and their use are to be layered according to the nature of problems and solutions: business processes (enterprise), supporting functionalities (systems), and technologies (platforms).

From an engineering perspective, the intended congruence between problems levels and architecture layers can be illustrated with the OMG’s model driven architecture (MDA) framework:

  • Computation independent models (CIMs) deal with business processes solutions, to be translated into functional problems for supporting systems.
  • Platform independent models (PIMs) deal with functional solutions, to be translated into technical problems for supporting platforms.
  • Platform specific models (PSMs) deal with technical solutions, to be implemented as code.
MDA layers correspond to a clear hierarchy of problems and solutions

MDA layers can be mapped to a clear hierarchy of problems and solutions

Along that understanding, architectures can be seen as solutions, and the primary responsibility of enterprise architects is to see that problems/solutions brace remain in their respective swim-lanes.

Business View: Business Value & Enterprise Assets

Whereas the engineering perspective may appear technical or specific to a model based approach, the same issue is all the more significant when expressed with regard to business concerns and corporate governance. In that case the critical distinction is between business value and assets:

  • Business value: Problems are set by business opportunities, and solutions by processes and applications. The critical factor is reactivity and time-to-market.
  • Assets: Problems are set by business objectives and strategy, and solutions are to be supported by organization and systems capabilities. The critical factor is reuse and ROI.
Decision-making must distinguish between business opportunities and enterprise governance

Decision-making must distinguish between business opportunities and enterprise governance

If opportunities are to be seized and operations managed on the fly  yet tally with strategic decisions, respective problems and solutions should be kept apart. Juggling with their dynamic alignment is at the core of enterprise architects’ job description.

Enterprise Architects & Governance

Engineering and business perspectives are not to be seen as the terms of an alternative to be picked by enterprise architects. As a matter of fact they must be crossed and governance policies selected depending on the point of view:

  • Looking at EA from an engineering perspective,  the business one will focus on systems governance and assets management as epitomized by model based systems engineering schemes.
  • Looking at EA from a business perspective, the engineering one will focus on lean and just-in-time solutions, as epitomized by agile development models.

As far as governance of large and complex corporate entities, supposedly EA’s primary target, must deal with tactical, operational, and strategic concerns, the nexus between business and engineering perspectives is where enterprise architects are to stand.



Business Stories: Stakeholders’ Plots & Users’ Narratives

July 4, 2016


As Aristotle noted some time ago, plots are the backbone of any story as they uphold the causal sequence of events and actions: they provide the “why” of what happens, compared to narratives, which tell “how” what happened is being told.


Only shadows will tell: as far as stories are concerned, possibilities remain unknown until their realization.

So, in principle, plots deal with possibilities and narratives with realizations. But in fact plots remain unknown until being narrated; in other words fictions are like Schrödinger’s cat: there is no way to set possibilities and realizations apart.

That literary conundrum may convey some useful clues for business analysis, with stakeholders objectives seen as plots, and users’ stories as narratives.

Stakeholders’ Plots vs Users’ Narratives

With regard to the functionalities of supporting systems, a key issue for business analysts is to accommodate specific and short-lived opportunities identified by business units with broader and long-standing objectives defined at corporate level.

Assuming a fictional view of business expectations, that issue can be charted in terms of plots and narratives:

  • Business objectives (as plots) are meant to apply continuously and consistently to different agents, different concerns, and different contexts. As such they are best defined as rules and constraints (declarative schemes).
  • Users’ stories (as narratives) are supposed to translate as soon as possible into business transactions. As such they are best defined as sequences of operations governed by users’ choices (procedural schemes).

Then, just like narratives are meant to carry out the plots, users’ stories are supposed to follow the paths set by business objectives. But if confusion is to be avoided between strategic orientations, regulatory directives, and opportunist moves, the walk of business objectives and the talk of users’ stories should be termed differently.

Business Objectives (Plots): Symbolic & Allochronic

The definition of business objectives has to find its terms between the Charybdis of abstractions and the Scylla of specific business processes, the former to be avoided because they are by nature detached from reality and only make sense with regard to models, the latter because they would be too specific and restrictive. In-between, business objectives would be best defined through:

  • Strategic and financial objectives expressed using symbolic categories applied to environments, products, and resources.
  • Modal time-frames identified in reference to events and qualified by assumptions with regard to symbolic categories.
  • Business functions to be optimized given a set of constraints.

These could be comprehensively and consistently expressed with declarative languages.

Users’ Stories (Narratives): Actual & Contemporaneous

Users’ stories are at their best when tied to specific circumstances and purposes without being led away by modeling concerns. As narratives they should stick to agents, triggering events, and scripted sequences of options, operations, and outcomes:

  • Compared to the symbolic categories used for business objectives, users stories should refer to actual subsets of objects and events defined on contexts.
  • Contrary to the modal time-frames of business objectives, the scripts of users’ stories must be fully timed with regard to their triggering events.

That can only be expressed as procedures.

From Fiction to Artifacts: Aligning Business Objectives & Enterprise Architectures

Likening business analysis to its distant literary kin goes beyond the metaphor as it points to a practical organization of business objectives and users’ stories.

And the benefits of the distinction between declarative (for business plots) and procedural (for users’ narratives) blueprints is not limited to business analysis but can be extended to systems architecture (as plots) and software design (as narratives). On that basis declarative schemes could be applied to business functions and architectures capabilities, and procedural ones to users’ stories (or use cases) and software design.


On a broader perspective such a fictional approach may help to align enterprise architectures to business objectives.

Further Reading

External Links

UML’s Semantic Master Key, Lost & Found

April 25, 2016


When its first version was published twenty years ago the prognosis for OMG’s UML (Unified Modeling Language) was of rapid and wide expansion. It didn’t happen, and notwithstanding a noteworthy usage, UML has not become  “the” unified modeling language. Beyond the diverse agendas of methods and tools providers, this falling short may have something to do with a lack of robust semantics, as illustrated by UML 2.5’s halfhearted attempt to define individuals.

(Jonathan Monk)

Actual & Digital Identities (Jonathan Monk)

UML 2.5 Aborted Attempt with Individual 

UML 2.5 has often been presented as an attempt to redress the wayward and increasingly convoluted paths taken by the previous versions. Yet, beside some useful (and long needed) clarifications and adjustments, its governing group failed to agree on some compact and unambiguous semantics and had to content itself with perfunctory guidelines introduced as an afterthought.

As a matter of fact the OMG committee may have tried to get its semantics in order, as suggested by the chapter “On semantics” making the point right away with a distinction between the things to be described and the categories to be applied:

“A UML model consists of three major categories of model elements [classifiers, events, and behaviors], each of which may be used to make statements about different kinds of individual things within the system being modeled (termed simply “individuals”in the following)”.

That straightforward understanding (UML is meant to describe individual objects, events, or behaviors) could have provided the semantic cornerstone of a sound approach. Surprisingly, and for obscure reasons, it is soon smudged by syntactical overlapping and semantic ambiguity, the term “individual” being used indifferently as adjective and noun, and then appears to be restricted to classifiers only. That leaves UML with a dearth of clear semantics regarding its scope.

Individual as a Semantic Master Key

The early dismiss of individual as a constitutive concept is unfortunate because, taking a leaf from Archimedes, it could have been the fulcrum on which to place the UML lever.

To begin with, no modeling language, especially one supposed to be unified, can do without some convincing semantics about what it is supposed to describe. Furthermore, such a requirement is of primary importance for UML whose scope straddles the divide between business and systems realms, and must therefore rigorously define what they share and how they differ.

And that could have been neatly achieved with a comprehensive and unified interpretation of individuals, combined with a clear taxonomy of the aspects to be modeled:

  • Individuals are whatever occurrences (in business or systems contexts) with identities of their own.
  • These individuals (objects, events, or behaviors) can be specified with regard to their structure and relationships.

The logical primacy of this approach is reinforced by its immediate, practical, and conclusive benefits for business processes modeling on one side, model based engineering processes on the other side.

A Key to Business Processes Modeling

As far as business processes are concerned, modeling the part played by supporting systems turns around few critical issues, and these issues can be dealt more clearly and consistently when set in reference to individuals taxonomy (objects, behaviors, events) e.g:

  • Functional or non functional requirements ? The former can be associated with individuals, the latter cannot.
  • Architecture or application ? The former affect the specification of interactions between individuals, the latter affect only their local features.
  • Synchronous or asynchronous ? Specifications can only be made with regard to life-cycles and time-frames: system (objects), process (behaviors), or instant (event).
  • Structures or Relationships ? The former are bound to individuals’ identity, the latter are used between different individuals.
  • Inheritance or Delegation ? The former is used for the specification of structural or functional features, the latter for individuals’ behaviors.

More generally that understanding of individuals should greatly enhance the alignment of systems functional architectures with business processes.

A Key to Model Based Systems Engineering

As should be expected from a lack of semantic foundations, one of the main characteristics of the UML community is its fragmented practices, regrouped around diagrams (e.g Use case or Class) or task (e.g requirements analysis or code generation).

The challenge can be directly observed for model based system engineering and software development: with the exception of Statecharts for RT modeling, Class diagrams are the only ones used all along engineering processes; the others, when used, are reduced to documentation purposes. That bottleneck in development flows can be seen as the direct consequence of UML restricted semantics: since behaviors are not identified as individuals in their own right, their description cannot be directly translated into software artifacts, but have to be understood as part of active objects descriptions before being translated into class diagrams. Hence the apparent redundancy of corresponding diagrams.

As a corollary, reinstating a unified semantics of individual for both classifiers and behaviors could be the key to a seamless integration of the main UML diagrams. Most important, that would bear out the cross benefits of combining UML and MBSE.

Further Readings

Event Oriented Analysis & Object Oriented Design

April 15, 2016

As it’s safe to assume that a primary objective of process analysis is to align business concerns (by nature specific and changing) with enterprise architectures (meant to be shared and stable), events could provide a good starting point.

(F. Handoko)

Event, concerns, processing (F. Handoko)

Business Analysis & Application Design

Taking example from the convincing track record of object oriented approaches for systems architectures and software design, the same principles have been tried for business requirements analysis. While that approach can be credited with significant realizations, success usually depends on some prior alignment of business domains with their system counterpart, in particular on the possibility to uniformly and consistently identify and define business entities as objects independently of operating processes.

Alternatively, when business entities cannot not be readily identified upfront as system objects, analysis may start with organization, entitled agents, activities, and be carried out with the definition of business flows and associated entities.

So, and whatever the approach, the question is how to ensure that the applications under consideration are designed in accordance with architecture capabilities.

System Architecture & Software Design

Words are worth the difference they make: as long as systems were not much more than an assortment of software modules, architecture and design could be understood as one and the same. But nowadays a distinction may be overdue between, on one hand the design of software components run within a single system’s address space and time-frame and, on the other hand, architectures of systems set across different spaces and time-frames.


Architecture vs Design: words are worth the difference they make

Object oriented solutions (e.g Domain Driven Design) are arguably the option of choice for the former, but services oriented approaches may be a better fit for the latter. Not by chance, events provide a sound conceptual hinge between the two approaches.

Event Oriented Analysis vs Object Oriented Design

Object oriented principles can be streamlined around three core topics: (a) information hiding and coupling between structures and methods; (b) inheritance between types; (c) communication through interfaces and polymorphism.

OO principles can be streamlined along three topics: encapsulation (a), inheritance (b), and communication through interfaces (c).

OO principles can be streamlined around three topics: encapsulation (a), inheritance (b), and communication through interfaces (c).

Of these, encapsulation and inheritance are specific to software design, but communication mechanisms are also at the core of services oriented architectures. Considering messages as the logical system counterparts of business events, event-oriented analysis should help to align business processes with systems capabilities.

From a business processes perspective, events are signaling changes in the states of activities, objects, or expectations. Given that  supporting systems are meant to deal with those changes, the analysis of business requirements could proceed from corresponding events:

  • Business events are defined with regard to time-frames (a) and sources to be authenticated and authorized (b).
  • Triggering changes must be described by messages with regard to their functional (c) and operational (d) scope.
  • Business logic (e) and entities (f) are often shared across applications and therefore better defined independently.
  • Internal changes (same space and time-frame) are hidden.
  • Triggered (external) changes are defined with regard to time-frames (h), processes (d), and devices (g).
A simplified blueprint of Event Oriented Process Analysis

A simplified blueprint of Event Oriented Process Analysis

As it happens, those facets can be aligned with OO design ones, with (c) and (d) for communication, (e) and (f) for encapsulation. On a broader perspective they also fit with the growing focus on event-driven applications and service oriented architectures.

From Event Oriented Process Analysis to Service Oriented Architectures

By moving business logic to the background, event-driven analysis fosters polymorphism at enterprise level with corresponding benefits:

  • With regard to business processes, events come with functional and operational requirements set independently of the business logic that will be carried out: trigger (what has changed), role (who is requesting), and message communication semantics (when the system is supposed to deal with the event).
  • With regard to system capabilities messages can be used to align business (aka external) events with system (aka internal) ones independently of the business entities and logic (what is to be done and how).
  • With regard to architecture and design, that approach is to uphold OO principles by dealing separately with polymorphic requests (interfaces) and business logic (methods).

Those benefits appear clearly when capabilities are realized by services defined with regard to business processes (customers), business objects (messages), business logic (contract), and business operations (policy).

Environment (bold) vs Services (italic)

Environment (bold) vs Services (italic)

It must be reminded that services are part of functional architectures and as a consequence cannot be directly addressed by users or devices.

Events & Action Semantics

With events set as modeling anchors, use cases may provide the modeling glue between processes and functional capabilities:

  • Triggering events (a) map changes in business environments (aka external events) to changes in systems objects (aka internal events).
  • Actors (b) map roles in organization to system users.
  • Messages (c) map the semantics of business processes to the semantics of applications (e) and domains (f).
Use cases (orange) provide a comprehensive and consistent mapping from processes (green) to services (blue).

Use cases (orange) provide a comprehensive and consistent mapping from processes (green) to services (blue).

On that basis, the main objective of event-oriented analysis would be to distinguish between communication and business semantics, the former dealing with interactions, the latter with business logic.

Further Reading

Conceptual Models & Abstraction Scales

March 22, 2016

Following the recent publication of a new standard for conceptual modeling of automation systems (Object-Process Methodology (ISO/PAS 19450:2015) it may be interesting to explore how it relates to abstraction and meta-models.


Meta-models are drawn along lean abstraction scales (Oskar Schlemmer )

Models & Meta Models

Just like models are meant to describe sets of actual instances, meta-models are meant to do the same for sets of modeling artifacts independently of their targets. Along that reasoning, conceptual modeling of automation systems could be achieved either with a single language covering all aspects, or with a meta-language dealing with different sets of models, e.g MDA’s computation independent, platform independent, and platform specific models.

Modeling Languages covering technical, functional, and business concerns.

Two alternative options for the modeling of automation systems: unified language, or a meta language covering technical (e.g PSMs), functional (e.g PIMs), and business (e.g CIMs) scopes.

Given a model based engineering framework (e.g MDA), meta-models are generally used to support downstream models transformation targeting designs and code. But when upstream conceptual models are concerned, the challenge is to tackle the knowledge-to-systems transition. For that purpose some shared modeling roof is required for the definition of the symbolic footprint of the targeted business in the automation system under consideration.

Symbolic Footprint

Given that automation systems are meant to manage symbolic objects (aka surrogates), one should expect the distinction between actual instances and their symbolic representations to be the cornerstone of corresponding modeling languages. Along that reasoning, modeling of automation systems should start with the symbolic representation of actual business footprints, namely: the sets of objects, events, and processes, the roles played by agents (aka active objects), and the description of the associated states and rules. Containers would be added for the management of collections.

Automation systems modeling begins with the symbolic representation of actual instances

Automation systems modeling begins with the symbolic representation by systems of actual instances of business related objects and phenomena.

Next, as illustrated by the Object/Agent hierarchy, business worlds are not flat but built from sundry structures and facets to be represented by multiple levels of descriptions. That’s where abstractions are to be introduced.

Abstraction & Variants

The purpose of abstractions is to manage variants, and as such they can be used in two ways:

  • For partial descriptions of actual instances depending on targeted features. That can be achieved using composition (for structural variants) and partitions (for functional ones).
  • As hierarchies of symbolic descriptions (aka types and sub-types) subsuming variants identified at instances level.

On that basis the challenge is to find the level of detail (targeted actual instances) and abstraction (symbolic footprint) that will best describe supporting systems functionalities. Such level will have to meet two conditions:

  1. A minimal number of comprehensive and exclusive categories covering the structural variants of the sets of instances to be uniformly, consistently, and continuously identified by both enterprise and supporting systems.
  2. A consistent but adjustable set of types and sub-types anchored to the core structural categories and covering the functional variants .

Climbing up and down abstraction ladders looking for right levels is arguably the critical part of conceptual modeling, but the search will greatly benefit from the distinction between models and meta-models. Assuming meta-models are meant to ignore domain specific features altogether, they introduce a qualitative gap on abstraction scales as the respective hierarchies of models and meta-models are targeting different kind of instances. The modeling of agents and roles epitomizes the benefits of that distinction.

Abstraction & Meta Models

Taking customers for example, a naive approach would use Customer as a modeling type inheriting from a super-type, e.g Party. But then, if parties are to be uniformly identified (#), that would preclude any agent for playing multiple roles, e.g customer and supplier.

A separate description of parties and roles would clearly be a better option as it would unify the identification of the former without introducing unwarranted constraints on the latter which would then be defined and identified as the realization of a relationship played by a party.

Not surprisingly, that distinction would also be congruent with the one between models and meta-model:

  • Meta-models will describe generic aspects independently of domain-specific considerations, in particular organizational context (units and roles) and interactions with systems (a).
  • Models will define StaffSupplier and Customer according to the semantics of the business considered (b).
Composition, partitions and specialization can be used to detail the symbolic footprint

Composition, partitions and specialization can be used along two different abstraction scales.

That distinction between abstraction scales can also be applied to the conceptual modeling of automation systems.

Abstraction Scales & Conceptual Models

To begin with definitions, conceptual representations could be used for all mental constructs, whereas symbolic representations would be used only for the subset earmarked for communication purposes. That would mean that, contrary to conceptual representations that can be detached of business and enterprise practicalities, symbolic representations are necessarily built on design, and should be assessed accordingly. In our case the aim of such representations would be to describe the exchanges between business processes and supporting systems.

That understanding neatly fits the conceptual modeling of automation systems whose purpose would be to consolidate generic and business specific abstraction scales, the former for symbolic representations of the exchanges between business and systems, the latter symbolic representation of business contents.

At this point it must be noted that the scales are not necessarily aligned in continuity (with meta-models’ being higher and models’ being lower) as their respective ontologies may overlap (Organizational Entity and Party) or cross (Function and Role).

Toward a System Modeling Ontology

Along an analytic perspective, ontologies are meant to determine the categories that can comprehensively and consistently denote the instances of a domain under consideration. With regard to the modeling of automation systems, a relevant ontology would map a subset of semantic categories (for conceptual representations) to functional ones (for systems symbolic representations).

Further Reading

External Links

Agile Collaboration & Social Creativity

February 22, 2016

Open-plan offices and social networks are often seen as significant factors of collaboration and innovation, breeding and nurturing the creativity of knowledge workers, weaving their ideas into webs of truths, and molding their minds into some collective intelligence.

Brains need some breathing space

Open-plan offices, collaboration, and knowledge workers creativity

Yet, as creativity comes with agility, knowledge workflows should give brains enough breathing space lest they get more pressure than pasture.

Collaboration & Thinking Flows

Collaboration is a means to an end. To be of any use exchanges have to be fed with renewed ideas and assumptions, triggering arguments and adjustments, and opening new perspectives. If not they may burn themselves out with hollow considerations blurring clues and expectations, clogging the channels, and finally stemming the thinking flows.

Taking example from lean manufacturing, the first objective should be to streamline knowledge workflows as to eliminate swirling pools of squabbles, drain stagnant puddles of stale thoughts, and gear collaboration to flowing knowledge streams. As illustrated by flood irrigation, the first step is to identify basin levels.

Dunbar Numbers & Collaboration Basins

Studying the grooming habits of social primates, psychologist Robin Dunbar came to the conclusion that the size of social circles that individuals of a living species can maintain is set by the size of brain’s neocortex. Further studies have confirmed Dunbar’s findings, with the corresponding sizes for humans set around 10 for trusted personal groups and 150 for untried social ones. As it happens, and not by chance, those numbers seem to coincide with actual observations: the former for personal and direct collaboration, the latter for social and mediated collaboration.

Based on that understanding, the objective would be to organize knowledge workflows across two primary basins:

  • On-site and face-to-face collaboration with trusted co-workers. Corresponding interactions would be driven by personal dispositions and attitudes.
  • On-line and networked collaboration with workers, trusted or otherwise. Corresponding interactions would be based on shared interests and past exchanges.

Knowledge Workflows

The aim of knowledge workflows is to process data into information and put it to use. That is to be achieved by combining different kinds of tasks, in particular:

  • Data and information management: build the symbolic descriptions of contexts, concerns, and means.
  • Objectives management: based on a set of symbolic descriptions, identify and refine opportunities together with the ways to realize them.
  • Tasks management: allocate rights and responsibilities across organizations and collaboration frames, public and shallow or personal and deep.
  • Flows management: monitor and manage actual flows, publish arguments and propositions, consolidate decisions, …

Taking into account constraints and dependencies between the tasks, the aims would be to balance creativity and automation while eliminating superfluous intermediate products (like documents or models) or activities (e.g unfocused meetings).

With regard to dependencies, KM tasks are often intertwined and cannot be carried out sequentially; moreover, as illustrated by the impact of “creative accounting” on accounted activities, their overlapping is not frozen but subject to feedback, changes and adjustments.

With regard to automation, three groups are to be considered: the first requires only raw processing power and can be fully automated; the second also involves some intelligence that may be provided by smart systems; and the third calls for decision-making that can only be done by human agents entitled by the organization.

At first sight some lessons could be drawn from lean manufacturing, yet, since knowledge processes are not subject to hardware constraints, agile approaches should provide a more informative reference.

Iterative Knowledge Processing

A simple preliminary step is to check the applicability of agile principles by replacing “software” by “knowledge”. Assuming that ground is secured, the core undertaking is to consider what would become of cycles and iterations when applied to knowledge processing:

  • Cycle invariants: tasks would be iterated on given sets of symbolic descriptions applied to the state of affairs (contexts, concerns, and means).
  • Iterations content: based on those descriptions data would be processed into information, changes would be monitored, and possibilities explored.
  • Exit condition: cycles would complete with decisions committing changes in the state of affairs that would also entail adjustments or changes in symbolic descriptions.

That scheme meets three of the basic tenets of the agile paradigm, i.e open scope (unknowns cannot be set in advance), continuity of delivery (invariants are defined and managed by knowledge workers), and users in driving seats (through exit conditions). Yet it still doesn’t deal with creativity and the benefits of collaboration for knowledge workers.

Thinking Space & Pace

The scope of creativity in processes is neatly circumscribed by the nature of flows, i.e the possibility to insert knowledge during the processing: external for material flows (e.g in manufacturing), internal for symbolic flows (e.g in software engineering and knowledge processing).

Yet, whereas both software engineering and knowledge processes come with some built-in capability to redefined their symbolic flows on-the-fly, they don’t grant the same room to creativity. Contrary to software engineering projects which have to close their perspectives on the delivery of working products, knowledge processes are meant to keep them open to new understandings and opportunities. For the former creativity is the means to an end, for the latter it’s the end in itself, with collaboration as means.

Such opposite perspectives have direct consequences for two basic agile collaboration mechanisms: backlog and time-boxing:

  • Backlogs are used to structure and manage the space under exploration. But contrary to software processes whose space is focused and structured by users’ needs, knowledge processes are supposed to play on workers’ creativity to expand and redefine the range under consideration.
  • Time-boxes are used to synchronize tasks. But with creativity entering the fray, neither space granularity or thinking pace can be set in advance and coerced into single-sized boxes. In that case individuals must remain in full control of the contents and stride of their thinking streams.

It ensues that when creativity is the primary success factor standard agile collaboration mechanisms are falling short and intelligent collaboration schemes are to be introduced.

Creativity & Collaboration Tiers

The synchronization of creative activities has to deal with conflicting objectives:

  • On one hand the mental maps of knowledge workers and the stream of their thoughts have to be dynamically aligned.
  • On the other hand unsolicited face-to-face interactions or instant communications may significantly impair the course of creative thinking.

When activities, e.g software engineering, can be streamlined towards the delivery of clearly defined outcomes, backlogs and time-boxes can be used to harness workers’ creativity. When that’s not the case more sophisticated collaboration mechanisms are needed.

Assuming that mediated collaboration has a limited impact on thinking creativity (emails don’t have to be answered, or even presented, instantly), the objective is to steer knowledge workflows across a two-tiered collaboration framework: one personal and direct between knowledge workers, the other social and mediated through enterprise or institutional networks.

On the first tier knowledge workers would manage their thinking flows (content and tempo) independently, initiating or accepting personal collaboration (either through physical contact or some kind of instant messaging) depending on their respective “state of mind”.

The second tier would be for social collaboration and would be expected to replace backlogs and time-boxing. Proceeding from the first to the second tier would be conditioned by workers’ needs and expectations, triggered on their own initiative or following prompts.

From Personal to Collective Thinking

The challenging issue is obviously to define and implement the mechanisms governing the exchanges between collaboration tiers, e.g:

  • How to keep tabs on topics and contents to be safeguarded.
  • How to mediate (i.e filter and time) the solicitations and contribution issued by the social tier.
  • How to assess the solicitations and contribution issued by individuals.
  • How to assess and manage knowledge deemed to remain proprietary.
  • How to identify and manage knowledge workers personal and social circles.

Whereas such issues are customary tackled by various AI systems (knowledge management, decision-making, multi-players games, etc), taken as a whole they bring up the question of the relationship between personal and collective thinking, and as a corollary, the role of organization in nurturing corporate innovation.

Further Readings

Relating to Functions

February 10, 2016


Functions map inputs with outputs as expected from a performing agent whatever its nature (concrete or abstract) or means (physical, logical, or magical).
They are not to be confused with objectives (which don’t necessarily specify performing agents or detail inputs) nor with activities (which purport to describe concrete execution paths).


Functions map inputs with outputs as expected from performing agents (Cheyenne view of Medicine)

Those distinctions are critical when the aim is to align business processes requirements with systems architecture capabilities.

Functions & Processes

Functions are complete (contrary to objectives) and abstract (contrary to activities) descriptions of what organizations (represented by actors), system architectures (represented by services), or objects (through operations) can do. As such they are akin to interfaces or types, and cannot be instanciated on their own. Processes on the contrary describe how activities are executed, i.e instanciated (#).

Business processes describe sets of execution instances (#). Functions describe what can be expected from enterprise or functional architectures. Business logic describe how the flows are to be processed.

Business processes describe sets of execution instances (#). Functions describe what can be expected from enterprise or functional architectures. Business logic describe how the flows are to be processed.

That understanding provides for a modular approach to business processes:

  • Business processes can be defined with regard to business functions independently of the way they are supported.
  • Business rules can be managed independently of the way they are applied, by people or systems.
  • Business logic can be factored out in functions (business or systems) or set within specific processes.

Yet that would not be possible without some modeling across enterprise architecture layers.

Functions & Models

Functions are meant to facilitate reuse across enterprise architectures, which entails descriptions that are clearly and easily accessible: context, modus operandi, expected outcome. Whatever the modeling method(s) in use, it’s safe to assume that different stakeholders across enterprise architectures will pursue different objectives, to be defined with different concepts. If they are to communicate they will need some explicit and unambiguous semantics for the links between processes, functions, and activities:

  • Functional flows are used between processes and functions (a) or actors (d), or between actors and functions (e).
  • Composition or aggregates are used to specify where the business logic is to be employed, by functions (b) or by processes (c).
  • Documentation references (f) are used between unspecified actors and business logic, in case it would performed by people.
Semantics of connectors: functional flows (a,d,e), aggregates (b) and composition (c), and documentation (f).

Semantics of connectors: functional flows (a,d,e), aggregates (b) and composition (c), and documentation (f).

Finally, the semantics of connectors used between functions will have to be consistent with the one used to connect them to processes and activities.

Combining Functions

Considering that functions are neatly set within the systems modeling realm, one would assume that inheritance and structure connectors can be used to detail and combine them. Yet, since functions cannot be instantiated, some paring down can be applied to their semantics:

  • Traditional structure connectors are set with regard to identification: bound to structure for composition, set independently otherwise. Since functions have no instances that criterion is irrelevant and the same reasoning goes for composition.
  • Likewise, since functions have no states to be considered, inheritance of functions can be represented by aggregates.

Functions can be combined at will using only aggregates

As far as functions are concerned, structures as well as inheritance connectors can be fully and soundly replaced by aggregate ones, which could significantly improve the mapping of business processes, activities, and supporting functions.

Further Readings