Models are meant to characterize categories of given (descriptive models), designed (prescriptive models), or hypothetical (predictive models) individuals.
Insofar as purposes can be kept apart, the discrepancies in targeted individuals can be ironed out, notably by using power-types. Otherwise, e.g if modeling concerns mix business analysis with software engineering, meta-models are introduced as jack-of-all-trades to deal with mixed semantics.
But meta-models generate exponential complexity when used across domains, not to mention the open and fuzzy ones of business intelligence. What is at stake can be better understood through the way individuals are identified and represented.
Partitions & Abstraction
Since models are meant to classify instances with regard to concerns, mixing concerns is to entail mixed classifications, horizontally across domains (e.g business and accounting), or vertically along engineering cycles (e.g business and engineering).
That can be achieved with power-types, meta-classes, or ontologies.
Delegation & Power-types
Given that categories (or classes or types) represent set of instances (given, designed, or simulated), they may by themselves be regarded as symbolic instances used to manage features commonly valued by their own members.
This approach is consistent as well as effective providing the semantics and identities of instances are shared by all agents concerned, e.g business and technical aspects of car rentals. Yet it falls short on both accounts when abstractions levels a set across domains, inducing connectors with different semantics and increased complexity.
Abstraction & Sub-classes
Sub-classes often appear as a way to overcome the difficulty, as illustrated by the Hepp Research’s Vehicle Sales Ontology: despite being set at different abstraction levels, instances for cars and models are defined and identified uniformly.
If, in that case, the model fulfills the substitution principle for external consistency, sub-types will fall short if engineering concerns were to be taken into account because the set of individual car models could then differ depending on perspective.
Meta-classes & Stereotypes
The overuse of meta-classes and stereotypes epitomizes the escapism school of modeling. That may be understandable, if not helpful, for the former which is by nature a free pass to abstraction; less so for the latter which is supposed to go the other way toward the specialization of meta-classes according to specific profiles. It ensues that stereotypes should never be used on their own or be extended by another stereotype.
As it happens, such consistency concerns appear to be easily diluted when stereotypes are jumbled with meta constructs; e.g:
- Abstract stereotype (a).
- Strong (aka class) inheritance of abstract stereotype from concrete meta-class (b).
- Weak inheritance (aka aspect) between stereotypes (c, f).
- Meta-constraint used to map Vehicle to methodology stereotype (d).
- Domain specific connector to stereotype (e).
Without comprehensive and consistent semantics for instances and abstractions, individuals cannot be solidly mapped to models:
- Individual concepts (car, horse, boat, …) have no clear mooring.
- Actual vehicles cannot be tied to the meta-class or the abstract stereotype.
- There is no strong inheritance tying individuals models and rental cars to an identification mechanism.
Assuming that detachment is not an option, the basis of models must be reset.
Put in simple words, ontologies are meant to examine the nature and categories of existence, in general (metaphysics) or in specific contexts. As for the latter, they can be applied to individuals according to the nature of their existence (aka epistemic identity): concepts, documents, categories and aspects.
As it happens, sorting things out makes the whole paraphernalia of meta-classes and stereotypes no longer needed because the semantics of inheritance and associations is set by the nature of individuals.
With individuals solidly rooted in targeted domains, models can then fully serve their purposes.
What’s the Point
It’s worth to remind that models have to be built on purpose, which cannot be achieved without a clear understanding of context and targets. Here are some examples:
Quality arguably come first:
- External consistency: to be checked by mapping individuals in analysis categories to big data.
- Internal consistency: to be checked by mapping individuals in design classes to observed or simulated run-time components.
- Acceptance: automated tests generation.
Then, individuals could be used to map models to past and future, the former for refactoring, the latter for business intelligence.
Refactoring looks to the past but is frustrated by undocumented legacy code. Combined with machine learning, individuals could help to bridge the gap between code and models.
Business Intelligence looks the other way as it is meant to map hypothetical business objects and behaviors to the structures and semantics already managed by information systems. As in reversal of legacy benefits, individuals could provide cues to new business meanings.
Finally, maturity assessment and optimization of enterprise processes fully depend on the reliability of their basis.