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Meta-modeling is the analysis, construction and development of the frames, rules, constraints, models and theories applicable and useful for the modeling in a predefined class of problems. The meta-data side of the diagram consists of a concept diagram. This is basically an adjusted class diagram as described in Booch, Rumbaugh and Jacobson (1999).
Consistency between all diagrams (class diagram, state chart, and business process models) relates to the correctness and completeness of reasons. Every event that represents a reason for transitioning between states in the state chart should have an equivalent event that represents a reason for the process activity in the business process ...
The primary thinking processes, as codified by Goldratt and others: Current reality tree (CRT, similar to the current state map used by many organizations) — evaluates the network of cause-effect relations between the undesirable effects (UDE's, also known as gap elements) and helps to pinpoint the root cause(s) of most of the undesirable effects.
Process models are core concepts in the discipline of process engineering. Process models are: Processes of the same nature that are classified together into a model. A description of a process at the type level. Since the process model is at the type level, a process is an instantiation of it. The same process model is used repeatedly for the ...
One model that incorporates the Bayesian theory of concept learning is the ACT-R model, developed by John R. Anderson. [citation needed] The ACT-R model is a programming language that defines the basic cognitive and perceptual operations that enable the human mind by producing a step-by-step simulation of human behavior. This theory exploits ...
The domain hierarchy and constraints are also given. The constraints are expressed as sentences in the formal theory of the meta model. [8] There are several notations for data modeling. The actual model is frequently called "entity–relationship model", because it depicts data in terms of the entities and relationships described in the data. [4]
A constrained conditional model (CCM) is a machine learning and inference framework that augments the learning of conditional (probabilistic or discriminative) models with declarative constraints. The constraint can be used as a way to incorporate expressive [ clarification needed ] prior knowledge into the model and bias the assignments made ...
Learning constraints representing these partial evaluation is called graph-based learning. It uses the same rationale of graph-based backjumping . These methods are called "graph-based" because they are based on pairs of variables in the same constraint, which can be found from the graph associated to the constraint satisfaction problem.