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Criterion
Design sheets and interface cards connect to jointly written pseudocode with highlighted unresolved questions.
Architecture
ARID (Active Reviews for Intermediate Designs)
Paper illustration of Ontology Design Patterns with its method-specific working model.
Knowledge Modeling
Ontology Design Patterns
Paper illustration of MVP Test Matrix with a method-specific labelled workspace.
Product Discovery
MVP Test Matrix
Paper illustration of a Learning Card with four fields for hypothesis, observation, insight, and action.
Product Discovery
Learning Card
Purposedifferent
ARID examines how well developers can work with a technical design. It focuses on a part of the system that can still be revised, between the architecture overview and the detailed specification.Ontology Design Patterns provide reusable building blocks for recurring modeling problems. The method brings structure to the search for clean concepts, relationships, and roles.When several minimal variants compete to answer the same question, it makes comparing and choosing between them cleaner. It separates problem, assumption, solution, and evidence. The result is captured as a Test Matrix and a Test Plan.The Learning Card connects traceable test results to a reasoned next action while keeping the limits of the insight visible.
Complexitydifferent
MediumMediumMediumLow
Timedifferent
2–4 h review as a planning range; preparation and evaluation additional1-3 h pro Pattern45-75 min25-40 min
Participantsdifferent
Designers, developers as users, and facilitation1-62-81-5
Formatdifferent
Workshop + asyncWorkshop + asyncWorkshopWorkshop + async
Outputdifferent
Prioritized development tasks, Jointly produced code or pseudocode, Observed difficulties and open design questions, Proposed design revisionsSelected Patterns, Adapted Schema Fragments, Pattern DocumentationTest Matrix, Test PlanLearning Card with evidence and next action
Tagsno overlap
ArchitectureEngineeringFeedbackValidationTasks
OntologyPatternsReuseSemanticModeling
ExperimentsValidationDiscoveryOptions
ExperimentsValidationDiscoveryLearning
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