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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 NeOn Methodology with its method-specific working model.
Knowledge Modeling
NeOn Methodology
Paper illustration of Experiment Canvas with a method-specific labelled workspace.
Product Discovery
Experiment Canvas
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.The NeOn Methodology structures ontology work as a controlled process with reuse, scenarios, and reviews. It is useful when ontologies should not emerge ad hoc but modularly and reusably.When experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric.The Learning Card connects traceable test results to a reasoned next action while keeping the limits of the insight visible.
Complexitydifferent
MediumHighLowLow
Timedifferent
2–4 h review as a planning range; preparation and evaluation additionalMehrere Wochen bis Monate30-60 min25-40 min
Participantsdifferent
Designers, developers as users, and facilitation2-101-51-5
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Prioritized development tasks, Jointly produced code or pseudocode, Observed difficulties and open design questions, Proposed design revisionsOntology Requirements Specification, Reuse Plan, Ontology Modules, Evaluation ReportCompleted Experiment Canvas, Success MetricLearning Card with evidence and next action
Tagsno overlap
ArchitectureEngineeringFeedbackValidationTasks
OntologyMethodologyReuseSemanticEngineering
ExperimentsValidationDiscoveryHypothesis
ExperimentsValidationDiscoveryLearning
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