Analysis of Characterisation in Domain Model Context

1. Analysis of Characterisationin Domain Model Context With application to (SNAP) simulations Gerard Lemson DWith feedback from (but don’t blame): Mireille Louys, Francois Bonnarel Claudio Gheller, Patrizia Manzato, Laurie Shaw, Herve Wozniak Miguel Cervino, Igor Chilingarian, Norman Gray, Jaiwon Kim, Franck Le Petit, Ugo Becciani, Sebastien Derriere Especially do not blame: Pat Dowler IVOA Interop Beijing, DM I

2. Goal • Understand characterisation ... • context • use • application to (SNAP) simulation data model:beyond space/time/lambda/flux ... through feedback from you • Apply to SNAP • note that use there probably not typical (pattern iso direct reuse?) • Maybe find uses elsewhere? IVOA Interop Beijing, DM I

3. Motivation • The thing that is characterised does (did?) not occurexplicitly inside characterisation model (Observation is gone) • Found characterisation-like features in SNAP data model, useful for discovery that do contain this thing explicitly • Carries over directly to full domain model IVOA Interop Beijing, DM I

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6. The simulation model • Focuses on experiments, which: • have target objects • which have observables • which have typical values (as function of time) • have representations • consisting of (simulation dependent) object types • which have (simulation dependent) properties/observables (mass, position, wavelength, flux, temperature, entropy etc) • have input parameters • have results • which have collections of measurement (simulation) objects (corresponding to the representation object types) • which assign values (and errors) to the properties IVOA Interop Beijing, DM I

7. Use values/params for discovery • The full data (results) can not be used as they are in discovery and (SXAP-)queryData • It is hard to query on input parameters when semantics, and consequences not well known/understood • Nevertheless useful info contained in them and desired for querying • Use statistical description characterising the results, both a priori and a posteriori IVOA Interop Beijing, DM I

8. In domain • Domain model analyses the domain • a priori characterisation: • restricts possible values an observable may have • summarises effects of input parameters • similar to Characterisation DM (private comm HMcD, ML last year) ?? • a posteriori characterisation • summarises actual results • statistics of particular observable in result collection of objects IVOA Interop Beijing, DM I

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12. Back to simulations • Logical model • application targeted • simpler, less normalised • 1 characterisation object IVOA Interop Beijing, DM I

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14. Conclusion • Treat characterisation as a pattern iso reusable software/dm component • Coverage characterisation of values • not (yet) of errors (is this Accuracy?) • necessary for discovery and query (of simulations)? • No • accuracy • where does this go for simulations • where in domain? • resolution (does this belong on target object, iso representation) • sampling precision (a priori?) IVOA Interop Beijing, DM I