2007-05-14ivoa interop beijing, dm i analysis of characterisation in domain model context with...
TRANSCRIPT
2007-05-14 IVOA Interop Beijing, DM I
Analysis of Characterisation in Domain Model Context
With application to (SNAP) simulations
Gerard LemsonDWith feedback from (but don’t blame):
Mireille Louys, Francois BonnarelClaudio 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
2007-05-14 IVOA Interop Beijing, DM I
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?
2007-05-14 IVOA Interop Beijing, DM I
Motivation
• The thing that is characterised does (did?) not occur explicitly 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
2007-05-14 IVOA Interop Beijing, DM I
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
2007-05-14 IVOA Interop Beijing, DM I
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
2007-05-14 IVOA Interop Beijing, DM I
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
2007-05-14 IVOA Interop Beijing, DM I
Back to simulations
• Logical model– application targeted– simpler, less normalised– 1 characterisation object
2007-05-14 IVOA Interop Beijing, DM I
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?)