2007-05-14ivoa interop beijing, dm i analysis of characterisation in domain model context with...

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

2007-05-14 IVOA Interop Beijing, DM I

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

2007-05-14 IVOA Interop Beijing, DM I

2007-05-14 IVOA Interop Beijing, DM I

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

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?)