The Virtual SolarTerrestrial Observatory and SPASE

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The Virtual Solar-Terrestrial Observatory and
SPASE

• Peter Fox
• HAO/ESSL/NCAR
• VSTO team
• Deborah McGuinness, Luca Cinquini, Patrick
  West, Jose Garcia, Tony Darnell, James
  Benedict, Don Middleton, Stan Solomon
• McGuinness Associates
• Knowledge Systems and AI Lab, Stanford Univ.
• SCD/CISL/NCAR

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Outline

• VSTO introduction
• VSTO status
• Role of semantics
• SPASE considerations
• Final remarks

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VSTO Introduction

• Datasets alone are not sufficient to build a
  virtual observatory VSTO integrates tools,
  models, and data (real and virtual)
• VSTO addresses the interface problem, effectively
  and scalably
• VSTO addresses the interdisciplinary metadata and
  ontology problem - bridging terminology and use
  of data across disciplines
• VSTO leverages the development of schema that
  adequately describe the
• syntax (name of a variable, its type, dimensions,
  etc. or the procedure name and argument list,
  etc.),
• semantics (what the variable physically is, its
  units, etc.) and
• pragmatics (or what the procedure does and
  returns, etc.) of the datasets and tools.
• VSTO provides a basis for a framework for
  building and distributing advanced data
  assimilation tools

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Integrative use-cases

• Find data which represents the state of the
  neutral atmosphere anywhere above 100km and
  toward the arctic circle (above 45N) at any time
  of high geomagnetic activity.
• Translate this into a complete query for data.
  Was all the needed information recorded?
• Information needs to be inferred (and integrated)
  from the use-case
• What is returned Data from instruments, indices
  and models.

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VSTO Status

• Semantic framework developed and built with a
  small team in a relatively short time
• Production portal released, includes security,
  etc. with full community migration (and so far
  endorsement)
• VSTO ontology version 0.4, (vsto.owl)
• Web Services encapsulation of semantic interfaces
  being documented
• More use-cases to drive the completion of the
  ontologies - filling out the instrument ontology

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VSTO - semantics and ontologies in an operational
environment vsto.hao.ucar.edu, www.vsto.org
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SPASE considerations
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Importance of (interface) stds - early days of
VxOs
VO2
VO3
VO1
DBn
DB2
DB3

DB1
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The Astronomy approach

• VOTable
• Simple Image Access Protocol
• Simple Spectrum Access Protocol
• Simple Time Access Protocol

VO App2
VO App3
VO App1
VO layer
DBn
DB2
DB3

DB1
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VO3
VO2
VO1
Query, access and use of data
Semantic mediation layer - VSTO - low level
SPASE
Metadata, schema, data
DBn
DB2
DB3

DB1
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Semantic Mediation Layer

• Ontology - capturing concepts of Parameters,
  Instruments, Date/Time, Data Product (and
  associated classes, properties) and Service
  Classes
• Maps queries to underlying data
• Generates access requests for metadata, data
• Allows queries, reasoning, analysis, new
  hypothesis generation, testing, explanation, etc.

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What is an Ontology A branch of study concerned
with the nature and relations of being, or things
which exist. A formal machine-operational
specification of a conceptualization.Semantic
Web an extension of the current web in which
information is given well-defined meaning, better
enabling computers and people to work in
cooperation, www.semanticweb.org
Thesauri narrower term relation
Frames (properties)
General Logical constraints
Formal is-a
Catalog/ ID
Informal is-a
Formal instance
Disjointness, Inverse, part-of
Terms/ glossary
Value Restrs.
based on AAAI 99 Ontologies panel McGuinness,
Welty, Ushold, Gruninger, Lehmann
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Plan To annotate this with SPASE mapping
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Final remarks

• VSTO in production, diverse datasets, robust
  semantic framework
• SPASE mapping into VSTO
• Some progress but not complete
• Publish results and a report
• Will be looking for a few SPASE-ready datasets
• SPASE and interoperability - partial solution
• Contact pfox_at_ucar.edu

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Additional slides
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Why we were led to semantics

• When we integrate, we integrate concepts, terms
• In the past we would ask, guess, research a lot,
  or give up
• Its pretty much about meaning
• Semantics can really help find, access,
  integrate, use, explain, trust
• What if you
• could not only use your data and tools but remote
  colleagues data and tools?
• understood their assumptions, constraints, etc
  and could evaluate applicability?
• knew whose research currently (or in the future)
  would benefit from your results?
• knew whose results were consistent (or
  inconsistent) with yours?

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Issues for Virtual Observatories

• Providing for multiple VOs consider
  federating/aggregating rather than one-on-one
• Scaling to large numbers of data providers
• Crossing disciplines
• Security, access to resources, policies
• Branding and attribution (where did this data
  come from and who gets the credit, is it the
  correct version, is this an authoritative
  source?)
• Provenance/derivation (propagating key
  information as it passes through a variety of
  services, copies of processing algorithms, )
• Data quality, preservation, stewardship, rescue
• Funding for participation - how to leverage
  existing efforts
• Interoperability at a variety of levels (3)

Semantic Web ontologies, reasoning, etc. are one
approach to address many of these issues