Meta Data

1
Meta Data

• Larry, Stirling md on data access data types,
  domain meta-data discovery
• Scott, Ohio State caBIG md driven architecture
  semantic md
• Alexander Vienna md tailored to optimise data
  integration do we need one data source or other
  in integration, performance capability
• Elias OGSA-DAI any md aspects
• Leena schema integration add semantics to
  schema element to aid finding elements and in
  integrating them for matching
• Mario OGSA-DAI extracting md, 3rd party
  registries for OGSA-DAI capabilities? OD not
  build registries but use globus registries
• Jessie domain semantics in md, discovery,
  integration

2
Meta Data

• For What?
• Discovery
• Data Access
• Data Integration
• Optimisation
• Service composition?
• What?
• Ontologies most conceptual
• Schema - data types
• Content -
• Capability

3
MetaData for Discovery Integration

• caBIG - Scott
• Content
• Structure (XML schema)
• Semantics (relationship to ontology)
• Data Registry
• Describe data model (UML) no constraints
• Review curated semantic ontology (EVS, UMLS
  proprietary)
• Bind UML model map to ontology
• ISO stnd md repositories class attribute value
  domain semantic concept.
• If data type doesnt exist add concept to
  ontology
• UML objects -gt XML (GME, data type in XML
  schemas)
• Issues
• Hard to get new types into ontology
• Review process restrictive to certain users
• What aspects of this could be relaxed without
  collapsing the whole system
• e.g. remove requirement for centralised ontology
• Currently ontology resides external to the
  registry, Registry doesnt understand ontology
• what added functionality would you get if you
  added semantics to the registry?

4
MetaData for Optimisation

• SemDIG (GRIDMiner) - Alex
• Lots of data sources want to choose the right
  one for data integration based on the data (does
  it contribute to the answer?)
• If several sources contribute which one(s) would
  be the best
• Technical information required not semantics as
  such
• Information on distribution, ranges etc (summary
  data)
• Defining a common meta data set for data sources
• Solved
• the metadata to be collected (data statistics),
• the collection of the data
• Questions
• Can we uniformly present histograms and data
  required?
• Pmml? Predictive model markup language (xml
  schema for describing decision trees,
  dictionaries etc)
• What is the architecture for presenting it?
• Can OGSA-DAI, data cutter etc. help?
• Possibly integrate ideas in OGSA-DAI
• Requires background threads

5
MetaData for Discovery

• GEODE (Larry)
• Has syntactic discovery based on DB schema
• Supported by OGSA-DAI Really data access but not
  discovery
• Doesnt tell the users what the data that is
  being exposed is about
• good if you know what you want
• Use OGSA-DAI to expose the semantics - for domain
  specific discovery
• Is this possible or sensible? (Japanese team -
  RDF OMII UK team - GRIMOIRES)
• How?
• Representation mechanism
• Mark up of the semantics of the domain in
  whatever the external ontology uses
• Storage of the semantics for discovery
• Need a metadata registry
• well record terms bound to local data structure
  which points to the external ontology
• If multiple ontologies used need to refer to
  the specific ontology used
• Reason about the concepts for discovery -
  external
• Requires a reasoning engine
• Return possible concepts for checking in the data
  registry
• Issues
• Creation/association of data with the ontology
  terms (similar to schema mapping)

6
MetaData for Access

• Dave
• Maintaining metadata separately from data
• e.g. a metadata catalogue linked to files
• Issue
• How can we maintain the metadata?
• Have service controlling both
• Notification system

7
Generation of MetaData Ontologies

• All preceding technologies rely on good metadata
  ontologies
• How do we encourage users/communities to create
  metadata or ontologies
• Know they are correct
• What are the limits - performance etc.
• Service infrastructure for representing
  controlled vocabularies
• LEXGRID
• API for graph operations etc.
• Point at which size of ontologies makes reasoning
  too expensive
• Survey of scalability techniques for reasoning
  with ontologies
• What they are doing about it