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Virtual Observatories and Virtual Grids: The Interplay in Fully Exploiting Solar-Terrestrial Data. Mauro Messerotti INAF-Astronomical Observatory of Trieste and Department of Physics, University of Trieste. Outline of the Talk. Historical evolution in S-T-Phys. data handling - PowerPoint PPT Presentation
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M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
Virtual Observatories and Virtual Grids: Virtual Observatories and Virtual Grids: The Interplay in Fully ExploitingThe Interplay in Fully Exploiting
Solar-Terrestrial DataSolar-Terrestrial Data
Mauro MesserottiMauro MesserottiINAF-Astronomical Observatory of TriesteINAF-Astronomical Observatory of Trieste
andandDepartment of Physics, University of TriesteDepartment of Physics, University of Trieste
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
Outline of the TalkOutline of the Talk
• Historical evolution in S-T-Phys. data handlingHistorical evolution in S-T-Phys. data handling
• Requirements for S-T Physics & Space WeatherRequirements for S-T Physics & Space Weather
• The role of VOs & VGridsThe role of VOs & VGrids
• Knowledge Embedding via CmapsKnowledge Embedding via Cmaps
• Visions about an ideal VOVisions about an ideal VO
• ConclusionsConclusions
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
HISTORICAL EVOLUTION INHISTORICAL EVOLUTION INS-T PHYSICS DATA HANDLINGS-T PHYSICS DATA HANDLING
REQUIREMENTS FORREQUIREMENTS FOR
S-T PHYSICS ANDS-T PHYSICS AND
SPACE WEATHER SPACE WEATHER
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
Solar and Geophysical DatabasesSolar and Geophysical Databases
Solar and Geophysical Databases: The Solar and Geophysical Databases: The TilesTiles
of a Planetary Meta-Archiveof a Planetary Meta-Archive
M. MesserottiTrieste Astronomical Observatory
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
Data OrganizationData Organization
• Matter of Fact Huge amount of space and g-b data
• Data Organization Databases, Archives, Meta-Archives
• Data Indexing Tables, Catalogs managed by RDBMS
• Data Access FTP, TELNET, WWW via GUI
• Data Search Local, Distributed over the net
• Data Analysis Local
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
Scientific RequirementsScientific Requirements
• Physical modelling MULTIWAVELENGTH DATA SEARCHMULTIWAVELENGTH DATA DISPLAY
MULTIWAVELENGTH DATA ANALYSIS via a common unified, user-friendly interface
• Space Weather SOLAR, SPACE, EARTH DATASETSMULTI-EVENT MODELLINGLARGEST COVERAGE POSSIBLE
• Event Prediction CROSS-SEARCH OVER ARCHIVESSTATISTICAL ANALYSESREAL-TIME DATA AVAILABILITY
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
Scientific MotivationsScientific Motivations
• Some major Solar-Terrestrial Data Portals exist
• Mainly Resource Indexing is available
• Few resources partially allow complex, distributed data searching over limited subsets of databases
• Very few resources partially allow data analysis on inhomogeneous datasets
A PLANETARY META-ARCHIVE IS NEEDED TO EXPLOIT THEFULL SCIENTIFIC POTENTIALITIES OF MULTIWAVELENGTHMODELLING IN SOLAR-TERRESTRIAL PHYSICS
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
Solar-Terrestrial Physics PortalsSolar-Terrestrial Physics Portals
CDS AstroWebhttp://cdsweb.u-strasbg.fr/astroweb.html
NASA Space Physics Data System (SPDS)http://spds.nasa.gov/
NASA Space Physics Data Facility (SPDF)http://nssdc.gsfc.nasa.gov/spdf/Magnetospheric Yellow Pages
http://nssdc.gsfc.nasa.gov/spdf/yellow-pages/data-by-type.html
NASA National Space Science Data Center (NSSDC)http://nssdc.gsfc.nasa.gov/
Canadian Astronomy Data Centerhttp://cadcwww.dao.nrc.ca/
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
CDS AstroWeb - Astronomy on the InternetCDS AstroWeb - Astronomy on the Internethttp://cdsweb.u-strasbg.fr/astroweb/solar.htmlhttp://cdsweb.u-strasbg.fr/astroweb/solar.html (A) (A)
Big Bear Solar Observatory (BBSO)
Boulder, Colorado – Dept. Astrophys. and Planet. Sciences
Birmingham Solar Oscillations Network (BiSON)
Catania Astrophysical Observatory (OAC)
Centre de Prevision de l'activite solaire et geomagnetique
Cluster II, ESA's spacefleet to the magnetosphere
Cracow - Solar radio emission in dm wavelength
Departement d'Astronomie Solaire (DASOP, Observatoire de Paris)
ETH Institute of Astronomy (ETH Zurich)
Estación de Observación Solar / Solar Observational Station (EOS)
European Incoherent SCATtter
GALLEX
ARTHEMIS
BAse Solaire Sol 2000 (BASS2000)
Global Oscillation Network Group (GONG)
Haleakala Observatories (Hawaii)
High Altitude Observatory (HAO)
High Energy Solar Spectroscopic Imager (HESSI)
Hiraiso Solar Terrestrial Research Center/CRL
IPS Radio & Space Services
Imager for Magnetopause-to-Aurora Global Exploration
Institut d'Astrophysique Spatiale (IAS)
Instituto de Astronomia y Fisica del Espacio (IAFE)
International Solar-Terrestrial Physics (ISTP)
Joint Organization for Solar Observations (JOSO)
Kharkov multi-wave station of solar monitoring (KHASSM)
Kiepenheuer-Institut für Sonnenphysik (KIS)
Laboratory for Atmospheric and Space Physics (LASP)
LASCO/SOHO
MEDOC (Multi-Experiment Data Operations Center for SOHO)
MSU Solar Physics Group (Montana)
Mees Solar Observatory (MSO, Hawaii)
Metsahovi Radio Research Station
Mount Wilson Observatory
NRL Solar Physics Branch
National Astron. Obs. of Japan - Solar Phys. Division
GDC
GDC
GSO
GSN
SRI
SOO
SEC
SRO
SSE
SRI
SRI
SOO
GIN
GPE
GSN
SOO
SOO
SSE
SEC
SEC
ESE
SPI
SPI
STN
ISO
SRI
SRO
SRI
SSE
SDC
SRI
SOO
SRO
SOO
SRI
SRI
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
CDS AstroWeb - Astronomy on the InternetCDS AstroWeb - Astronomy on the Internethttp://cdsweb.u-strasbg.fr/astroweb/solar.htmlhttp://cdsweb.u-strasbg.fr/astroweb/solar.html (B) (B)
Naval Research Laboratory Space Science Division (NRL SSD)
Service d'Aeronomie
Observatoire Midi-Pyrenees (OMP)
Soft X-Ray Telescope onboard Yohkoh Satellite, ISAS, Japan
Solar Data Analysis Center (SDAC)
Solar Extreme-ultraviolet Rocket Telescope and Spectrograph
Solar Flare Theory (NASA/Goddard Space Flight Center)
Solar Group of RATAN-600
Solar Physics Division - American Astronomical Society
Solar Physics at Stanford University
Solar Terrestrial Activity Report
Solar Terrestrial Dispatch (STD)
National Solar Observatory (NSO)
NSO Sacramento Peak, Sunspot, NM (NSO/SP)
Solar UV Atlas from HRTS (HRTS data)
Solar and Heliospheric Observatory (SOHO)
Solar, Auroral, Ionospheric, ... Information (Lethbridge, Canada)
Solar-Terrestrial Physics Home Page (STP)
Space Environment Center
Stanford SOLAR Center
Sternberg Astronomical Institute (Heliophys. and Seismology)
THEMIS
The INTER-SOL Sun Observation Programme (ISP)
Transition Region And Coronal Explorer (TRACE)
Universitat de les Illes Balears - Solar Phys. at Dept. of Phys.
Wilcox Solar Observatory (WSO)
Yohkoh Public Outreach Project (YPOP)
Zurich Solar Radio Spectrometer
64 Entries
SOO
SOO
SPI
SOO
EDC
SSE
SEC
SRI
SSE
SRO
NSO
SRI
SEC
SEC
SDC
SSE
SEC
SEC
SEC
SEC
SRI
SOI
GSN
SSE
SRI
SSE
SOO
SRO
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
GoalsGoals
• Index observational resources in ST Physics
• Index theoretical resources in ST Physics
• Allow
• User-transparent data access to distributed datasets all over the world
• Complex data searching, retrieval and analysis via a simplified common GUI
PRESENT DATA ARCHIVING TECHNOLOGIES ALLOW THEACHIEVEMENT OF SUCH GOALS PROVIDED THAT A GLOBALCOORDINATION AND COLLABORATION IS ESTABLISHEDAS WELL AS THE ALLOCATION OF PROPER FINANCIALRESOURCES BY THE PARTICIPATING ORGANIZATIONS
COST Action 724 Athens, 29-30 January 2004 M. Messerotti, INAF-OATs & UNI-TSCOST Action 724 Athens, 29-30 January 2004 M. Messerotti, INAF-OATs & UNI-TS
An Advanced System for Data Handling in SPWAn Advanced System for Data Handling in SPW
• Title of contribution Title of contribution STEVM (Solar-Terrestrial Environment Virtual STEVM (Solar-Terrestrial Environment Virtual Monitor)Monitor)
• Proposer Proposer M. Messerotti (& al.)M. Messerotti (& al.)
• Relevant MoU objectivesRelevant MoU objectives Data standardization and Data standardization and accessibilityaccessibility
• Relevant parts of MoU sci. programme Relevant parts of MoU sci. programme Aims of WG4Aims of WG4
• DeliverablesDeliverables Resources survey & architecture Resources survey & architecture (& ?)(& ?)
• TimetableTimetable 3-5 years according to final goals3-5 years according to final goals
• Required ManpowerRequired Manpower To be defined according To be defined according to final goalsto final goals
• Resources availabilityResources availability Existing Data Archives Existing Data Archives communitycommunity
• Expected collaborationsExpected collaborations With main VO projects (e.g. With main VO projects (e.g. EGSO)EGSO)
• Previous experience in the fieldPrevious experience in the field SOLAR, SOLRA,SOLAR, SOLRA, SOLARNET, , EGSO
COST Action 724 Athens, 29-30 January 2004 M. Messerotti, INAF-OATs & UNI-TSCOST Action 724 Athens, 29-30 January 2004 M. Messerotti, INAF-OATs & UNI-TS
Space Weather as Driver of Data HomogeneizationSpace Weather as Driver of Data Homogeneization
• Inhomogeneous and fragmented character of Inhomogeneous and fragmented character of available observationsavailable observations
CAUSESCAUSESDifficulties in carrying out a posteriori modelling of complex Difficulties in carrying out a posteriori modelling of complex
phenomenaphenomena
• These limitations are intrinsic to data acquisition These limitations are intrinsic to data acquisition modemode
HENCEHENCEEven advanced data search by Grid architectures cannot Even advanced data search by Grid architectures cannot
overcomeovercome
COST Action 724 Athens, 29-30 January 2004 M. Messerotti, INAF-OATs & UNI-TSCOST Action 724 Athens, 29-30 January 2004 M. Messerotti, INAF-OATs & UNI-TS
DRAWBACKS OF DATA INADEQUACYDRAWBACKS OF DATA INADEQUACY
• The outcomes are:The outcomes are:
– Inadequate modellingInadequate modelling
– Limited to a subset of phenomenological and physical Limited to a subset of phenomenological and physical aspectsaspects
– Often neglects the complex interplays among different Often neglects the complex interplays among different processesprocesses
COST Action 724 Athens, 29-30 January 2004 M. Messerotti, INAF-OATs & UNI-TSCOST Action 724 Athens, 29-30 January 2004 M. Messerotti, INAF-OATs & UNI-TS
Scheme of SpW Data RequirementsScheme of SpW Data Requirements
SPACESPACEWEATHERWEATHER
SPACESPACEWEATHERWEATHER
EffectsEffectsEffectsEffects
PrecursorsPrecursorsPrecursorsPrecursors
DriversDriversDriversDrivers
NRTNRTOBSERVATIONOBSERVATION
NRTNRTOBSERVATIONOBSERVATION
NRTNRTPUBLICATIONPUBLICATION
NRTNRTPUBLICATIONPUBLICATION
NRTNRTINGESTIONINGESTION
NRTNRTINGESTIONINGESTION
NRTNRTModellingModellingPredictionPrediction
NRTNRTModellingModellingPredictionPrediction
Post-EventPost-EventModellingModelling
InterpretationInterpretation
Post-EventPost-EventModellingModelling
InterpretationInterpretation
CompletenessCompletenessCompletenessCompleteness
HomogeneityHomogeneityHomogeneityHomogeneity
NRTNRTAvailabilityAvailability
NRTNRTAvailabilityAvailability
ImprovedImprovedInterpretationInterpretation
ImprovedImprovedInterpretationInterpretation
ImprovedImprovedModellingModellingImprovedImprovedModellingModelling
ImprovedImprovedPredictionPredictionImprovedImprovedPredictionPrediction
StandardizationStandardizationStandardizationStandardization
TimeTimeTimeTime
SpaceSpaceSpaceSpace
WavelengthWavelengthWavelengthWavelength
SpW DataSpW DataRequirementsRequirements
SpW DataSpW DataRequirementsRequirements
COST Action 724 Athens, 29-30 January 2004 M. Messerotti, INAF-OATs & UNI-TSCOST Action 724 Athens, 29-30 January 2004 M. Messerotti, INAF-OATs & UNI-TS
The Role of Observing Requirements for SpWThe Role of Observing Requirements for SpW
Observing requirementsObserving requirements for SpW and SpW drivers for SpW and SpW drivers observation in monitoring and nowcasting can play observation in monitoring and nowcasting can play a a primary roleprimary role in providing: in providing:
1.1. homogeneization in observationshomogeneization in observations2.2. near real-time data ingestion in archivesnear real-time data ingestion in archives3.3. unified data access via web through a user friendly GUIunified data access via web through a user friendly GUI
capable to facilitate:capable to facilitate:
1.1. data availability in near real-timedata availability in near real-time2.2. full exploitation of the data information content by full exploitation of the data information content by
pointing out interrelationships in different datasetspointing out interrelationships in different datasets3.3. self-consistent modellingself-consistent modelling
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
THE ROLE OF VOs AND V-GRIDSTHE ROLE OF VOs AND V-GRIDS
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
http://solarnet.to.astro.it:8080/portal/http://solarnet.to.astro.it:8080/portal/
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
DATA GRID ArchitectureDATA GRID Architecture
SOLARNETSOLARNETSOLARNETSOLARNET
SOLARSOLARSOLARSOLAR
DISCODISCODISCODISCO SOLRASOLRASOLRASOLRA
SOLARNET is a Data GRIDSOLARNET is a Data GRIDwhich links multiple data collectionswhich links multiple data collections
by managing data entitiesby managing data entitiesacross distributed repositoriesacross distributed repositories
SOLARNET is a Data GRIDSOLARNET is a Data GRIDwhich links multiple data collectionswhich links multiple data collections
by managing data entitiesby managing data entitiesacross distributed repositoriesacross distributed repositories
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
http://www.egso.org/http://www.egso.org/
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
VIRTUAL DATA GRID ArchitectureVIRTUAL DATA GRID ArchitectureA consistent modelling requires a multi-instrument multi-wavelength approachA consistent modelling requires a multi-instrument multi-wavelength approach
DATA ANALYSISDATA ANALYSIS must be provided in addition to SEARCH and RETRIEVALSEARCH and RETRIEVALDATA ANALYSISDATA ANALYSIS must be provided in addition to SEARCH and RETRIEVALSEARCH and RETRIEVAL
VirtualVirtualData GRIDData GRID
VirtualVirtualData GRIDData GRIDData CollectionData CollectionData CollectionData Collection DerivedDerived
Data ProductsData ProductsDerivedDerived
Data ProductsData Products
Data Collection managementData Collection management++
GRID protocols to process dataGRID protocols to process data
Data Collection managementData Collection management++
GRID protocols to process dataGRID protocols to process data
EGSO European Grid of Solar ObservationsEGSO European Grid of Solar ObservationsEGSO European Grid of Solar ObservationsEGSO European Grid of Solar Observations
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
INTELLIGENT VIRTUAL DATA GRID ArchitectureINTELLIGENT VIRTUAL DATA GRID Architecture
DATA ASSOCIATION AND GUIDED PROCEDURESDATA ASSOCIATION AND GUIDED PROCEDURES are EMBEDDEDare EMBEDDEDDATA ASSOCIATION AND GUIDED PROCEDURESDATA ASSOCIATION AND GUIDED PROCEDURES are EMBEDDEDare EMBEDDED
IntelligentIntelligentVirtualVirtual
Data GRIDData GRID
IntelligentIntelligentVirtualVirtual
Data GRIDData GRIDData CollectionData CollectionData CollectionData Collection DerivedDerived
Data ProductsData ProductsDerivedDerived
Data ProductsData Products
Data Collection managementData Collection management++
GRID protocols to process dataGRID protocols to process data++
EMBEDDED KNOWLEDGEEMBEDDED KNOWLEDGE
Data Collection managementData Collection management++
GRID protocols to process dataGRID protocols to process data++
EMBEDDED KNOWLEDGEEMBEDDED KNOWLEDGE
KNOWLEDGE DISCOVERY IN DATABASES (KDD)KNOWLEDGE DISCOVERY IN DATABASES (KDD)KNOWLEDGE DISCOVERY IN DATABASES (KDD)KNOWLEDGE DISCOVERY IN DATABASES (KDD)
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
• Pointing out the Pointing out the physical associationsphysical associations in multi-wavelength in multi-wavelength datasets is the datasets is the basis of interpretative scientific researchbasis of interpretative scientific research
• Concept association Concept association is the is the kernel of knowledgekernel of knowledge
• Automated storage and search of knowledge in databasesAutomated storage and search of knowledge in databases is possibleis possible through advanced techniques and is called through advanced techniques and is called
Knowledge Discovery in Databases (KDD)Knowledge Discovery in Databases (KDD)
• Advanced techniques are based on Advanced techniques are based on Artificial Intelligence (AI)Artificial Intelligence (AI) and Expert Systems (ES) embeddingand Expert Systems (ES) embedding
THE THE EMBEDDING OF AI-ES TECHNIQUESEMBEDDING OF AI-ES TECHNIQUES IN THE GRIDIN THE GRIDARCHITECTUREARCHITECTURE REPRESENTS REPRESENTS THE NEXT GENERATIONTHE NEXT GENERATIONIN DATA SEARCH, RETRIEVAL, PROCESSING AND ANALYZINGIN DATA SEARCH, RETRIEVAL, PROCESSING AND ANALYZING
ADVANCED GOALADVANCED GOAL
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
KNOWLEDGE EMBEDDINGKNOWLEDGE EMBEDDINGVIA CONCEPT MAPSVIA CONCEPT MAPS
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
A Foundation OntologyA Foundation Ontologyforfor
Space MeteorologySpace Meteorology
For a Structured OrganizationFor a Structured Organization
of the Knowledge on the Subjectof the Knowledge on the Subject
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
A Semantic Model for KnowledgeA Semantic Model for Knowledge
KNOWLEDGEKNOWLEDGEKNOWLEDGEKNOWLEDGEConceptsConcepts
andandRelationshipsRelationships
ConceptsConceptsandand
RelationshipsRelationshipsPropositionsPropositionsPropositionsPropositions
CONCEPTCONCEPTCONCEPTCONCEPTPattern ofPattern of
RegularitiesRegularitiesin Objectsin Objects
Pattern ofPattern ofRegularitiesRegularitiesin Objectsin Objects
DescriptiveDescriptiveKnowledgeKnowledge
ElementElement
DescriptiveDescriptiveKnowledgeKnowledge
ElementElement
RELATIONSHIPRELATIONSHIPRELATIONSHIPRELATIONSHIPLogicalLogicalActionActionLink Link
LogicalLogicalActionActionLink Link
InferencingInferencingKnowledgeKnowledge
ElementElement
InferencingInferencingKnowledgeKnowledge
ElementElement
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
Knowledge Representation SchemeKnowledge Representation Scheme
ConceptsConceptsConceptsConcepts RelationshipsRelationshipsRelationshipsRelationships
ConceptsConceptsConceptsConcepts RelationshipsRelationshipsRelationshipsRelationships
ConceptsConceptsConceptsConcepts RelationshipsRelationshipsRelationshipsRelationships PropositionPropositionAA
PropositionPropositionBB
PropositionPropositionCC
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
IHMCIHMC11 Concept Map Features Concept Map Features
• Represent a graphical scheme of Represent a graphical scheme of knowledge in organized formknowledge in organized form
• Are interactively generated by means of a Are interactively generated by means of a multi-platform software toolmulti-platform software tool
• Are implementable as XHTML/XML Are implementable as XHTML/XML documentsdocuments
• External resources can be associated to External resources can be associated to concepts (e.g. scripts, hyperlinks, etc.)concepts (e.g. scripts, hyperlinks, etc.)
• A CXL (Connection Mapping) XL is A CXL (Connection Mapping) XL is implemented)implemented)
1Institute for Human and Machine Cognition, FL, USA
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
IHMC Concept Map Development ToolIHMC Concept Map Development Tool
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
A Sample CmapA Sample Cmap
2-D REPRESENTATION2-D REPRESENTATIONOFOF
A SET OF CONCEPTSA SET OF CONCEPTSANDAND
THEIR INTERRELATIONSHIPSTHEIR INTERRELATIONSHIPS
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
Cmap StructureCmap Structure
CONCEPTCONCEPT
CONCEPTCONCEPT
RELATIONRELATION
PropositionProposition
KNOWLEDGEKNOWLEDGE
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
CONCEPTCONCEPTFRAMEWORKFRAMEWORK
Most inclusiveMost inclusive
Least inclusiveLeast inclusive
HIE
RA
RC
HY
HIE
RA
RC
HY
GENERALIZATIONGENERALIZATION
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
What is a Foundation Ontology?What is a Foundation Ontology?
• ONTOLOGYONTOLOGY describes knowledge and it describes knowledge and it is the is the formulation of a conceptual formulation of a conceptual schema about a domainschema about a domain constructed by: constructed by:– Defining the precise meaning of domain Defining the precise meaning of domain
entities (entities (semanticssemantics))– Identifying the relationships between entities Identifying the relationships between entities
((associativityassociativity))– Stating the rules between entitites/set of Stating the rules between entitites/set of
entities (entities (operativityoperativity))
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
Why do we need a Foundation Ontology?Why do we need a Foundation Ontology?
• No clear definition of the terminologyNo clear definition of the terminology
• No clear definition of the physical domainsNo clear definition of the physical domains
• Interrelationships defined only on a Interrelationships defined only on a fragmentary basis and limited to sub-fragmentary basis and limited to sub-domainsdomains
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
Definition of Space MeteorologyDefinition of Space Meteorology
M. Messerotti, 2005
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
Technological and Environmental EffectsTechnological and Environmental Effects
M. Messerotti, 2005
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
Solar DriversSolar Drivers
M. Messerotti, 2005
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
Review ofReview ofAvailable ModelsAvailable Modelsfor Solar Driversfor Solar Drivers
F. Zuccarello, 2005
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
Solar ActivitySolar Activity
M. Messerotti, 2005
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
VISIONS ABOUT THE IDEAL VOVISIONS ABOUT THE IDEAL VO
THE SEMANTIC VOTHE SEMANTIC VO
FORFOR
SUN-EARTH CONNECTIONSSUN-EARTH CONNECTIONS
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
The Semantic VOThe Semantic VO
ONTOLOGYONTOLOGY&&
SEMANTICSSEMANTICS
SCIENCE &SCIENCE &APPLICATIONSAPPLICATIONS
SCHEMESCHEME
OBSERVATIONALOBSERVATIONALRESOURCES &RESOURCES &
DATA ARCHIVESDATA ARCHIVES
METADATA STANDARDMETADATA STANDARD&&
DATA MODELDATA MODEL
KNOWLEDGE METADATAKNOWLEDGE METADATA&&
KNOWLEDGE MODELKNOWLEDGE MODEL
VIRTUAL OBSERVATORYVIRTUAL OBSERVATORYWITH V-GRID &WITH V-GRID &KNOWLEDGEKNOWLEDGE
DISCOVERY (KD)DISCOVERY (KD)FEATURESFEATURES
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
ONTOLOGY & SEMANTICSONTOLOGY & SEMANTICS
CMAPS
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
Metadata Standards & Data ModelMetadata Standards & Data Model
DescribesDescribes• Multi-disciplinaryMulti-disciplinary• Multi-domainMulti-domain
– Multi-wavelengthMulti-wavelength– Multi-instrumentMulti-instrument
DataData
MULTI-DATAMETAMODEL
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
Knowledge Metadata & Knowledge ModelKnowledge Metadata & Knowledge Model
DescribesDescribes• ConceptsConcepts• InterrelationshipsInterrelationships• Case-Based ReasoningCase-Based Reasoning
KNOWLEDGEMETAMODEL
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
Advanced Brokering SystemAdvanced Brokering System
• OWLOWL Web Ontology Web Ontology LanguageLanguage– Web ServicesWeb Services– Description on Description on
DemandDemand
• KEAKEA Knowledge Knowledge Exchange Exchange ArchitectureArchitecture– Web ServicesWeb Services– CmapsCmaps– CXL Concept Mapping CXL Concept Mapping
XLXL
DATAMETABROKER
KNOWLEDGEMETABROKER
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
MULTI-DOMAIN SPATIAL GUIMULTI-DOMAIN SPATIAL GUI
• AJAXAJAX Asynchronous Asynchronous Javascript & XMLJavascript & XML
– XHTML + CSS for XHTML + CSS for visualsvisuals
– DOM Document DOM Document Object ModelObject Model
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
GRAPHICAL GRAPHICAL DISPLAYDISPLAY
of aof aMULTI-DOMAINMULTI-DOMAINDATA SEARCHDATA SEARCH
From IVOSEC prototyping
M. Messerotti M. Messerotti CODATA, 24 October 2006, BeijingCODATA, 24 October 2006, Beijing
CONCLUSIONSCONCLUSIONS• COMMONS TO SPACE- AND COMMONS TO SPACE- AND
GROUND-BASED MONITORSGROUND-BASED MONITORS::
– HUGE NUMBER OF DATA HUGE NUMBER OF DATA SETSSETS
– LARGE NUMBER OF DATA LARGE NUMBER OF DATA STANDARDSSTANDARDS
– LIMITED DATA LIMITED DATA AVAILABILITYAVAILABILITY
– NON-REAL-TIME NON-REAL-TIME AVAILABILITYAVAILABILITY
– LIMITED DATA LIMITED DATA ACCESSIBILITYACCESSIBILITY
– NON-USER-FRIENDLY NON-USER-FRIENDLY SEARCH AND RETRIEVALSEARCH AND RETRIEVAL
– DIFFICULT DATA DIFFICULT DATA CALIBRATIONCALIBRATION
– COMPLEX DATA ANALYSISCOMPLEX DATA ANALYSIS
– LIMITED CROSS-DATA AN.LIMITED CROSS-DATA AN.
• POSSIBLE SOLUTIONS TO POSSIBLE SOLUTIONS TO MOST ISSUES:MOST ISSUES:– NONE: WILL INCREASE TO NONE: WILL INCREASE TO
PBsPBs– COORDINATION ON COORDINATION ON
COMMON STANDARDSCOMMON STANDARDS– AGREEMENT ON DATA AGREEMENT ON DATA
POLICIESPOLICIES– DEVELOPMENT OF DEVELOPMENT OF
VIRTUAL MONITORSVIRTUAL MONITORS– IMPROVEMENT IN WEB IMPROVEMENT IN WEB
ACCESSIBILITYACCESSIBILITY– ADVANCED DATA ADVANCED DATA
HANDLINGHANDLING– INCORPORATION OF S/W INCORPORATION OF S/W
LIBRARIESLIBRARIES– DEVELOPMENT OF DEVELOPMENT OF
VIRTUAL OBSERVATORIESVIRTUAL OBSERVATORIES
HS NETWORKING, HPC, I-GRID
Messerotti & Lundstedt (2004)