Virtual Observatories and Virtual Grids: The Interplay in Fully Exploiting Solar-Terrestrial Data

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


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


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


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


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


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


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


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/

http://cdsweb.u-strasbg.fr/astroweb.html

http://spds.nasa.gov/v

http://nssdc.gsfc.nasa.gov/spdf/

http://nssdc.gsfc.nasa.gov/spdf/yellow-pages/data-by-type.html

http://nssdc.gsfc.nasa.gov/

http://cadcwww.dao.nrc.ca/


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

http://cdsweb.u-strasbg.fr/astroweb/solar.html


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

http://cdsweb.u-strasbg.fr/astroweb/solar.html


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


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


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


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


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


THE ROLE OF VOs AND V-GRIDSTHE ROLE OF VOs AND V-GRIDS


http://solarnet.to.astro.it:8080/portal/http://solarnet.to.astro.it:8080/portal/


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


http://www.egso.org/http://www.egso.org/


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


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


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


GRID protocols to process dataGRID protocols to process data++

EMBEDDED KNOWLEDGEEMBEDDED KNOWLEDGE


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)


• 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


KNOWLEDGE EMBEDDINGKNOWLEDGE EMBEDDINGVIA CONCEPT MAPSVIA CONCEPT MAPS


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


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


Knowledge Representation SchemeKnowledge Representation Scheme

ConceptsConceptsConceptsConcepts RelationshipsRelationshipsRelationshipsRelationships

ConceptsConceptsConceptsConcepts RelationshipsRelationshipsRelationshipsRelationships

ConceptsConceptsConceptsConcepts RelationshipsRelationshipsRelationshipsRelationships PropositionPropositionAA

PropositionPropositionBB

PropositionPropositionCC


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


IHMC Concept Map Development ToolIHMC Concept Map Development Tool


A Sample CmapA Sample Cmap

2-D REPRESENTATION2-D REPRESENTATIONOFOF

A SET OF CONCEPTSA SET OF CONCEPTSANDAND

THEIR INTERRELATIONSHIPSTHEIR INTERRELATIONSHIPS


Cmap StructureCmap Structure

CONCEPTCONCEPT

CONCEPTCONCEPT

RELATIONRELATION

PropositionProposition

KNOWLEDGEKNOWLEDGE


CONCEPTCONCEPTFRAMEWORKFRAMEWORK

Most inclusiveMost inclusive

Least inclusiveLeast inclusive

HIE

RA

RC

HY

HIE

RA

RC

HY

GENERALIZATIONGENERALIZATION


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))


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


Definition of Space MeteorologyDefinition of Space Meteorology

M. Messerotti, 2005


Technological and Environmental EffectsTechnological and Environmental Effects

M. Messerotti, 2005


Solar DriversSolar Drivers

M. Messerotti, 2005


Review ofReview ofAvailable ModelsAvailable Modelsfor Solar Driversfor Solar Drivers

F. Zuccarello, 2005


Solar ActivitySolar Activity

M. Messerotti, 2005


VISIONS ABOUT THE IDEAL VOVISIONS ABOUT THE IDEAL VO

THE SEMANTIC VOTHE SEMANTIC VO

FORFOR

SUN-EARTH CONNECTIONSSUN-EARTH CONNECTIONS


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


ONTOLOGY & SEMANTICSONTOLOGY & SEMANTICS

CMAPS


Metadata Standards & Data ModelMetadata Standards & Data Model

DescribesDescribes• Multi-disciplinaryMulti-disciplinary• Multi-domainMulti-domain

– Multi-wavelengthMulti-wavelength– Multi-instrumentMulti-instrument

DataData

MULTI-DATAMETAMODEL


Knowledge Metadata & Knowledge ModelKnowledge Metadata & Knowledge Model

DescribesDescribes• ConceptsConcepts• InterrelationshipsInterrelationships• Case-Based ReasoningCase-Based Reasoning

KNOWLEDGEMETAMODEL


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


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


GRAPHICAL GRAPHICAL DISPLAYDISPLAY

of aof aMULTI-DOMAINMULTI-DOMAINDATA SEARCHDATA SEARCH

From IVOSEC prototyping


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)

Documents

Virtual Observatories and Virtual Grids: The Interplay in Fully Exploiting Solar-Terrestrial Data