Data R&D Issues for GTL Data R&D Issues for GTL Data and Knowledge Systems Data and Knowledge Systems San Diego Supercomputer Center San Diego Supercomputer Center University of California, San Diego University of California, San Diego Bertram Ludäscher Bertram Ludäscher [email protected][email protected]
9
Embed
Data R&D Issues for GTL Data and Knowledge Systems San Diego Supercomputer Center University of California, San Diego Bertram Ludäscher [email protected].
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Data R&D Issues for GTLData R&D Issues for GTL
Data and Knowledge SystemsData and Knowledge Systems
San Diego Supercomputer CenterSan Diego Supercomputer Center
University of California, San DiegoUniversity of California, San Diego
Data R&D Issues for GTLData R&D Issues for GTL GTL data management infrastructureGTL data management infrastructure Service-oriented Data GridsService-oriented Data Grids for for
Seamless data sharing (volume, distribution, access restrictions, …) Capabilities for data integration (mediators/warehouses), digital library functions, knowledge-based
(“semantic”) extensions (e.g. ontologies), and archival capabilities Data analysis and knowledge-enabling infrastructureData analysis and knowledge-enabling infrastructure
Analytical PipelinesAnalytical Pipelines (“ (“Scientific WorkflowsScientific Workflows”)”) Rapid design and prototyping, handling of complex data & task semantics, large volume, sci. workflow as
a first-class product, validation, execution, monitoring, sharing, archiving How to go from a scientist’s abstract (conceptual) workflow to a data grid execution plan?
New Model Management and Knowledge Representation Technologies New Model Management and Knowledge Representation Technologies :: Closing the gap between data management (DBMS’s, data grids) and knowledge-based systems (desktop-
oriented, rule-based systems) and analysis and modeling systems Mapping between numerous formalisms at the syntactic, structural, and semantic level (terminological,
process-semantics, …) “Gluing” together models and formalisms across different levels: from genes to proteins to molecular
Data exploration and hypothesis generation tools (KNOW-ME, SKIDL, SEEK AMS, …) Computational facilitiesComputational facilities
Use of high-end networked facilities a la Use of high-end networked facilities a la TeraGridTeraGrid Opportunities (and challenges!) in leveraging related efforts:Opportunities (and challenges!) in leveraging related efforts:
NIH BIRN, …, NSF Cyberinfrastructure (ITRs GEON, GriPhyN, SCEC, SEEK, …), UK e-Science, …NIH BIRN, …, NSF Cyberinfrastructure (ITRs GEON, GriPhyN, SCEC, SEEK, …), UK e-Science, … Standardization Standardization (OGSA, KR/Semantic Web technologies, e.g., ontology languages (OWL), inference (OGSA, KR/Semantic Web technologies, e.g., ontology languages (OWL), inference
mechanisms, …), scientific workflow standards, …mechanisms, …), scientific workflow standards, … interoperable, open source tools interoperable, open source tools One size/standards fits all? Probably not: data-intensive vs computation-intensive vs “semantics-intensive” One size/standards fits all? Probably not: data-intensive vs computation-intensive vs “semantics-intensive”