# Presentation: 504942

## Data R&D Issues for GTL

*Data and Knowledge Systems*
*San Diego Supercomputer Center*
*University of California, San Diego*

- Bertram Ludäscher
- ludaesch@sdsc.edu

## Data R&D Issues for GTL

- GTL data management infrastructure
***Service-oriented Data Grids*** 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 infrastructure
***Analytical Pipelines*** (“***Scientific 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 ***:
- 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 machines to microbial communities...(compare: pnp transistors, boolean circuits, assembly language, high-level PLs , declarative QLs, ... )  *abstraction & elaboration mechanisms*
-  *Data exploration and hypothesis generation tools* (KNOW-ME, SKIDL, SEEK AMS, ...)
- Computational facilities
- Use of high-end networked facilities a la ***TeraGrid***
- Opportunities (and challenges!) in  leveraging  related efforts:
- NIH BIRN, ..., NSF Cyberinfrastructure (ITRs GEON, GriPhyN, SCEC, SEEK, ...), UK e-Science, ...
***Standardization ***(OGSA, KR/Semantic Web technologies, e.g., ontology languages (OWL), inference mechanisms, ...), scientific workflow standards, ... interoperable, open source tools
- One size/standards fits all? Probably not:  data-intensive vs computation-intensive vs “semantics-intensive”  (capturing implicit domain knowledge, hidden assumptions, ...)

## Bonus Material (beyond 1 slide limit ;-) starts here ...

## Up & Down: Abstraction & Elaboration Mechanisms

*Knowledge*
*Mgmt*

*Information Mgmt*

*Data Management*

- How to punch through the technology barriers?
-  Data Grids
-  vs Digital Libraries
-  vs DBMS’s
-  vs Knowledge-Based Analysis & Modeling Systems

## Biomedical Informatics Research Network

## Getting Formal: Source Contextualization & Ontology Refinement in Logic

**Biomedical **
**Informatics**
**Research Network**
**http://nbirn.net**

- Getting Formal: Source Contextualization & Ontology Refinement in Logic

## Scientific Data Integration ... Questions to Queries ...

*What is the distribution and U/ Pb zircon ages of A-type plutons in VA? *
*How about their 3-D geometry ? *
*How does it relate to host rock structures?*

***Geologic Map***
***(Virginia)***

***GeoChemical***

***GeoPhysical***
***(gravity contours)***

***GeoChronologic***
***(Concordia)***

***Foliation Map***
***(structure DB)***

***“******Complex ***
***Multiple-Worlds”***
***Mediation***

**Notes:**
protloc = neurotrans (stimulate then electrical responses, probes)  = RDB, SENSELAB, Yale
CaBP (chemical structure, PDB links, function of CaBP, found-in...) = Web, Vanderbilt U
Expasy (Protein-info as Sequence data) = Web, Europe

## Geologic Map Integration: Geo & IT/CS meet

- +/-  a few hundred million years 

## Large collaborative NSF/ITR project: UNM, UCSB, UCSD (SDSC), UKansas,..
“Analysis & Modeling System” to design, execute, reproduce/refine scientific workflows in the ecology and biodiversity domains.

- “*Analysis & Modeling System*” to design, execute, reproduce/refine scientific workflows in the ecology and biodiversity domains.

- SEEK Project Overview