1. Data R&D Issues for GTL Bertram Ludäscher Data and Knowledge Systems
Data and Knowledge Systems
San Diego Supercomputer Center
University of California, San Diego

2. 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, …)
Data R&D Issues for GTL

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

4. Up & Down: Abstraction & Elaboration Mechanisms
How to punch through the technology barriers?
Data Grids
vs Digital Libraries
vs DBMS’s
vs Knowledge-Based Analysis & Modeling Systems
Knowledge
Mgmt
Information Mgmt
Data Management

5. Biomedical Informatics Research Network

6. Getting Formal: Source Contextualization & Ontology Refinement in Logic
Biomedical
Informatics
Research Network

7. Scientific Data Integration ... Questions to Queries ...
GeoSciences Network
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?
domain knowledge
Knowledge Representation:
ontologies, concept spaces
“Complex
Multiple-Worlds”
Mediation ?
Information
Integration
Database mediation
Data modeling
raw data
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
(Virginia)
GeoPhysical
(gravity contours)
GeoChronologic
(Concordia)
Foliation Map
(structure DB)
GeoChemical

8. Geologic Map Integration: Geo & IT/CS meet
domain
knowledge
Knowledge representation
AGE ONTOLOGY
Nevada
+/- a few hundred million years 
Geoscientists + Computer Scientists Igneous Geoinformaticists
+/- Energy
GEON Metamorphism Equation:

9. Analytical Pipeline (AP)
   
SEEK Project Overview
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.
ASx
ASy
ASz
TS1
TS2
Semantic Mediation
Engine
Data Binding
Query Processing
j¬y
j¬ a
ECO2
Logic Rules ECO2-CL
Analytical Pipeline (AP)
SMS: Semantic Mediation System
EcoGrid
provides unified access to Distributed Data Stores , Parameter Ontologies, & Stored Analyses, and runtime capabilities via the Execution Environment
Semantic Mediation System & Analysis and Modeling System use EcoGrid web services, enabling analytically driven data discovery and integration
SEEK is the combination of EcoGrid data resources and information services, coupled with advanced semantic and modeling capabilities
AM: Analysis and Modeling System
ASr
Parameters w/ Semantics C
Parameter Ontologies
WSDL
SRB
KNB MC
Species
Wrp
Dar
...
Raw data sets wrapped for integration w/ EML, etc.
TaxOn
EML
etc.
Execution Environment
SAS, MATLAB, FORTRAN, etc
Library of Analysis Steps, Pipelines
& Results
Invasive species over time W S D L
Example of “AP0”
AP0