Analysis of e-science's aims, its applications, challenges and future.

1. Hector Quintero Casanova
University of Edinburgh
Distributed Scientific Computing in
Practice

2. Distributed Scientific Computing?

Also known as e-Science.

According to Dr. John Taylor, 2 dimensions:
– Global collaboration effort
• Cross-organisational effort demanded.
• Technical and formal differences are likely.
– Infrastructure that will enable it
• Middleware hides differences and complexities
• Aims at seamless instant access to resources
• Much like a utility. Hence, the grid.

3. Current state of affairs

Shift to data: find hypothesis for a pattern
– Cosmology: dark flow in WMAP data.
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Emphasis depends on area of application:
– Astronomy: uniform data access
• Data and its correct annotation. E.g: VO
– Particle Physics: universal job submission
• Processing of jobs. E.g: JDL
– Biology: workflow.
• Research activity model-based. E.g: Myexperiment

4. Current state of affairs

Differences in emphasis reflect on tools:
– Astronomy: analysis of data
• Multiple approaches ⇒ extensive user interaction.
– Biology: workflow design
• Decide order mainly ⇒ some user interaction.
– Particle Physics: job submission
• Define job and submit ⇒ minimal user interaction

Scientific research is also conducted in arts:
– E-science also applied to them
• E-Dance project: annotation of coreography videos

5. Challenges: semantics

Transition from annotation to semantics:
– Biology very advanced. E.g: Gene ontology
• Describe experimental models.
– In Astronomy not so easy despite rich meta-data
• Problems such as description of units.

Semantics leading to over-standardisation?
– Not yet since scientists still play a big role.
– Common model of knowledge could limit
creativity.
• Thinking processes shaped by common framework.
 Balance between standardisation & flexibility

6. Challenges: politics

Politics does affect scientific decisions:
– Astronomy: TAP protocol
• Compromise between US and UK.
• Each side implements the options it wants.
– In effect 2 flavours of TAP available:
• Organisations: which TAP to implement?
• Undermines standard access to data.
– Similar situation with CORBA ended in failure.

Solution: avoid compromises. Hold things up?
 Balance: standard's robustness vs. advancement

7. Challenges: collaboration

Focus still on sharing and not on collaborating:
– Astronomy: uniform access to data
• Data can be shared.
• No platform to exchange views on that data.
– Exception: myexperiment. Caters only biologists.

Also, targeted collaboration during development.
– Developers should actively engage with
scientists.
– Example: evolution of EDIKT project.

8. EDIKT

First, generic solutions that found applications
– Holistic approach to e-science problems.
• General solutions: BinX and Eldas.
• Specific applications: AstroBinX and BioDAS.

Change: active engagement with would-be users.
– Regular talks involving developers & researchers
– Embedded developer: specific to research
activity.

Example: ECDF portal.
– Draws on experience with RAPID.
– Fidelity to scientists reqs: command-line look.

9. Future

E-science just started: multi-disciplinary science
– New challenges cover wider areas of knowledge.
– Example: effects of climate change in migration
• Climate change complex already.
• Couple that with sociology and geography. Nice!

Will push for more standards and collaboration
– Semantics would ease establishment of
correlations.
– Example: social unrest and increase temperature.

E-science begging for funding? Hope not.

10. ¿?