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e-Science:
Stuart Anderson
National e-Science Centre
Cool White Dwarves
Issues 1
• Astronomers are looking for:
– Many objects in globular clusters
– Very faint objects
– Interested in observations of many locations
• But:
– The observations are noisy:
• Artifacts created by the sensor technology, scanning
and digitizing.
• Junk in orbit, e.g. satellite tracks.
• Computer Science can help:
- Pattern recognition, computational learning, data
mining.
- But: Astronomers are more picky.
Cool Dwarves are faint and close
•The sky is full of faint objects.
• Cool White Dwarves are close.
• So they move about relative to
the background stars.
• The illustrated observations
cover a period of 30 years.
• We need to match up very faint
objects observed by different
equipment at different times.
Issues 2
• Astronomers have a model of how luminous
CWDs are that predicts how distant they
are and hence how they move over time.
• We can use computational learning (aka data
mining) to recognize CWDs provided we have
a model that allows tractable learning.
• We can use the model to create training
cases for various learning techniques.
• Astronomers also want to observe the same
objects at different wavelengths.
• Models of objects can be used as a basis for
data mining to link observations.
Problem Scale
• Cosmos (old technology), megabytes per
plate.
• Super Cosmos (current technology),
gigabytes per plate.
• Cosmos and Super Cosmos use 1m telescope
images
• Vista (new technology): imaging in visible and
x-ray using digital detectors, 4m telescope,
terabytes per night.
• Sky surveys look at large-scale structure of
space so many images are involved e.g. to
estimate the density of CWDs in the galaxy.
E-Science and Old Science
• Computational models have been used for
many years.
• e-Science systems will include vast
collections of observed data.
• Scientific models are the essential
organizing principle for data in such
systems.
• Currently we are hand-crafting models that
organise subsets of the data (e.g. CWDs).
• Can we create experimental environments
that allow scientists to create new models
of phenomena and test them against data?
Data, Information and Knowledge
• Much Grid work identifies a three-layer
architecture for data.
• Data is the raw data acquired from sensors
(e.g. telescopes, microscopes, particle
detectors).
• Information is created when we “clean up”
data to eliminate artifacts of the collection
process.
• Knowledge is information embedded within
an interpretive framework.
• Science provides strong interpretive
frameworks
Pattern: More science “in silico”
• Improved sensors, more sensors, huge
increase in data volume.
• Need to “clean”, “mine” structure data.
• Support complex models and large-scale
data collections inside the computer(s)
• Support for flexible model development and
using models to organise and access data.
• E.g. in databases, spatial organisation,
temporal organisation and support for
queries exploiting that structure – useful
for Geoscience?
Credits
• Cosmos, Super Cosmos and Vista are
projects looking at large scale structure of
the cosmos, based at the Royal Observatory
Edinburgh.
• Chris Williams, Bob Mann and Andy Lawrence
are working on using computational learning
to analyse super Cosmos data at RoE.
• Andy Lawrence is director of the AstroGrid
project that is a major UK contribution to
the international “Virtual Observatory” that
will federate the worlds major astronomical
data assets.
Whither Data Management?
• Scientific data is not particularly well
behaved.
• In particular, it does not fit the relational
model particularly well.
• We need new data models that are better
suited to the needs of science (and everyone
else too!).
• The model should attempt to support the
work of scientists effectively.
• Current data models are not particularly
useful.
Curated Databases
• Useful scientific databases are often curated :
they are created/ maintained with a great deal of
“manual” labour.
What really happens
DB2
DB1
Database people’s idea
of what happens
select xyz
from pqr
where abc
Inter-dependence is Complex
GERD
EpoDB
TRRD
BEAD
TransFac
GenBank
GAIA
Swissprot
A few of the 500 or so public curated
molecular biology databases
Issues in Curated Databases
• Data integration (always a problem). Need to
deal with schema evolution
• Data provenance. How do you track data
back to its source (this information is
typically lost)
• Data annotation. How should annotations
spread through this network?
• Archiving. How do you keep all the archives
when you are “publishing” a new database
every day?
Archiving
• Some recent results on efficient archiving
(Buneman, Khanna, Tajima, Tan)
• OMIM (On-line Mendelian Inheritance in
Man) is a widely used genetic database. A
new version is released daily.
• Bottom line, we can archive a year of
versions of OMIM with <15% more space
than the most recent version
A Sequence of Versions
“Pushing” time down
[Driscoll, Sarnak, Sleator, Tarjan: “Making Data Structures Persistent.” ]
The final result
(for the randomly
selected data)
Predicted expansion
for a year’s archive:
< 15%
Summary: technical issues
• Why and where:
– better characterization of where (new ideas
needed)
– negation/aggregation
• Keys:
– inference rules for relative keys
– foreign key constraints
– interaction between keys and DTDs/types
• Types for deterministic model (and other
models).
• Annotation
• Temporal QLs and archives
Pattern: Better support for work
• Data is increasingly complex and
interdependent.
• “Curating” the data is continuous, and
involves international effort to increase the
scientific value of the data.
• Understanding the way we work with data is
the key to providing adequate support for
that work.
• Deeper support for projects working across
the globe.
Credits
• These issues are being addressed by Peter
Buneman at Edinburgh.
• Peter has recently joined Informatics and
NeSC.
• He has worked for a number of years on
Digital Libraries and Biological Data
Management.