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1
Data-IntensiveResearch: Actions
to make better use of Data for
Research
Malcolm Atkinson (& David De Roure)
2 February 2010
Reportto
EPSRC Research Facilities SAT, London
Mission goal: learn how researchers use data
Acknowledgements: the UK e-Science Directors CIR authors, our teams, the EPSRC (Sarah Fulford), the RCUK, USA office (Ruth Lee) and all of our hosts in the USA,
their good ideas; opinions, observations and recommendations are our own.
Mission
• How will research be conducted in the future?• Discussion and insight from the experts at the
cutting edge– What are the changes in
method and practice?– What works and what
doesn’t work?• Share our e-Science experiences and do the
groundwork for future collaborations• BBSRC and ESRC missions too
Senseable Cities
Andrey Rzhetsky: hand-crafted datascopes
Institute for Data-Intensive Engineering & Science
2D X-Ray Detector SystemsEuropean X-Ray Laser XFEL @ DESY
•≥ 106 pixels per frame for one detector•O(400-500) frames per train (goal, likely will start with less)•10 trains per second (machine allows up to 30 Hz…)•With 2 Byte/pixel average rate ≥10 Gbyte/sec for one 2D detector!•Time between frames as short as 200ns buffering needed•5 Detectors
600 μs
99.4 ms
100 ms 100 ms
200 ns
LPD
Text
Year Rate Capability[Gbyte/sec]
Storage Space[Petabyte]
2009 1 3
2012 5 26
2016 40 200
Datascopes Summary
• Better methods for extracting information from data• better algorithms for
discovery, selection, fusion, distillation, aggregation, presentation
algorithms transformed to run incrementally
• Better strategies for using the algorithms• Better data/metadata and semantics• Better platforms supporting the strategies• Data centres hosting data and computation
• Coping with more complexity, more users & more questions
• Knowledge, questions & datascopes co-evolve
Rally cr
oss-d
isciplin
ary
effortRally cr
oss-d
isciplin
ary
effort
Ramps: Summary
• An easy path to use a data analysis method• An opportunity• Not an obligation• Engage as far as you want
• Use a service for routine tasks• Types of ramp
• in browser - now powerful - can reach the GPU• in familiar tools• support from centres and crowd-sourced
• Strongly linked with education• Removes distracting technical clutter• Rescues educators & students• Ramp & education co-evolve
Boost investm
ent
hereBoost investm
ent
here
Actions
1. Workshops on DIR
2. DIR education3. Sand-pit to
inject initial momentum
4. Test best practice
5. Immediate research challenges
6. DIR facilities pool7. Boost reference
data services8. Foundational
research9. Green DIR10.Coordination
http://tinyurl.com/ye8x4bw
Phase 1
1. Workshops on DIR
2. DIR education3. Sand-pit to
inject initial momentum
4. Test best practice
5. Immediate research challenges
In Edinburgh 15-19 March 2010
http://tinyurl.com/ye8x4bw
http://www.nesc.ac.uk/esi/events/1047/
Phase 2
6. DIR facilities pool7. Boost reference
data services8. Foundational
research9. Green DIR10.Coordination
S/W, H/W & support: what services do researchers need? How much? How soon?
More softw
are; Less
hardwareMore so
ftware; L
ess
hardware
More bandwidth; Fewer
FLOPSMore bandwidth; F
ewer
FLOPS
Summary
• Much research is data intensive
• More of it will be
• Exploiting the opportunity is urgent for the UK (you/your org.)
• This requires changes• In facility provision
• In research investment
• In research behaviour (incentives)
• In education
• These changes are part of the digital revolution• Understand, engage and ride the wave (co-evolution)
• Investing in data-intensive research• Will accelerate research
• Deliver more applicable research
• Provide a better return on investment
24
ADMIRE – Framework 7 ICT 215024
?
Picture compositionbyLuke Humphrybased on prior art by Frans Hals
www.omii.ac.uk
www.admire-project.eu
www.ogsadai.org.uk
www.nesc.ac.uk