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Scaling distributed search for diagnostics and prognostics applications

Scaling distributed search for diagnostics and prognostics applications. Prof. Jim Austin Computer Science, University of York UK CEO Cybula Ltd. Outline. Challenge to be addressed Background to Signal Data Explorer The industry problem: Aerospace The science problem: Neuroscience

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Scaling distributed search for diagnostics and prognostics applications

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  1. Scaling distributed search for diagnostics and prognostics applications Prof. Jim Austin Computer Science, University of York UK CEO Cybula Ltd.

  2. Outline • Challenge to be addressed • Background to Signal Data Explorer • The industry problem: Aerospace • The science problem: Neuroscience • Scaling to other industries OGF 2007

  3. The challenge

  4. Challenge Data Asset or System Data Store Knowledge Massive data collection happening everywhere Need to get knowledge from the data OGF 2007

  5. Challenge Europe One Data Warehouse China US Japan OGF 2007

  6. Challenge • Data too large for a single data store • Data changes too fast to keep up to date • Networks too slow to transfer the data • The system has a central point of failure • Large processing needed on centralised store OGF 2007

  7. Signal Data Explorer Local nodes PME PMC PME PMC PME PMC PME PMC SDE data architecture OGF 2007

  8. Challenge • Do not move the data - move the processing • That’s much lower effort • More reliability • Scalable • Networks need not be high bandwidth • Leverages local computation for free (the Grid vision) OGF 2007

  9. The Signal Data Explorer

  10. SDE Data feed Asset Data silos ! Event detector Basic SDE data process OGF 2007

  11. SDE • Allows a user • Set up triggers on multiple, complex, real time data feeds. • Find examples of events that are seen but unknown. • Manage distributed data. OGF 2007

  12. SDE • Signal Search and analysis • Finds examples like previous OGF 2007

  13. Portal Security management Workflow Manager Collaborative Working Novel Data Middleware Services & Data Management Data PMC & SRB Resource Broker Grid Service Manager Quality of Service/SLA’s Download Grid Infrastructure GT3 e e CBR a a n n t i i t Signal Engine Data a g a g AURA-G n Decision n D D Processing e E E Model Visualisation c a Support i t v a r e e D a n t i S g a n D E A A R T A U D A Distributed Data Repositories Enterprise SDE system OGF 2007

  14. IndustryAerospace: DAME, BROADEN

  15. Industry • Industry demonstrator for the SDE technology • Demonstrates the impact SDE can have on the business process • Allows asset management – full life support processes OGF 2007

  16. Engine flight data Engine flight data Engine flight data Engine flight data London Airport London Airport New York Airport New York Airport Airline office Airline office Diagnostics Diagnostics GRID GRID Centre Centre Maintenance Maintenance Centre Centre US data centre European data centre Rolls-Royce OGF 2007

  17. DAME and BROADEN • DAME developed basic idea – developed in lab (2000-2004) • BROADEN now has demonstrated this in Rolls-Royce (2004-2007) • Engineers can now use SDE to visualise and analyse data from Trent engines and test rigs OGF 2007

  18. DAME and BROADEN • The example has shown how this can be applied in many industries. OGF 2007

  19. ScienceNeuroscience: CARMEN

  20. Science • Data is often not shared from experiments • Individuals duplicate many expensive experiments • Should share both data and methods • Distributed data and service repositories needed to support this OGF 2007

  21. CARMEN • Tackles the scientific application of SDE • 4 year project to build a neuroscience repository and experimental platform • Project started Oct 2006 • £4.5M over 4 years OGF 2007

  22. Understanding the brain may be the greatest informatics challenge of the 21st century  determining ion channel contribution to the timing of action potentials  resolving the ‘neural code’ from the timing of action potential activity  examining integration within networks of differing dimensions CARMEN OGF 2007

  23. Neuroscience Neuroscience Gain Recording from brain tissue removed from epileptic patients (scarce tissue and data rates up to 20 Gb/h) On line analysis by distributed collaborators will enable experiment to be defined Repository will enable integration of rare case types from different laboratories New knowledge will lead to advances in treatment OGF 2007

  24. Scaling to other sectors

  25. Other sectors • Concept is to provide full asset monitoring • Provision of the complete maintenance package, not just the asset • Better value add • Manufacturers of the asset are the best people to diagnose faults, manage the maintenance. • The SDE system allows this OGF 2007

  26. Other sectors • Rail industry • Track and carriage monitoring • Oil and Gas • Pipeline monitoring – for leaks • Power Generation • Monitoring of generation equipment • Transport • Road traffic management OGF 2007

  27. Thanks • DAME, BROADEN and CARMEN teams • Support of EPSRC, DTI and industrial collaborators OGF 2007

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