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Published byJordan Jimenez Modified over 11 years ago
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Scaling distributed search for diagnostics and prognostics applications Prof. Jim Austin Computer Science, University of York UK CEO Cybula Ltd.
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OGF 20072 Outline Challenge to be addressed Background to Signal Data Explorer The industry problem: Aerospace The science problem: Neuroscience Scaling to other industries
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The challenge
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OGF 20074 Challenge Asset or System Data Data Store Massive data collection happening everywhere Need to get knowledge from the data Knowledge
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OGF 20075 Challenge Europe China US Japan One Data Warehouse
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OGF 20076 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
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OGF 20077 Signal Data Explorer PMEPMC PMEPMC PMEPMC PMEPMC SDE data architecture Local nodes
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OGF 20078 Challenge Do not move the data - move the processing Thats much lower effort More reliability Scalable Networks need not be high bandwidth Leverages local computation for free (the Grid vision)
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The Signal Data Explorer
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OGF 200710 SDE ! Event detector Data silos Asset Data feed Basic SDE data process
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OGF 200711 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.
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OGF 200712 SDE Signal Search and analysis Finds examples like previous
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OGF 200713 Enterprise SDE system
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Industry Aerospace: DAME, BROADEN
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OGF 200715 Industry Industry demonstrator for the SDE technology Demonstrates the impact SDE can have on the business process Allows asset management – full life support processes
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OGF 200716 Rolls-Royce Engine flight data Airline office Maintenance Centre London Airport New York Airport GRID Diagnostics Centre Engine flight data European data centre Engine flight data Airline office Maintenance Centre London Airport New York Airport US data centre GRID Diagnostics Centre Engine flight data
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OGF 200717 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
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OGF 200718 DAME and BROADEN The example has shown how this can be applied in many industries.
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Science Neuroscience: CARMEN
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OGF 200720 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
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OGF 200721 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
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OGF 200722 CARMEN resolving the neural code from the timing of action potential activity determining ion channel contribution to the timing of action potentials examining integration within networks of differing dimensions Understanding the brain may be the greatest informatics challenge of the 21 st century
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OGF 200723 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
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Scaling to other sectors
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OGF 200725 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
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OGF 200726 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
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OGF 200727 Thanks DAME, BROADEN and CARMEN teams Support of EPSRC, DTI and industrial collaborators
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