Transcript DAME - National e

Data Mining with AURA
Jim Austin
University of York
&
Cybula Ltd
Overview
• AURA
• Background to AURA
• Brief overview of its components
• Its implementation
• AURA within UK e-Science
• What is e-Science
• The DAME pilot project
• Use of AURA in DAME
• GRID issues in DM
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The AURA Technology
• Neural network based associative storage
• Set of tools to build fast pattern recognition
systems
• Aimed at unstructured data
• Aimed at large datasets
• Scaleable technology
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AURA as a basis for search
• The game is to remove the chaff using
AURA.
• Later processes find the exact match.
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The storage system
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Correlation Matrix Memory based
Exploits threshold logic methods
Uses distributed encoding of information
Implemented using binary ‘weights’ for
efficient software and hardware
implementation
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weights (
P
)
M
Inputs
Threshold, T
R
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Why is it fast?
• Access only rows that are activated by
inputs.
• Inputs are made as sparse as possible and
fixed weight.
• Only need to sum over active rows (bit
vectors) – ideal for most processors
• Great for bit vector machines (DAP!).
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Use of the CMM
Query
CMM system
Data
Data subset
Slow algorithm
Final data
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CMM system
Pre-process
Operations
Prepare data
CMM system
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Post process
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Pre-processing
• Implements a number of pre-processors
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N-grams for text strings
CMAC for numeric data
Graphs for images and graphics
Tokens for logical data
Quantisation for time series
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Post processing
• Data selected by the CMM must be
accessed quickly.
• Uses ‘best bit index’ method to match
output data and recover stored data.
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Implementation
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The AURA C++ library
Implemented on PC or workstation
Beowulf parallel cluster
Origin 2000 supercomputer
Bespoke hardware
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Cortex-1
AURA parallel implementation
28 dedicated PCI based processors
Beowulf configuration
3.5Gb memory size
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UK eScience
• Aims to build on the concept of Grids
– To make computing and data provision as direct
and simple as electrical power delivery
• £110M initiative started 18 months ago
• DAME is a £3.5M pilot project to
demonstrate its application in the
engineering field.
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DAME Objectives
• DAME: Distributed Aircraft Maintenance
Environment.
• Demonstrate diagnostic capability on the
GRID
• Examine timeliness properties of the GRID
• Demonstrate on the RR Aeroengine
diagnostic problem
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University of Sheffield, P Fleming.
University of Leeds, Peter Dew, Alison McKay.
York, J Austin, J McDermid, A Wellings.
University of Oxford, Lionel Tarassenko.
Rolls-Royce
Rolls-Royce, Derby.
Data Systems & Solutions.
Cybula Ltd.
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Engine flight data
London Airport
Airline
office
New York Airport
Grid
Diagnostics centre
Maintenance Centre
American data center
European data center
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Diagnostic issues
• The system must analyse and report
– Novel engine operation
– Identify any cause of events
– Do this quickly
• Data
– Large (many Tb)
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Data – Zmod plots
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How does AURA contribute
• Search technology for multi-media data
• Parallel pattern match engine based on
neural networks.
• Built on Correlation Matrix Memories.
• High performance Beowulf and dedicated
hardware implementations.
• Commercially sold by Cybula Ltd.
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Diagnostic
station
Engine data
Novelty
indication
Quote
Data used to
identify
novelty
Data reduction
processes
Match requests
Features
Data to be
searched for
Data stores/
data
warehouse
Pattern match
results
Diagnosis
AURA-G
GRID
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Data sample
DM coding
Simple example of processing chain
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CMM
Matching
previous events
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Frequency
Typical pre-processing
01101111011110111
DM coding
(1 up and 0 down)
Fast
Preserves information
Produces a binary vector
Time
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AURA-G
• This is a Globus enabled AURA
implementation.
• Developed under DAME
• Will be available end of 2002 for use in
other problems.
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AURA-G
• Support of scalable pattern matching
• Supports distributed search, across multiple
CMM engines at different sites
• OGSA compliant
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Grid Issues in Data Mining
• Data provenance
• Standards:
– Data transparency independent of location
– Managing DB/Data mining link in distributed
system
– OGSA DAI
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Conclusions
• AURA is a mature component for data
search and retrieval
• Robust software and hardware
implementation available
• Applications in e-Science for Grid
applications underway
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Contacts
Jim Austin
Dept Computer Science, University
of York, York, YO1O 5DD.
www.cs.york.ac.uk/arch
[email protected]
01904 432734
01904 432767
Cybula Ltd.
www.cybula.com
01377 236382
DAME : www.cs.york.ac.uk/dame
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