Data Mining with AURA

1
Data Mining with AURA

• Jim Austin
• University of York
• Cybula Ltd

2
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

3
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

4
AURA as a basis for search

• The game is to remove the chaff using AURA.
• Later processes find the exact match.

5
The storage system

• Correlation Matrix Memory based
• Exploits threshold logic methods
• Uses distributed encoding of information
• Implemented using binary weights for efficient
  software and hardware implementation

6
weights (
)
M
P
Inputs
Threshold, T
R
7
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!).

8
Use of the CMM
CMM system
Query
Data subset
Data
Slow algorithm
Final data
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CMM system
Pre-process Operations Prepare data
Post process
CMM system
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Pre-processing

• Implements a number of pre-processors
• 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.

12
Implementation

• The AURA C library
• Implemented on PC or workstation
• Beowulf parallel cluster
• Origin 2000 supercomputer
• Bespoke hardware

13
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, 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
Pattern match results
Data stores/ data warehouse
Diagnosis
AURA-G
GRID
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CMM
Data sample
DM coding
Simple example of processing chain
Matching previous events
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Typical pre-processing
01101111011110111
Frequency
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

27
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

28
Contacts
Jim Austin Dept Computer Science, University of
York, York, YO1O 5DD. www.cs.york.ac.uk/arch austi
n_at_cs.york.ac.uk 01904 432734 01904 432767
Cybula Ltd. www.cybula.com 01377 236382
DAME www.cs.york.ac.uk/dame