1. Jim Austin University of York & Cybula Ltd
Data Mining with AURA
Jim Austin
University of York
&
Cybula Ltd

2. Overview AURA AURA within UK e-Science GRID issues in DM
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
22 Oct 2001

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
22 Oct 2001

4. AURA as a basis for search
The game is to remove the chaff using AURA.
Later processes find the exact match.
22 Oct 2001

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
22 Oct 2001

6. weights ( ) M P
Inputs
Threshold, T R
22 Oct 2001

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!).
22 Oct 2001

8. Use of the CMM CMM system Query Data subset Data Slow algorithm
Final data
22 Oct 2001

9. CMM system Pre-process Operations Prepare data Post process CMM system
22 Oct 2001

10. 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
22 Oct 2001

11. Post processing Data selected by the CMM must be accessed quickly.
Uses ‘best bit index’ method to match output data and recover stored data.
22 Oct 2001

12. Implementation The AURA C++ library Implemented on PC or workstation
Beowulf parallel cluster
Origin 2000 supercomputer
Bespoke hardware
22 Oct 2001

13. AURA parallel implementation 28 dedicated PCI based processors
Cortex-1
AURA parallel implementation
28 dedicated PCI based processors
Beowulf configuration
3.5Gb memory size
22 Oct 2001

14. 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.
22 Oct 2001

15. 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
22 Oct 2001

16. University of Sheffield, P Fleming.
Rolls-Royce
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.
22 Oct 2001

17. Grid Diagnostics centre Engine flight data London Airport
Airline office
New York Airport
Grid
Diagnostics centre
Maintenance Centre
American data center
European data center
22 Oct 2001

18. Diagnostic issues The system must analyse and report Data
Novel engine operation
Identify any cause of events
Do this quickly
Data
Large (many Tb)
22 Oct 2001

19. Data – Zmod plots
22 Oct 2001

20. 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.
22 Oct 2001

21. 22 Oct 2001 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
22 Oct 2001

22. Simple example of processing chain Matching previous events
CMM
Data sample
DM coding
Simple example of processing chain
Matching
previous events
22 Oct 2001

23. Typical pre-processing
Frequency
DM coding
(1 up and 0 down)
Fast
Preserves information
Produces a binary vector
Time
22 Oct 2001

24. AURA-G This is a Globus enabled AURA implementation.
Developed under DAME
Will be available end of 2002 for use in other problems.
22 Oct 2001

25. AURA-G Support of scalable pattern matching
Supports distributed search, across multiple CMM engines at different sites
OGSA compliant
22 Oct 2001

26. Grid Issues in Data Mining
Data provenance
Standards:
Data transparency independent of location
Managing DB/Data mining link in distributed system
OGSA DAI
22 Oct 2001

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
22 Oct 2001

28. Dept Computer Science, University of York, York, YO1O 5DD.
Contacts
Jim Austin
Dept Computer Science, University of York, York, YO1O 5DD.
Cybula Ltd.
DAME :
22 Oct 2001