Many applications require access to distributed data assets. The DAME project has investigated one such example based upon condition monitoring of civil aero-engine sensor data. A service-based solution is introduced that has been implemented within the Globus Grid framework. It provides a general architecture for distributed search and identifies the generic functionality that is required.

We provide an overview of the DAME project, and a discussion of the progress made to date on the development of a distributed aeroengine diagnosis environment as a proof of concept demonstration for grid computing. We discuss the development of a demonstration diagnosis workbench system for this complex, data intensive, diagnosis application that must be operated as a distributed 'virtual organisation'. We describe the core diagnosis applications that have been implemented as grid services, and explain how these services are being deployed within the overall diagnosis process

The “Grid” provides the ability to bring together diverse tools and data into a single virtual environment, increasing the availability to a user of distributed data that requires pattern matching and/or searching algorithms performed against the full dataset. A service-based solution is evaluated that provides a generic architecture for distributed search. The ability of the system to scale to data distributed across many locations is analysed and it is compared with proprietary solutions to estimate the performance overhead of the Grid protocols.

The use of search engines within the Internet is now ubiquitous. This work examines how Grid technology may affect the implementation of search engines by focusing on the Signal Data Explorer application developed within the Distributed Aircraft Maintenance Environment (DAME) project. This application utilizes advanced neural-network-based methods (Advanced Uncertain Reasoning Architecture (AURA) technology) to search for matching patterns in time-series vibration data originating from Rolls-Royce aeroengines (jet engines). The large volume of data associated with the problem required the development of a distributed search engine, where data is held at a number of geographically disparate locations. This work gives a brief overview of the DAME project, the pattern marching problem, and the architecture. It also describes the Signal Data Explorer application and provides an overview of the underlying search engine technology and its use in the aeroengine health-monitoring domain.

Aero-engine vibration and performance data is downloaded each time an aircraft lands. On a fleet wide basis this process soon generates terabyte scale datasets. Given the large volume of data and the rate at which it is produced, there is a need to consider how the data is managed. In addition, there is a requirement for pattern matching against this engine data. The DAME project has investigated the use of Grid technology to support a fully distributed storage and pattern matching architecture. This paper describes how this technology can be used to solve the data management problem.

The availability of high frequency data sets in finance has allowed the use of very data intensive techniques using large data sets in forecasting. An algorithm requiring fast k-NN type search has been implemented using AURA, a binary neural network based upon Correlation Matrix Memories. This work has also constructed probability distribution forecasts, the volume of data allowing this to be done in a nonparametric manner. In assistance to standard statistical error measures the implementation of simulations has allowed actual measures of profit to be calculated.

This paper describes research on the DAME Project into the use of Correlation Matrix Memories for imprecise pattern matching on large volumes of time series data using the Grid. This technology, named AURA has been developed over a period of 15 years at the University of York. In the DAME project, we have begun to apply AURA technology to the problem of searching aero-engine vibration data with the aim of finding partial matches to vibration anomalies and thus providing information that can be used to form an early prognosis / diagnosis of potential faults. Within the DAME project, the underlying AURA technology has been improved in terms of its speed and memory usage and adapted for use as a Grid service under Globus Toolkit 3. At the mid-point in the project (July 2003), research is ongoing into encoding techniques suited to fast processing of time series data. In the second half of the project, we intend to investigate the use of advanced data mining methods in combination with a distributed grid enabled implementation of AURA to support flexible and very high performance pattern matching over large amounts of data.