Liang, B. and Austin, J. (2005) A neural network for mining large volumes of time series data. In: IEEE International Conference on Industrial Technology (ICIT) 2005 (14-17 December 2005, City University of Hong Kong). IEEE , New York , pp. 688-693. ISBN 0-7803-9484-4Full text available as:
Efficiently mining large volumes of time series data is amongst the most challenging problems that are fundamental in many fields such as industrial process monitoring, medical data analysis and business forecasting. This paper discusses a high-performance neural network for mining large time series data set and some practical issues on time series data mining. Examples of how this technology is used to search the engine data within a major UK eScience Grid project (DAME) for supporting the maintenance of Rolls-Royce aero-engine are presented.
|Item Type:||Book Section|
|Copyright, Publisher and Additional Information:||© 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Academic Units:||The University of York > Computer Science (York)|
|Depositing User:||Repository Assistant|
|Date Deposited:||25 Aug 2006|
|Last Modified:||17 Oct 2013 15:17|