Dimensionality Reduction of Data Warehouse using Wavelet Transformation: An Enhanced Approach for Business Process
Abstract
Dimensionality reduction is an essential data preprocessing technique used for reducing the number of random variables under consideration. Traditional dimensionality reduction approaches fall under two categories one is Feature Extraction and Feature Selection. Many data warehouse contain massive amount of data, accumulated over long period of time. This is done in order to maintain the privacy concerns in which the data are aggregated to a certain levels. Another reason is to maintain a balance between the use of data that changes as data age and the size of data, thus avoiding over large data warehouse. In this paper an enhanced approach for business process has been carried out using dimensionality reduction by implementing wavelet transformation, which will help us in automated selection of most relevant independent attributes in a data warehouse.
Recommended Citation
A. S. Tripathy et al., "Dimensionality Reduction of Data Warehouse using Wavelet Transformation: An Enhanced Approach for Business Process," Communications in Computer and Information Science, vol. 142 CCIS, pp. 523 - 525, Springer-Verlag, Mar 2011.
The definitive version is available at https://doi.org/10.1007/978-3-642-19542-6_101
Meeting Name
2nd International Conference on Advances in Communication, Network, and Computing, CNC 2011 (2011: Feb. 23-24, Bangalore, India)
Department(s)
Computer Science
Keywords and Phrases
Data Warehouse; Dimensionality Reduction; Wavelet Transformation
International Standard Book Number (ISBN)
978-364219541-9
International Standard Serial Number (ISSN)
1865-0929
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2011 Association of Computer Electronics and Electrical Engineers (ACEEE), All rights reserved.
Publication Date
29 Mar 2011
