Methodology of Mining Massive Data Sets for Improving Manufacturing Quality/Efficiency

  • Jye-Chyi Lu
Part of the Massive Computing book series (MACO, volume 3)


In this information era, many enterprises have begun exploring ways to utilize information stored in various databases for creating a competitive edge in managing their supply chain and networked manufacturing processes. This practice requires tools to automatically synthesize a large volume of data for getting needed knowledge. Although there are several existing data mining techniques, most of them are not effective in processing large amounts of data with possible nonstationary and dynamically changing trends. Our procedure first reduces the massive data sets into smaller size data by using data splitting and other data reduction techniques. Then, the traditionally used methods in data mining, signal/image processing and statistical analysis can be useful to handle the reduced-size data. Thus, decision rules for identifying and classifying process problems can be constructed based on these reduced-size data to improve manufacturing quality and efficiency. Finally, by using weighted averaging or voting procedures including artificial neural networks, the synthesized results obtained from the split-data can be integrated. Our real-life examples show a great potential of the proposed methods in mining knowledge from massive manufacturing data sets and in making significant impact in many fields including E-business operations.


Local Feature Discrete Wavelet Transform Wavelet Coefficient Multivariate Adaptive Regression Spline Wavelet Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Jye-Chyi Lu
    • 1
  1. 1.School of Industrial and Systems EngineeringGeorgia Institute of TechnologyAtlantaUSA

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