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Knowledge discovery in temporal databases

Saraee, M and Theodoulidis , B 1995, Knowledge discovery in temporal databases , in: IEE Colloquium on Knowledge Discovery in Databases, February 1995, London, UK.

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    Abstract

    Knowledge discovery in databases is the process of applying statistical, machine learning and other techniques to conventional database systems. Our survey in knowledge discovery systems has indicated that up to date there is no knowledge discovery system to deal with temporal databases. In this paper, we first give a brief description of temporal database systems and then we present some examples to show how the ORES temporal database management system could provide the necessary functionality to infer accurate and valuable knowledge from temporal databases. In particular, we discuss three common classes of database mining problems involving classifications, associations and sequences. We give a short description of our overall framework for knowledge discovery under research. The work focuses on two areas and their integration: on one side, data mining as a technique to increase the quality of data, and on the other side, temporal databases as a technique to keep the history of data. We believe that their integration will lead to even higher quality data.

    Item Type: Conference or Workshop Item (Paper)
    Themes: Media, Digital Technology and the Creative Economy
    Schools: Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > Data Mining and Pattern Recognition Research Centre
    Journal or Publication Title: Proceedings of IEE Colloquium on Knowledge Discovery in Databases
    Publisher: IEE
    Refereed: Yes
    Depositing User: Dr Mo Saraee
    Date Deposited: 26 Oct 2011 15:06
    Last Modified: 20 Aug 2013 18:16
    URI: http://usir.salford.ac.uk/id/eprint/18695

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