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\title{Temporal Data Mining 
with a Case Study of Astronomical Data Analysis\thanks{The 
research is supported by The Cooperative Research Centre for 
Advanced Computational Systems (ACSys) in Australia.}}
\author{Michael K. Ng\thanks{
CRC for Advanced Computational Systems, Computer Sciences Laboratory, 
The Australian National University, Canberra, ACT 0200, Australia.
E-mail: mng@cslab.anu.edu.au}
\and Zhexue Huang\thanks{CRC for Advanced Computational Systems, 
CSIRO Mathematical and Information Sciences, GPO Box
664, Canberra, ACT 2601, Australia.}} 
\date{}
\begin{document}
\maketitle

Many real world databases possess time components. Examples range from
scientific databases to business databases. Temporal data mining is a
technique to deal with problems of knowledge discovery from the
large temporal databases. In this paper we present a case study of
discovering microlensing events from an extremely large astronomical
database consisting of 40 million time series (20 million stars with
two series each). We discuss the integrated use of techniques from
signal processing, pattern recognition, machine learning, statistics
and high performance computing to solve this large temporal database
mining problem. We take the star light curve classification as a
special focus to highlight some temporal data mining issues. We also
discuss some computing problems encountered in our work. 

\end{document}


