Title :
Extensible Markov model
Author :
Dunham, Margaret H. ; Meng, Yu ; Huang, Jie
Author_Institution :
Dept. of Comput. Sci. & Eng., Southern Methodist Univ., Dallas, TX, USA
Abstract :
A Markov chain is a popular data modeling tool. This paper presents a variation of Markov chain, namely extensible Markov model (EMM). By providing a dynamically adjustable structure, EMM overcomes the problems caused by the static nature of the traditional Markov chain. Therefore, EMMs are particularly well suited to model spatiotemporal data such as network traffic, environmental data, weather data, and automobile traffic. Performance studies using EMMs for spatiotemporal prediction problems show the advantages of this approach.
Keywords :
Markov processes; data models; Markov chain; data modeling tool; extensible Markov model; spatiotemporal data modeling; spatiotemporal prediction problems; Automobiles; Biomedical engineering; Cities and towns; Computer science; Power system modeling; Spatiotemporal phenomena; Telecommunication traffic; Traffic control; Training data; Weather forecasting;
Conference_Titel :
Data Mining, 2004. ICDM '04. Fourth IEEE International Conference on
Print_ISBN :
0-7695-2142-8
DOI :
10.1109/ICDM.2004.10067
