Time Series Data Mining in Process Industry

Abstract:

time series; data mining; knowledge discovery; trend extremum representation. Abstract. In recent years, there has been an explosion of interest in mining time series databases. In this paper, we make some attempts to mining process industrial time series. As with most computer science problems, representation of the data is the key to efficient and effective solutions. We introduce a novel algorithm, Trend Extremum Representation, which is empirically proved to be superior to Piecewise Linear Representation and Important Points Representation in manipulating large-scale industrial data. Then, subsequent mining procedure is undertaken. Through clustering analysis and association rule discovery, several useful rules are derived for differentiating normal and abnormal events in everyday operations.

• Periodical: Advanced Materials Research (Volumes 532-533)
• Pages: 1069-1074
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.532-533.1069
• Online since: June 2012
• Authors: Jia Ren, Jin Feng Gao
• Keywords: Data Mining (DM), Knowledge Discovery, Time Series, Trend Extremum Representation

References

[1] G. Francesco, P. Giovanni, T. Andrea, and G. Sergio. Time series representation model for accurate and fast similarity detection. Pattern Recognition, vol. 42, no. 11, (2009), pp.2998-3014.

DOI: 10.1016/j.patcog.2009.03.030

[2] R. Agrawal, C. Faloutsos, A. Swami. Efficient similarity search in sequence databases. Proc. of the 4th Conference on Foundations of Data Organization and Algorithms, (1993), pp.69-84.

DOI: 10.1007/3-540-57301-1_5

[3] K. Chan, and W. Fu . Efficient time series matching by wavelets. Proc. 15th IEEE International Conference on Data Engineering, (1999), pp.126-133.

[4] E. Keogh, C. Selina, H. David, and P. Michael. An online algorithm for segmenting time series. Proc. IEEE International Conference on Data Mining, (2001), pp.289-296.

[5] H. Xiao, F.H. Shang. Representation of time series based on trend turning points. Science Technology and Engineering (in Chinese), vol. 10, no. 13, (2010), pp.3254-3257.

[6] B. P. Kevin. Search for patterns in compressed time series. International Journal of Image and Graphics, vol. 2, no. 1, (2002), pp.89-106.

[7] Z. Shi. Probabilistic model-based clustering of time series. Ph.D. Qualifying Proposal, Department of Electrical and Computer Engineering. The University of Texas at Austin, USA. (2002).

[8] R. Agrawal, P. Giuseppe, W. L. Edward, and M. Zait. Querying shapes of histories. Proc. 21st VLDB Conference, (1995), pp.502-514.

[9] R. Agrawal, K. Lin, S. S. Harpreet, S. Kyuseok. Fast similarity search in the presence of noise, scaling and translation in time-series databases. Proc. 21st VLDB Conference, (1995), pp.490-501.

[10] J. Zhang. Multi-variable statistical process control (in Chinese). Chemical Industry Press, (2000).

[11] R. Agrawal, and R. Srikant. Mining sequential patterns. Proc. Eleventh International Conference on Data Engineering, Taipei, Taiwan, (1995).