Abstract
As a key issue in distributed monitoring, time series data are a series of values collected in terms of sequential time stamps. Requesting them is one of the most frequent requests in a distributed monitoring system. However, the large scale of these data users request may not only cause heavy loads to the clients, but also cost long transmission time. In order to solve the problem, we design an efficient two-step method: first classify various sets of time series according to their sizes, and then compress the time series with relatively large size by appropriate compression algorithms. This two-step approach is able to reduce the users’ response time after requesting the monitoring data, and the compression effects of the algorithms designed are satisfactory.
This paper is supported by National Science Foundation of China under grant 90412010, ChinaGrid project from Ministry of Education, and CNGI projects under grant CNGI-04-15-7A.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Lee, D., Dongarra, J., Ramakrishna, R.: Visperf: Monitoring Tool for Grid Computing. In: Proceeding of ICCS, pp. 1–12 (2003)
Newman, H.B., Legrand, I.C., Galvez, P., Voicu, R., Cirstoiu, C.: MonALISA: A Distributed Monitoring Service Architecture. In: Proceedings of CHEP, La Jolla, CA, pp. 1–8 (2003)
Guangbo, N., Jie, M., Bo, L.: GridView: A dynamic and visual grid monitoring system. In: Proceedings of HPC Asia, pp. 89–92 (2004)
OutOfMemoryException and other pathological cases, http://haacked.com/archive/2004/02/11/189.aspx
Foster, I., Kesselman, C., Tuecke, S.: The Anatomy of the Grid: Enabling Scalable Virtual Organizations. International J. Supercomputer Applications 15(3) (2001)
Zheng, W., Liu, L., Hu, M., Wu, Y., Li, L., He, F., Tie, J.: CGSV: An Adaptable Stream-Integrated Grid Monitoring System. In: **, H., Reed, D., Jiang, W. (eds.) NPC 2005. LNCS, vol. 3779, pp. 22–31. Springer, Heidelberg (2005)
Wu, D., Angrawal, D., Abbadi, A.E., Singh, A., Smith, T.R.: Efficient Retrieval for Browsing Large Image Databases. In: Proceedings of 5th International Conference on Knowledge Information, pp. 11–18 (1996)
Agrawal, R., Faloutsos, C., Swami, A.: Efficient Similarity Search in Sequence Databases. In: Proceedings of the 4th Conference on Foundations of Data Organization and Algorithms, pp. 69–84 (1993)
Chan, K., Fu, W.: Efficient Time Series Matching by Wavelets. In: Proceedings of the 15th IEEE International Conference on Data Engineering, pp. 126–133 (1999)
Agrawal, R., Lin, K.I., Sawhney, H.S., Shim, K.: Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Times-series Databases. In: Proceedings of 21th International Conference on Very Large Data Bases, pp. 490–500 (1995)
Das, G., Lin, K., Mannila, H., Renganathan, G., Smyth, P.: Rule Discovery from Time Series. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, pp. 16–22 (1998)
Perng, C., Wang, H., Zhang, S., Parker, S.: Landmarks: a New Model for Similarity based Pattern Querying in Time Series Databases. In: Proceedings of 16th International Conference on Data Engineering, pp. 33–42 (2000)
Keogh, E.J., Pazzani, M.J.: Scaling up Dynamic Time War** to Massive Datasets. In: Żytkow, J.M., Rauch, J. (eds.) Principles of Data Mining and Knowledge Discovery. LNCS (LNAI), vol. 1704, pp. 1–11. Springer, Heidelberg (1999)
Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 151–162 (2001)
CGSV Manual, http://www.chinagrid.edu.cn/CGSV/doc/CGSV-Manual/en/html/book.html
Schroeder, M.: Fractals, Chaos, Power Laws: Minutes From an Infinite Paradise. W.H. Freeman and Company, New York (1991)
Bechmann, N., Kriegel, H.P., Schneider, R., Seeger, B.: The R*-tree: An Efficient and Robust Access Method for Points and Rectangles. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 322–331 (1990)
Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction To Algorithms, 2nd edn., pp. 127–128. MIT Press, Cambridge
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Di, S., **, H., Li, S., Tie, J., Chen, L. (2007). Efficient Time Series Data Classification and Compression in Distributed Monitoring. In: Washio, T., et al. Emerging Technologies in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77018-3_39
Download citation
DOI: https://doi.org/10.1007/978-3-540-77018-3_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77016-9
Online ISBN: 978-3-540-77018-3
eBook Packages: Computer ScienceComputer Science (R0)