Efficient time series data classification and compression in distributed monitoring

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4819 LNAI, Issue , 2007, Pages 389-400

  Di, Sheng ^a   Jin, Hai ^a   Li, Shengli ^a   Tie, Jing ^b   Chen, Ling ^a  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b THE UNIVERSITY OF CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA COMPRESSION; DATA STRUCTURES; DATA TRANSFER; TIME SERIES ANALYSIS; USER INTERFACES;

COMPRESSION ALGORITHMS; DISTRIBUTED MONITORING;

CLASSIFICATION (OF INFORMATION);

EID: 38549164083     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-77018-3_39     Document Type: Conference Paper

References (18)

1. Lee, D., Dongarra, J., Ramakrishna, R.: Visperf: Monitoring Tool for Grid Computing. In: Proceeding of ICCS, pp. 1-12 (2003)
2. Newman, H.B., Legrand, L.C., Galvez, P., Voicu, R., Cirstoiu, C.: MonALISA: A Distributed Monitoring Service Architecture. In: Proceedings of CHEP, La Jolla, CA, pp. 1-8 (2003)
3. Guangbo, N., Jie, M., Bo, L.: GridView: A dynamic and visual grid monitoring system. In: Proceedings of HPC Asia, pp. 89-92 (2004)
4. OutOfMemoryException and other pathological cases, http://haacked.com/ archiv e/2004/02/11/189.aspx
5. Foster, I., Kesselman, C., Tuecke, S.: The Anatomy of the Grid: Enabling Scalable Virtual Organizations. International J. Supercomputer Applications 15(3) (2001)
6. Zheng, W., Liu, L., Hu, M., Wu, Y., Li, L., He, F., Tie, J.: CGSV: An Adaptable Stream-Integrated Grid Monitoring System. In: Jin, H., Reed, D., Jiang, W. (eds.) NPC 2005. LNCS, vol. 3779, pp. 22-31. Springer, Heidelberg (2005)
7. 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)
8. 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)
9. 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)
10. 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)
11. 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)
12. 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)
13. Keogh, E.J., Pazzani, M.J.: Scaling up Dynamic Time Warping 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)
14. 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)
15. CGSV Manual, http://www.chinagrid.edu.cn/CGSV/doc/CGSV-Manual/en/html/ book.html
16. Schroeder, M.: Fractals, Chaos, Power Laws: Minutes From an Infinite Paradise. W.H. Freeman and Company, New York (1991)
17. 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)
18. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction To Algorithms, 2nd edn., pp. 127-128. MIT Press, Cambridge