Toward autonomic grids: Analyzing the job flow with affinity streaming

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2009, Pages 987-995

  Zhang, Xiangliang ^a   Furtlehner, Cyril ^a   Perez, Julien ^b   Germain Renaud, Cecile ^b   Sebag, Michèle ^c  

^a INRIA   (France)

^b UNIVERSITÉ PARIS SACLAY   (France)

^c CNRS   (France)

Author keywords

Afinity propagation; Autonomic computing; Online clus tering

Indexed keywords

AFINITY PROPAGATION; AUTONOMIC COMPUTING; CHANGE DETECTION; DATA ITEMS; DATA SETS; DATA STREAMING; NONSTATIONARY DATA; ONLINE CLUS- TERING; SYSTEM ADMINISTRATORS;

CLUSTERING ALGORITHMS; COMPUTER HARDWARE DESCRIPTION LANGUAGES; DATA REDUCTION; LARGE SCALE SYSTEMS;

COMPUTER SCIENCE;

EID: 71049172937     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1557019.1557126     Document Type: Conference Paper

References (25)

1. F. Bergeaud and S. Mallat. Matching pursuit of images. In ICIP, pages 53{56, 1995.
2. F. Cao, M. Ester, W. Qian, and A. Zhou. Density-based clustering over an evolving data stream with noise. In SIAM Conference on Data Mining (SDM), pages 326{337, 2006.
3. G. Cormode, S. Muthukrishnan, and W. Zhuang. Conquering the divide: Continuous clustering of distributed data streams. In ICDE, pages 1036{1045, 2007.
4. M. Ester. A density-based algorithm for discovering clusters in large spatial databases with noise: the uniqueness of a good optimum for k-means. In SIGKDD, pages 226{231, 1996.
5. U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. From data mining to knowledge discovery: An overview. In Advances in Knowledge Discovery and Data Mining, pages 1{34. MIT Press, 1996.
6. B. Frey and D. Dueck. Clustering by passing messages between data points. Science, 315:972{976, 2007.
7. J. Gama, R. Rocha, and P. Medas. Accurate decision trees for mining highspeed data streams. In SIGMOD, pages 523{528, 2003.
8. J. Gama and P. P. Rodrigues. Stream-based electricity load forecast. In PKDD, pages 446{453, 2007.
9. S. Guha, A. Meyerson, N. Mishra, R. Motwani, and L. O'Callaghan. Clustering data streams: Theory and practice. TKDE, 15:515{528, 2003.
10. Z. Harchaoui, F. Bach, and E. Moulines. Kernel change-point analysis. In NIPS, 2008.
11. D. Hinkley. Inference about the change-point from cumulative sum tests. Biometrika, 58:509{523, 1971.
12. D. Judd, P. K. McKinley, and A. K. Jain. Large-scale parallel data clustering. IEEE Trans. Pattern Anal. Mach. Intell., 20:871{876, 1998.
13. M. Leone, Sumedha, and M. Weigt. Clustering by soft-constraint afinity propagation: Applications to gene-expression data. Bioinformatics, 23:2708, 2007.
14. M. Meila. The uniqueness of a good optimum for k-means. In ICML, pages 625{632, 2006.
15. S. Muthukrishnan, E. v. d. Berg, and Y. Wu. Sequential change detection on data streams. In ICDM Workshops, 2007.
16. E. Page. Continuous inspection schemes. Biometrika, 41:100{115, 1954.
17. N. Palatin, A. Leizarowitz, A. Schuster, and R. Wolff. Mining for misconfigured machines in grid systems. In SIGKDD, pages 687{692, 2006.
18. C. E. Rasmussen and C. K. Williams. Gaussian Processes for Machine Learning. MIT Press, 01 2006.
19. I. Rish, M. Brodie, and S. M. et al. Adaptive diagnosis in distributed systems. IEEE Trans. on Neural Networks, 16:1088{1109, 2005.
20. G. Schwarz. Estimating the dimension of a model. The Annals of Statistics, 6:461{464, 1978.
21. J. Villemonteix, E. Vazquez, M. Sidorkiewicz and E. Walter. Global optimization of expensive-to-evaluate functions: an empirical comparison of two sampling criteria. Journal of Global Optimization, vol 43 (2-3), p.373-389, 2009
22. X. Zhang, C. Furtlehner, and M. Sebag. Data streaming with afinity propagation. In ECML/PKDD, pages 628{643, 2008.
23. J. Andreeva, B. Gaidioz, J. Herrala, and et al. Dashboard for the LHC experiments. Journal of Physics: Conference Series, vol. 119, 2008.
24. Real Time Monitor: http://gridportal.hep.ph.ic.ac.uk/rtm/.
25. X. Zhang, C. Furtlehner, and M. Sebag. INRIA research report in progress.