Efficient event detection by exploiting crowds

DEBS 2013 - Proceedings of the 7th ACM International Conference on Distributed Event-Based Systems

Volumn , Issue , 2013, Pages 123-134

  Boutsis, Ioannis ^a   Kalogeraki, Vana ^a   Gunopulos, Dimitrios ^b  

^a ATHENS UNIVERSITY OF ECONOMICS AND BUSINESS   (Greece)

^b UNIVERSITY OF ATHENS   (Greece)

Author keywords

Clustering; Community based participatory sensing; Distributed systems; Event detection; Mobile systems; Sampling

Indexed keywords

CLUSTERING; COMMUNITY-BASED PARTICIPATORY SENSING; DISTRIBUTED SYSTEMS; EVENT DETECTION; MOBILE SYSTEMS;

SAMPLING;

SOFTWARE ARCHITECTURE;

EID: 84881125308     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2488222.2488264     Document Type: Conference Paper

References (38)

1. C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu. A framework for clustering evolving data streams. In VLDB, Berlin, Germany, Sep 2003.
2. M. Al-Kateb, B. S. Lee, and X. S. Wang. Adaptive-size reservoir sampling over data streams. In SSDBM, Banff, Canada, July 2007.
3. M. H. Ali, M. F. Mokbel, and W. G. Aref. Phenomenon-aware stream query processing. In MDM, pages 8-15, Mannheim, Germany, May 2007.
4. M. Ankerst, M. M. Breunig, H.-P. Kriegel, and J. Sander. Optics: ordering points to identify the clustering structure. In SIGMOD, Philadelphia, PA, June 1999.
5. B. Babcock, M. Datar, and R. Motwani. Sampling from a moving window over streaming data. In SODA, San Francisco, CA, January 2002.
6. M. Benkert, J. Gudmundsson, F. Hübner, and T. Wolle. Reporting flock patterns. Computational Geometry, 41(3): 111-125, 2008.
7. I. Boutsis and V. Kalogeraki. Radar: Adaptive rate allocation in distributed stream processing systems under bursty workloads. In SRDS, Irvine, CA, October 2012.
8. F. Cao, M. Ester, W. Qian, and A. Zhou. Density-based clustering over an evolving data stream with noise. In SIAM, Bethesda, MD, April 2006.
9. Y. Chen and L. Tu. Density-based clustering for real-time stream data. In KDD, San Jose, CA, Aug 2007.
10. W. G. Cochran. Sampling Techniques, 3rd Edition. John Wiley, 1977.
11. N. Cressie. Statistics for spatial data. Wiley & Sons, 1993.
12. A. Dou, V. Kalogeraki, D. Gunopulos, T. Mielikinen, V. Tuulos, S. Foley, and C. Yu. Data clustering on a network of mobile smartphones. In SAINT, Munich, Germany, July 2011.
13. R. K. Ganti, N. Pham, H. Ahmadi, S. Nangia, and T. F. Abdelzaher. Greengps: a participatory sensing fuel-efficient maps application. In MobiSys, San Francisco, California, USA, June 2010.
14. S. Guha, R. Rastogi, and K. Shim. Cure: an efficient clustering algorithm for large databases. In SIGMOD, pages 73-84, Seattle, Washington, USA, June 1998.
15. M. Halkidi, V. Kalogeraki, D. Gunopulos, D. Papadopoulos, D. Zeinalipour-Yazti, and M. Vlachos. Efficient online state tracking using sensor networks. In MDM, Nara, Japan, May 2006.
16. J. A. Hartigan and M. A. Wong. Algorithm AS 136: A K-Means Clustering Algorithm. Applied Statistics, 28(1): 100-108, 1979.
17. B. Hull, V. Bychkovsky, Y. Zhang, K. Chen, M. Goraczko, A. Miu, E. Shih, H. Balakrishnan, and S. Madden. Cartel: a distributed mobile sensor computing system. In SenSys, pages 125-138, Boulder, Colorado, USA, Oct-Nov 2006.
18. C. S. Jensen, D. Lin, and B. C. Ooi. Continuous clustering of moving objects. IEEE Trans. on Knowl. and Data Eng., 19(9): 1161-1174, September 2007.
19. S. Johnson. Hierarchical clustering schemes. Psychometrika, 32: 241-254, 1967.
20. P. Kalnis, N. Mamoulis, and S. Bakiras. On discovering moving clusters in spatio-temporal data. In Advances in spatial and temporal databases, pages 364-381. Springer, 2005.
21. M. Keally, G. Zhou, and G. Xing. Watchdog: Confident event detection in heterogeneous sensor networks. In RTAS, pages 279-288, Stockholm, Sweden, April 2010.
22. G. Kollios, D. Gunopulos, N. Koudas, and S. Berchtold. Efficient biased sampling for approximate clustering and outlier detection in large datasets. IEEE Trans. on Knowl. and Data Eng., 2003.
23. Z. Li, B. Ding, J. Han, and R. Kays. Swarm: mining relaxed temporal moving object clusters. Proc. VLDB Endow., 3(1-2):723-734, September 2010.
24. S. Lin, B. Arai, D. Gunopulos, and G. Das. Region sampling: Continuous adaptive sampling on sensor networks. In ICDE, Canćun, México, April 2008.
25. R. V. Nehme and E. A. Rundensteiner. Scuba: scalable cluster-based algorithm for evaluating continuous spatio-temporal queries on moving objects. In EDBT, Munich, Germany, March 2006.
26. C. S. d. Oliveira, P. I. Godinho, A. S. G. Meiguins, B. S. Meiguins, and A. A. Freitas. Edacluster: an evolutionary density and grid-based clustering algorithm. In ISDA, Rio de Janeiro, Brazil, Oct 2007.
27. M. Olson, A. H. Liu, M. Faulkner, and K. M. Chandy. Rapid detection of rare geospatial events: earthquake warning applications. In DEBS, New York, NY, July 2011.
28. C. R. Palmer and C. Faloutsos. Density biased sampling: an improved method for data mining and clustering. In SIGMOD, Dallas, TX, May 2000.
29. H.-S. Park and C.-H. Jun. A simple and fast algorithm for k-medoids clustering. Expert Syst. Appl., 36(2): 3336-3341, Mar. 2009.
30. PlanetLab Consortium. http://www.planet-lab.org, 2004.
31. T. Repantis, X. Gu, and V. Kalogeraki. Synergy: Sharing-aware component composition for distributed stream processing systems. In Middleware, Melbourne, Australia, Nov. 2006.
32. J. Sander, M. Ester, H.-P. Kriegel, and X. Xu. Density-based clustering in spatial databases: The algorithm gdbscan and its applications. Data Min. Knowl. Discov., 2(2): 169-194, June 1998.
33. B. Silverman. Density Estimation for Statistics and Data Analysis. Monographs on Statistics and Applied Probability. Chapman & Hall, 1986.
34. S. Subramaniam, V. Kalogeraki, and T. Palpanas. Distributed real-time detection and tracking of homogeneous regions in sensor networks. In RTSS, Rio de Janeiro, Brazil, Dec 2006.
35. A. Thiagarajan, L. Ravindranath, K. LaCurts, S. Madden, H. Balakrishnan, S. Toledo, and J. Eriksson. Vtrack: accurate, energy-aware road traffic delay estimation using mobile phones. In SenSys, Berkeley, CA, Nov 2009.
36. M. Wand and C. Jones. Kernel Smoothing. Monographs on Statistics & Applied Probability. Chapman & Hall, 1995.
37. J. Yuan, Y. Zheng, X. Xie, and G. Sun. Driving with knowledge from the physical world. In KDD, San Diego, California, USA, August 2011.
38. J. Yuan, Y. Zheng, C. Zhang, W. Xie, X. Xie, G. Sun, and Y. Huang. T-drive: driving directions based on taxi trajectories. In SIGSPATIAL GIS, San Jose, CA, November 2010.