On biased reservoir sampling in the presence of stream evolution

VLDB 2006 - Proceedings of the 32nd International Conference on Very Large Data Bases

Volumn , Issue , 2006, Pages 607-618

  Aggarwal, Cham C ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING PROBLEMS; DATA STREAM ALGORITHMS; EFFECTIVE SOLUTION; EFFICIENT IMPLEMENTATION; EVOLUTION ANALYSIS; RESERVOIR SAMPLINGS; SAMPLING ALGORITHM; SAMPLING PROCESS;

ALGORITHMS; DATA COMMUNICATION SYSTEMS; DATA MINING;

DIGITAL STORAGE;

EID: 84893850265     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. C. Aggarwal, J. Han, J. Wang, and P. Yu. A Framework for High Dimensional Projected Clustering of Data Streams. VLDB Conference Proceedings, pp. 852-863, 2004.
2. C. Aggarwal, and P. Yu. A Survey of Synopsis Construction Methods in Data Streams. Data Streams: Models and Algorithms, Springer, ed. C, Aggarwal, to appear 2007.
3. B. Babcock, M. Datar, and Ft Motwani. Sampling from a Moving Window over Streaming Data. SODA Conference Proceedings, pp. 633-634, 2002.
4. S. Babu, and J. Widom. Continuous queries over data streams. ACM SIGMOD Record Archives, 30(3): 109-120, 2001.
5. E. Cohen, and M. Strauss. Maintaining Time-Decaying Stream Aggregates. ACM PODS Conference Proceedings, pp. 223-233, 2003.
6. M. Datar, A. Gionis, P. Indyk, and R. Motwani. Maintaining Stream Statistics over Sliding Windows. SODA Conference Proceedings, pp. 635-644, 2002.
7. P. Gibbons, and Y. Mattias. New Sampling-Based Summary Statistics for Improving Approximate Query Answers. ACM SIGMOD Conference Proceedings, pp. 331-342, 1998.
8. P. Gibbons. Distinct sampling for highly accurate answers to distinct value queries and event reports. VLDB Conference Proceedings, pp. 541-550, 2001.
9. A. Gilbert, Y. Kotidis, S. Muthukrishnan, and M. Strauss. One-Pass Wavelet Decompositions of Data Streams. IEEE Transactions on Knowledge and Data Engineering, 15(3):541-554, May 2003.
10. P. Karras, and N. Mamoulis. One-pass wavelet synopsis for maximum error metrics. VLDB Conference Proceedings, pp. 421-432, 2005.
11. Y. Matias, J. Vitter, and M. Wang. Wavelet-Based Histograms for Selectivity Estimation. ACM SIGMOD Conference Proceedings, pp. 448-459, 1998.
12. J. Hellerstein, P. Haas, and H. Wang. Online Aggregation. ACM SIGMOD Conference Proceedings, pp. 171-182, 1997.
13. G. Manku, S. Rajagopalan, and B. Lindsay. Random Sampling for Space Efficient Computation of order statistics in large datasets. ACM SIGMOD Conference Proceedings, pp. 251-262, 1999.
14. G. Manku, and R. Motwani. Approximate Frequency Counts over Data Streams. VLDB Conference Proceedings, pp. 346-357, 2002.
15. N. Thaper, S. Guha, P. Indyk, and N. Koudas. Dynamic multidimensional histograms. ACM SIGMOD Conference Proceedings, pp. 428-439, 2002.
16. J. S. Vitter. Random Sampling with a Reservoir. ACM Transactions on Mathematical Software, Vol. 11(1):37-57, March 1985.
17. Y. Wu, D. Agrawal, and A. Abbadi. Applying the Golden Rule of Sampling for Query Estimation. ACM SIGMOD Conference Proceedings pp. 449-460, 2001.