Sketches for size of join estimation

ACM Transactions on Database Systems

Volumn 33, Issue 3, 2008, Pages

  Rusu, Florin ^a   Dobra, Alin ^a  

^a UNIVERSITY OF FLORIDA   (United States)

Author keywords

AGMS sketches; Count Min sketches; DMAP; Fast range summation; Fast AGMS sketches; Fast Count sketches; Size of join estimation

Indexed keywords

AGMS SKETCHES; COUNT-MIN SKETCHES; DMAP; FAST RANGE-SUMMATION; FAST-AGMS SKETCHES; FAST-COUNT SKETCHES; SIZE OF JOIN ESTIMATION;

STATISTICS;

METHOD OF MOMENTS;

EID: 51149086160     PISSN: 03625915     EISSN: 15574644     Source Type: Journal    
DOI: 10.1145/1386118.1386121     Document Type: Article

References (28)

1. ADURI, P. AND TIRTHAPURA, S. 2005. Range efficient computation of F 0 over massive data streams. In Proceedings of the 21st IEEE ICDE International Conference on Data Engineering. IEEE Computer Society, 32-43.
2. ALON, N., GIBBONS, P. B., MATIAS, Y., AND SZEGEDY, M. 2002. Tracking join and self-join sizes in limited storage. J. Comput. Syst. Sci. 64, 3, 719-747.
3. ALON, N., MATIAS, Y., AND SZEGEDY, M. 1996. The space complexity of approximating the frequency moments. In Proceedings of the 28th ACM Symposium on Theory of Computing. ACM, New York, 20-29.
4. AN, N., YANG, Z.-Y., AND SIVASUBRAMANIAM, A. 2001. Selectivity estimation for spatial joins. In Proceedings of the 17th IEEE ICDE International Conference on Data Engineering. IEEE Computer Society, 368-375.
5. BALANDA, K. P. AND MACGILLIVRAY, H. L. 1988. Kurtosis: A critical review. J. Amer. Statist. 42, 2, 111-119.
6. CHARIKAR, M., CHEN, K., AND FARACH-COLTON, M. 2002. Finding frequent items in data streams. In Proceedings of the 29th International Colloquium on Automata, Languages and Programming. Springer, 693-703.
7. COLES, S. 2001. An Introduction to Statistical Modeling of Extreme Values. Springer, Berlin, Germany.
8. CORMODE, G. AND GAROFALAKIS, M. 2005. Sketching streams through the net: distributed approximate query tracking. In Proceedings of the 31st International Conference on Very Large Data Bases. VLDB Endowment, 13-24.
9. CORMODE, G. AND MUTHUKRISHNAN, S. 2005a. An improved data stream summary: the count-min sketch and its applications. J. Algor. 55, 1, 58-75.
10. CORMODE, G. AND MUTHUKRISHNAN, S. 2005b. Summarizing and mining skewed data streams. In Proceedings of SIAM Conference on Data Mining. SIAM, Philadelphia, PA.
11. CPS. 2006. http://www.census.gov/cps.
12. DAS, A., GEHRKE, J., AND RIEDEWALD, M. 2004. Approximation techniques for spatial data. In Proceedings of the 23rd ACM SIGMOD International Conference on Management of Data (SIGMOD'O4). ACM, New York, 695-706.
13. DOBRA, A., GAROFALAKIS, M., GEHRKE, J., AND RASTOGI, R. 2002. Processing complex aggregate queries over data streams. In Proceedings of the 21st ACM SIGMOD International Conference on Management of Data (SIGMOD'02). ACM, New York, 61-72.
14. ESTAN, C. AND NAUGHTON, J. F. 2006. End-biased samples for join cardinality estimation. In Proceedings of the 22nd IEEE International Conference on Data Engineering. IEEE Computer Society, 20-31.
15. GANGULY, S., G AROFALAKIS, M., AND RASTOGI, R. 2004. Processing data-stream join aggregates using skimmed sketches. In Proceedings of the 9th International Conference on Extending Database Technology. Springer, 569-586.
16. GANGULY, S., KESH, D., AND SAHA, C. 2005. Practical algorithms for tracking database join sizes. In Proceedings of the 25th Conference on Foundations of Software Technology and Theoretical Computer Science. Springer, 297-309.
17. GILBERT, A. C., KOTIDIS, Y., MUTHUKRISHNAN, S., AND STRAUSS, M. J. 2005. Domain-driven data synopses for dynamic quantiles. IEEE Transactions on Knowl. Data Engin. 17, 7, 927-938.
18. HAAS, P. J. AND HELLERSTEIN, J. M. 1999. Ripple joins for online aggregation. In Proceedings of the eighteenth ACM SIGMOD International Conference on Management of Data (SIGMOD'99). ACM Press, New York, 287-298.
19. KEMPE, D., DOBRA, A., AND GEHRKE, J. 2003. Gossip-based computation of aggregate information. In Proceedings of the 44th IEEE Symposium on Foundations of Computer Science. IEEE Computer Society, 482-491.
20. MASSDAL. 2006. http://www.cs.rutgers.edu/_muthu/massdal.html.
21. MOTWANI, R. AND RAGHAVAN, P. 1995. Randomized Algorithms. Cambridge University Press, Cambridge, UK.
22. OLIVE, D. J. 2005. A simple confidence interval for the median. Manuscript. www.math.siu.edu/olive/ppmedci.pdf.
23. p estimator. Metrologia 43, 3, 213-219.
24. PRICE, R. M. AND BONETT, D. G. 2001. Estimating the variance of the sample median. J. Statist. Comput. Simul. 68, 3, 295-305.
25. RUSU, F. AND DOBRA, A. 2007. Pseudorandom number generation for sketch-based estimations. ACM Trans. Datab. Syst. 32, 2, 11.
26. SACHS, L. 1984. Applied Statistics - A Handbook of Techniques. Springer, Berlin, Germany.
27. SHAO, J. 1999. Mathematical Statistics. Springer, Berlin, Germany.
28. THORUP, M. AND ZHANG, Y. 2004. Tabulation based 4-universal hashing with applicationstosecond moment estimation. In Proceedings of the 15th ACM-SIAM Symposium on Discrete Algorithms. 615-624.