A deterministic algorithm for summarizing asynchronous streams over a sliding window

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

Volumn 4393 LNCS, Issue , 2007, Pages 465-476

  Busch, Costas ^a   Tirthapura, Srikanta ^b  

^a Rensselaer Polytechnic Institute   (United States)

^b Iowa State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; DATA REDUCTION; DATA STRUCTURES; RANDOM PROCESSES;

ASYNCHRONOUS DATA STREAMS; SLIDING TIMESTAMP WINDOW; SLIDING WINDOW;

ALGORITHMS;

EID: 38049169339     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-70918-3_40     Document Type: Conference Paper

References (12)

1. A. Arasu and G. Manku. Approximate counts and quantiles over sliding windows. In Proc. ACM Symposium on Principles of Database Systems (PODS), pages 286-296, 2004.
2. B. Babcock, M. Datar, R. Motwani, and L. O'Callaghan. Maintaining variance and k-medians over data stream windows. In Proc. 22nd ACM Symp. on Principles of Database Systems (PODS), pages 234-243, June 2003.
3. C. Busch and S. Tirthapura. A deterministic algorithm for summarizing asynchronous streams over a sliding window. Technical report, Iowa State University, 2006. Available at http://archives.ece.iastate.edu/view/year/2006. html.
4. G. Cormode, F. Korn, S. Muthukrishnan, and D. Srivastava. Space- and time-efficient deterministic algorithms for biased quantiles over data streams. In Proc. ACM Symposium on Principles of Database Systems, pages 263-272, 2006.
5. M. Datar, A. Gionis, P. Indyk, and R. Motwani. Maintaining stream statistics over sliding windows. SIAM Journal on Computing, 31(6):1794-1813, 2002.
6. J. Feigenbaum, S. Kannan, and J. Zhang. Computing diameter in the streaming and sliding-window models. Algorithmica, 41:25-41, 2005.
7. P. Gibbons and S. Tirthapura. Distributed streams algorithms for sliding windows. Theory of Computing Systems, 37:457-478, 2004.
8. S. Guha, D. Gunopulos, and N. Koudas. Correlating synchronous and asynchronous data streams. In Proc.9th ACM International Conference on Knowledge Discovery and Data Mining (KDD), pages 529-534, 2003.
9. A. Manjhi, V. Shkapenyuk, K. Dhamdhere, and C. Olston. Finding (recently) frequent items in distributed data streams. In Proc. IEEE International Conference on Data Engineering (ICDE), pages 767-778, 2005.
10. S. Muthukrishnan. Data Streams: Algorithms and Applications. Foundations and Trends in Theoretical Computer Science. Now Publishers, August 2005.
11. U. Srivastava and J. Widom. Flexible time management in data stream systems. In Proc. 23rd ACM Symposium on Principles of Database Systems (PODS), pages 263-274, 2004.
12. S. Tirthapura, B. Xu, and C. Busch. Sketching asynchronous streams over a sliding window. In Proc. 25th annual ACM symposium on Principles of distributed computing (PODC), pages 82-91, 2006.