Abstract
The classical data stream problems are now greatly understood [1]. This talk will focus on problems with stochastic (rather than deterministic) streams where the underlying physical phenomenon generates probabilistic data or data distributions.
Chapter PDF
Similar content being viewed by others
References
Muthukrishnan, S.: Data Streams: Algorithms and Applications. In: Foundations and Trends in Theoretical Computer Science. NOW publishers (2005); Also Barbados Lectures (2009), http://www.cs.mcgill.ca/~denis/notes09.pdf
Jayram, T.S., McGregor, A., Muthukrishnan, S., Vee, E.: Estimating statistical aggregates on probabilistic data streams. ACM Trans. Database Syst. 33(4) (2008)
Cormode, G., Garofalakis, M.: Sketching probabilistic data streams. SIGMOD, 281–292 (2007)
Samuel-Cahn, E.: Comparisons of optimal stopping values and prophet inequalities for independent non-negative random variables. Ann. Prob. 12, 1213–1216 (1984)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Muthukrishnan, S. (2009). Stochastic Data Streams. In: Královič, R., Niwiński, D. (eds) Mathematical Foundations of Computer Science 2009. MFCS 2009. Lecture Notes in Computer Science, vol 5734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03816-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-03816-7_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03815-0
Online ISBN: 978-3-642-03816-7
eBook Packages: Computer ScienceComputer Science (R0)