TY - GEN
T1 - Zero-one laws for sliding windows and universal sketches
AU - Braverman, Vladimir
AU - Ostrovsky, Rafail
AU - Roytman, Alan
N1 - Publisher Copyright:
© Vladimir Braverman, Rafail Ostrovsky, and Alan Roytman;licensed under Creative Commons License CC-BY.
PY - 2015/8/1
Y1 - 2015/8/1
N2 - Given a stream of data, a typical approach in streaming algorithms is to design a sophisticated algorithm with small memory that computes a specific statistic over the streaming data. Usually, if one wants to compute a different statistic after the stream is gone, it is impossible. But what if we want to compute a different statistic after the fact? In this paper, we consider the following fascinating possibility: can we collect some small amount of specific data during the stream that is "universal," i.e., where we do not know anything about the statistics we will want to later compute, other than the guarantee that had we known the statistic ahead of time, it would have been possible to do so with small memory? This is indeed what we introduce (and show) in this paper with matching upper and lower bounds: we show that it is possible to collect universal statistics of polylogarithmic size, and prove that these universal statistics allow us after the fact to compute all other statistics that are computable with similar amounts of memory. We show that this is indeed possible, both for the standard unbounded streaming model and the sliding window streaming model.
AB - Given a stream of data, a typical approach in streaming algorithms is to design a sophisticated algorithm with small memory that computes a specific statistic over the streaming data. Usually, if one wants to compute a different statistic after the stream is gone, it is impossible. But what if we want to compute a different statistic after the fact? In this paper, we consider the following fascinating possibility: can we collect some small amount of specific data during the stream that is "universal," i.e., where we do not know anything about the statistics we will want to later compute, other than the guarantee that had we known the statistic ahead of time, it would have been possible to do so with small memory? This is indeed what we introduce (and show) in this paper with matching upper and lower bounds: we show that it is possible to collect universal statistics of polylogarithmic size, and prove that these universal statistics allow us after the fact to compute all other statistics that are computable with similar amounts of memory. We show that this is indeed possible, both for the standard unbounded streaming model and the sliding window streaming model.
KW - Sliding windows
KW - Streaming algorithms
KW - Universality
UR - https://www.scopus.com/pages/publications/84958523372
U2 - 10.4230/LIPIcs.APPROX-RANDOM.2015.573
DO - 10.4230/LIPIcs.APPROX-RANDOM.2015.573
M3 - Conference contribution
AN - SCOPUS:84958523372
T3 - Leibniz International Proceedings in Informatics, LIPIcs
SP - 573
EP - 590
BT - Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques - 18th International Workshop, APPROX 2015, and 19th International Workshop, RANDOM 2015
A2 - Garg, Naveen
A2 - Jansen, Klaus
A2 - Rao, Anup
A2 - Rolim, Jose D. P.
PB - Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
T2 - 18th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2015, and 19th International Workshop on Randomization and Computation, RANDOM 2015
Y2 - 24 August 2015 through 26 August 2015
ER -