Affiliation:
1. Carnegie Mellon University, United States
Abstract
One of the oldest problems in the data stream model is to approximate the
p
th moment
\(\Vert \mathbf {X}\Vert _p^p = \sum _{i=1}^n \mathbf {X}_i^p\)
of an underlying non-negative vector
\(\mathbf {X}\in \mathbb {R}^n\)
, which is presented as a sequence of
\(\mathrm{poly}(n)\)
updates to its coordinates. Of particular interest is when
\(p \in (0,2]\)
. Although a tight space bound of
\(\Theta (\epsilon ^{-2} \log n)\)
bits is known for this problem when both positive and negative updates are allowed, surprisingly, there is still a gap in the space complexity of this problem when all updates are positive. Specifically, the upper bound is
\(O(\epsilon ^{-2} \log n)\)
bits, while the lower bound is only
\(\Omega (\epsilon ^{-2} + \log n)\)
bits. Recently, an upper bound of
\(\tilde{O}(\epsilon ^{-2} + \log n)\)
bits was obtained under the assumption that the updates arrive in a
random order
.
We show that for
\(p \in (0, 1]\)
, the random order assumption is not needed. Namely, we give an upper bound for worst-case streams of
\(\tilde{O}(\epsilon ^{-2} + \log n)\)
bits for estimating
\(\Vert \mathbf {X}\Vert _p^p\)
. Our techniques also give new upper bounds for estimating the empirical entropy in a stream. However, we show that for
\(p \in (1,2]\)
, in the natural coordinator and blackboard distributed communication topologies, there is an
\(\tilde{O}(\epsilon ^{-2})\)
bit max-communication upper bound based on a randomized rounding scheme. Our protocols also give rise to protocols for heavy hitters and approximate matrix product. We generalize our results to arbitrary communication topologies
G
, obtaining an
\(\tilde{O}(\epsilon ^{2} \log d)\)
max-communication upper bound, where
d
is the diameter of
G
. Interestingly, our upper bound rules out natural communication complexity-based approaches for proving an
\(\Omega (\epsilon ^{-2} \log n)\)
bit lower bound for
\(p \in (1,2]\)
for streaming algorithms. In particular, any such lower bound must come from a topology with large diameter.
Publisher
Association for Computing Machinery (ACM)
Subject
Mathematics (miscellaneous)
Reference79 articles.
1. http://hadoop.apache.org/. (n. d.). Retrieved from http://hadoop.apache.org/.
2. https://spark.apache.org/. (n. d.). Retrieved from https://spark.apache.org/
3. Noga Alon, Yossi Matias, and Mario Szegedy. 1996. The space complexity of approximating the frequency moments. In Proceedings of the 28th Annual ACM Symposium on Theory of Computing. ACM, 20–29.
4. Alexandr Andoni, Robert Krauthgamer, and Krzysztof Onak. 2011. Streaming algorithms via precision sampling. In Proceedings of the IEEE 52nd Annual Symposium on Foundations of Computer Science. IEEE, 363–372.
5. Chrisil Arackaparambil, Joshua Brody, and Amit Chakrabarti. 2009. Functional monitoring without monotonicity. In Proceedings of the International Colloquium on Automata, Languages, and Programming. Springer, 95–106.
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1. A New Information Complexity Measure for Multi-pass Streaming with Applications;Proceedings of the 56th Annual ACM Symposium on Theory of Computing;2024-06-10