Towards Optimal Moment Estimation in Streaming and Distributed Models

Author:

Jayaram Rajesh1ORCID,Woodruff David P.1ORCID

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.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3