惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

D
DataBreaches.Net
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Google DeepMind News
Google DeepMind News
博客园 - 聂微东
Microsoft Azure Blog
Microsoft Azure Blog
V
Visual Studio Blog
IT之家
IT之家
博客园 - 【当耐特】
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
B
Blog
爱范儿
爱范儿
阮一峰的网络日志
阮一峰的网络日志
云风的 BLOG
云风的 BLOG
Vercel News
Vercel News
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
H
Hackread – Cybersecurity News, Data Breaches, AI and More
H
Help Net Security
J
Java Code Geeks
aimingoo的专栏
aimingoo的专栏
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
B
Blog RSS Feed
Blog — PlanetScale
Blog — PlanetScale
S
SegmentFault 最新的问题
Apple Machine Learning Research
Apple Machine Learning Research

cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
SF-sketch: A Two-stage Sketch for Data Streams
Tong Yang, Lingtong Liu, Yibo Yan, Muhammad Shahzad, Yulong Shen · 2017-01-16 · via cs.DS updates on arXiv.org

A sketch is a probabilistic data structure used to record frequencies of items in a multi-set. Sketches are widely used in various fields, especially those that involve processing and storing data streams. In streaming applications with high data rates, a sketch "fills up" very quickly. Thus, its contents are periodically transferred to the remote collector, which is responsible for answering queries. In this paper, we propose a new sketch, called Slim-Fat (SF) sketch, which has a significantly higher accuracy compared to prior art, a much smaller memory footprint, and at the same time achieves the same speed as the best prior sketch. The key idea behind our proposed SF-sketch is to maintain two separate sketches: a small sketch called Slim-subsketch and a large sketch called Fat-subsketch. The Slim-subsketch is periodically transferred to the remote collector for answering queries quickly and accurately. The Fat-subsketch, however, is not transferred to the remote collector because it is used only to assist the Slim-subsketch during the insertions and deletions and is not used to answer queries. We implemented and extensively evaluated SF-sketch along with several prior sketches and compared them side by side. Our experimental results show that SF-sketch outperforms the most widely used CM-sketch by up to 33.1 times in terms of accuracy. We have released the source codes of our proposed sketch as well as existing sketches at Github. The short version of this paper will appear in ICDE 2017.