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检索条件"主题词=Streaming Algorithm"

共 111 条记录，以下是81-90 订阅

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Maximization of Monotone Non-submodular Functions with a Knapsack Constraint over the Integer Lattice 15th

Maximization of Monotone Non-submodular Functions with a Kna...

引用

15th Annual International Conference on Combinatorial Optimization and Applications (COCOA)

作者： Tan, Jingjing Wang, Fengmin Zhang, Xiaoqing Zhou, Yang Weifang Univ Sch Math & Informat Sci Weifang 261061 Peoples R China Beijing Jinghang Res Inst Comp & Commun Beijing 100074 Peoples R China Beijing Univ Technol Dept Operat Res & Informat Engn Beijing 100124 Peoples R China Shandong Normal Univ Sch Math & Stat Jinan 250014 Peoples R China

ISBN: (纸本)9783030926816;9783030926809

The problem of submodular maximization on the integer lattice has attracted more and more attention due to its deeply applications in many areas. In this paper, we consider maximizing a non-negative monotone non-submodular function with knapsack constraint on massive data. We combine two proposed algorithms called streamingKnapsack and BinarySearch for this problem by introducing the DR ratio gamma(d) and the weak DR ratio gamma(w) of the non-submodular objective function. Finally, we obtain the performance guarantee of the streamingKnapsack supplemented by a simple one-pass algorithm, with the approximation ratio of the better output of them as min{gamma(2)(d)(1 - epsilon)/2(gamma d+1), 1 - 1/gamma(w)2(gamma d) - epsilon}. Meanwhile, both the time complexity and space complexity are dependent on the size of knapsack capacity K and epsilon is an element of (0, 1).

关键词： Integer lattice Non-submodular Knapsack constraint streaming algorithm

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Coresets for Minimum Enclosing Balls over Sliding Windows 19

Coresets for Minimum Enclosing Balls over Sliding Windows

引用

25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD)

作者： Wang, Yanhao Li, Yuchen Tan, Kian-Lee Natl Univ Singapore Singapore Singapore Singapore Management Univ Singapore Singapore

ISBN: (纸本)9781450362016

Coresets are important tools to generate concise summaries of massive datasets for approximate analysis. A coreset is a small subset of points extracted from the original point set such that certain geometric properties are preserved with provable guarantees. This paper investigates the problem of maintaining a coreset to preserve the minimum enclosing ball (MEB) for a sliding window of points that are continuously updated in a data stream. Although the problem has been extensively studied in batch and append-only streaming settings, no efficient sliding-window solution is available yet. In this work, we first introduce an algorithm, called AOMEB, to build a coreset for MEB in an append-only stream. AOMEB improves the practical performance of the state-of-the-art algorithm while having the same approximation ratio. Furthermore, using AOMEB as a building block, we propose two novel algorithms, namely SWMEB and SWMEB+, to maintain coresets for MEB over the sliding window with constant approximation ratios. The proposed algorithms also support coresets for MEB in a reproducing kernel Hilbert space (RKHS). Finally, extensive experiments on real-world and synthetic datasets demonstrate that SWMEB and SWMEB+ achieve speedups of up to four orders of magnitude over the state-of-the-art batch algorithm while providing coresets for MEB with rather small errors compared to the optimal ones.

关键词： coresets minimum enclosing ball streaming algorithm

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A Randomized algorithm for Finding Frequent Elements in Streams Using O(log log N) Space 1

引用

22nd International Symposium on algorithms and Computation (ISAAC)

作者： Ogata, Masatora Yamauchi, Yukiko Kijima, Shuji Yamashita, Masafumi Kyushu Univ Grad Sch Informat Sci & Elect Engn Dept Informat Fukuoka 8190395 Japan

ISBN: (数字)9783642255915

ISBN: (纸本)9783642255908

Finding frequent items in a data stream is a fundamental problem;Given a threshold theta is an element of (0, 1), find items appearing more than theta. N times in an input stream with length N. Karp, Shenker, Papadimiriou (2003) gave a simple deterministic online algorithm, which allows false positive outputs using memory of O(theta(-1) log N) bits, while they also gave a lower bound. Motivated by the theoretical bound of the space complexity, this paper proposes a simple randomized online algorithm using memory of O(theta(-2) log(2) theta(-1) + log log N) bits where parameters for approximation are hidden in the constant. Our algorithm is robust for memory overflow, compared with other naive randomized algorithms, or deterministic algorithms using memory of O(log N) bits. We also give some randomized algorithms for approximate counting.

关键词： streaming algorithm space complexity

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Optimality of Linear Sketching Under Modular Updates 34

Optimality of Linear Sketching Under Modular Updates

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34th Computational Complexity Conference (CCC)

作者： Hosseini, Kaave Lovett, Shachar Yaroslavtsev, Grigory Univ Calif San Diego San Diego CA 92103 USA Indiana Univ Bloomington IN USA

ISBN: (纸本)9783959771160

We study the relation between streaming algorithms and linear sketching algorithms, in the context of binary updates. We show that for inputs in n dimensions, the existence of efficient streaming algorithms which can process Omega(n(2)) updates implies efficient linear sketching algorithms with comparable cost. This improves upon the previous work of Li, Nguyen and Woodruff [23] and Ai, Hu, Li and Woodruff [3] which required a triple-exponential number of updates to achieve a similar result for updates over integers. We extend our results to updates modulo p for integers p >= 2, and to approximation instead of exact computation.

关键词： communication complexity linear sketching streaming algorithm

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Robust Factorization of Real-world Tensor Streams with Patterns, Missing Values, and Outliers 37

Robust Factorization of Real-world Tensor Streams with Patte...

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37th IEEE International Conference on Data Engineering (IEEE ICDE)

作者： Lee, Dongjin Shin, Kijung Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Sudan Korea Adv Inst Sci & Technol Grad Sch AI Daejeon South Sudan

ISBN: (纸本)9781728191843

Consider multiple seasonal time series being collected in real-time, in the form of a tensor stream. Real-world tensor streams often include missing entries (e.g., due to network disconnection) and at the same time unexpected outliers (e.g., due to system errors). Given such a real-world tensor stream, how can we estimate missing entries and predict future evolution accurately in real-time? In this work, we answer this question by introducing SOFIA, a robust factorization method for real-world tensor streams. In a nutshell, SOFIA smoothly and tightly integrates tensor factorization, outlier removal, and temporal-pattern detection, which naturally reinforce each other. Moreover, SOFIA integrates them in linear time, in an online manner, despite the presence of missing entries. We experimentally show that SOFIA is (a) robust and accurate: yielding up to 76% lower imputation error and 71% lower forecasting error;(b) fast: up to 935x faster than the second-most accurate competitor;and (c) scalable: scaling linearly with the number of new entries per time step.

关键词： Tensor Factorization streaming algorithm Outlier Robustness Holt-Winters Forecasting

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Development of a Green-Based Quantitative Assay Using Time Series to Detect Risks 15

Development of a Green-Based Quantitative Assay Using Time S...

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15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET)

作者： Bakay, Borys Rudko, Ihor Horzov, Stanislav Brohovska, Iryna Ukrainian Natl Forestry Univ Dept Forest Engn Lvov Ukraine

ISBN: (纸本)9781728155661

Designing efficient algorithms for forecasting of the state of the environment is one of the most important challenges in the field of time series analysis and accurate prediction. With the exponential rate of development of remote sensing and with the availability of fast computing platforms, it has now become possible to effectively and efficiently make use of vulnerability indicators forest stands.

关键词： time series forecasting streaming algorithm time series data remote sensing

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Optimal quantile estimation: beyond the comparison model 65

Optimal quantile estimation: beyond the comparison model

引用

65th Symposium on Foundations of Computer Science

作者： Gupta, Meghal Singhal, Mihir Wu, Hongxun Univ Calif Berkeley EECS Berkeley CA 94720 USA

ISBN: (纸本)9798331516758;9798331516741

Estimating quantiles is one of the foundational problems of data sketching. Given n elements x(1), x(2), ... , x(n) from some universe of size U arriving in a data stream, a quantile sketch estimates the rank of any element with additive error at most epsilon n. A low-space algorithm solving this task has applications in database systems, network measurement, load balancing, and many other practical scenarios. Current quantile estimation algorithms described as optimal include the GK sketch (Greenwald and Khanna 2001) using O(epsilon(-1) log n) words (deterministic) and the KLL sketch (Karnin, Lang, and Liberty 2016) using O(epsilon(-1) log log(1/delta)) words (randomized, with failure probability delta). However, both algorithms are only optimal in the comparison-based model, whereas many typical applications involve streams of integers that the sketch can use aside from making comparisons. If we go beyond the comparison-based model, the deterministic q-digest sketch (Shrivastava, Buragohain, Agrawal, and Suri 2004) achieves a space complexity of O(epsilon(-1) log U) words, which is incomparable to the previously-mentioned sketches. It has long been asked whether there is a quantile sketch using O(epsilon(-1)) words of space (which is optimal as long as n <= poly(U)). In this work, we present a deterministic algorithm using O(epsilon(-1)) words, resolving this line of work.

关键词： streaming algorithm quantiles sketching

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Optimal Multi-pass Lower Bounds for MST in Dynamic Streams 2024

Optimal Multi-pass Lower Bounds for MST in Dynamic Streams

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56th Annual ACM Symposium on Theory of Computing (STOC)

作者： Assadi, Sepehr Kol, Gillat Zhang, Zhijun Univ Waterloo Waterloo ON Canada Rutgers State Univ Piscataway NJ USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798400703836

The seminal work of Ahn, Guha, and McGregor in 2012 introduced the graph sketching technique and used it to present the first streaming algorithms for various graph problems over dynamic streams with both insertions and deletions of edges. This includes algorithms for cut sparsification, spanners, matchings, and minimum spanning trees (MSTs). These results have since been improved or generalized in various directions, leading to a vastly rich host of efficient algorithms for processing dynamic graph streams. A curious omission from the list of improvements has been the MST problem. The best algorithm for this problem remains the original AGM algorithm that for every integer p >= 1, uses n(1+O(1/p)) space in p passes on n-vertex graphs, and thus achieves the desired semi-streaming space of (O) over tilde (n) at a relatively high cost of O(log n/log log n) passes. On the other hand, no lower bound beyond a folklore one-pass lower bound is known for this problem. We provide a simple explanation for this lack of improvements: The AGM algorithm for MSTs is optimal for the entire range of its number of passes! We prove that even for the simplest decision version of the problem deciding whether the weight of MSTs is at least a given threshold or not any p-pass dynamic streaming algorithm requires n(1+Omega(1/p)) space. This implies that semi-streaming algorithms do need Omega(log n/log log n) passes. Our result relies on proving new multi-round communication complexity lower bounds for a variant of the universal relation problem that has been instrumental in proving prior lower bounds for single-pass dynamic streaming algorithms. The proof also involves proving new composition theorems in communication complexity, including majority lemmas and multi-party XOR lemmas, via information complexity approaches.

关键词： streaming algorithm Minimum spanning trees Communication complexity

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Estimating Cardinality Distributions in Network Traffic

Estimating Cardinality Distributions in Network Traffic

引用

International Conference on Measurement and Modeling of Computer Systems

作者： Chen, Aiyou Li, Li Cao, Jin Bell Labs Alcatel Lucent Technol Murray Hill NJ 07974 USA

ISBN: (纸本)9781605580050

Information on network host, connectivity patterns are important for network monitoring and traffic engineering. In this paper, all efficient streaming algorithm is proposed to estimate cardinality distributions including connectivity distributions, e.g. percent of hosts with any given number of distinct communicating peers or flows.

关键词： Cardinality distribution streaming algorithm

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Fair and Representative Subset Selection from Data Streams 21

Fair and Representative Subset Selection from Data Streams

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30th World Wide Web Conference (WWW)

作者： Wang, Yanhao Fabbri, Francesco Mathioudakis, Michael Univ Helsinki Helsinki Finland Pompeu Fabra Univ Barcelona Spain Eurecat Barcelona Spain

ISBN: (纸本)9781450383127

We study the problem of extracting a small subset of representative items from a large data stream. In many data mining and machine learning applications such as social network analysis and recommender systems, this problem can be formulated as maximizing a monotone submodular function subject to a cardinality constraint k. In this work, we consider the setting where data items in the stream belong to one of several disjoint groups and investigate the optimization problem with an additional fairness constraint that limits selection to a given number of items from each group. We then propose efficient algorithms for the fairness-aware variant of the streaming submodular maximization problem. In particular, we first give a (1/2 - epsilon)-approximation algorithm that requires O(1/epsilon log k/epsilon) passes over the stream for any constant epsilon > 0. Moreover, we give a single-pass streaming algorithm that has the same approximation ratio of (1/2 - epsilon) when unlimited buffer sizes and post-processing time are permitted, and discuss how to adapt it to more practical settings where the buffer sizes are bounded. Finally, we demonstrate the efficiency and effectiveness of our proposed algorithms on two real-world applications, namely maximum coverage on large graphs and personalized recommendation.

关键词： algorithmic fairness approximation algorithm data summarization streaming algorithm submodular maximization

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