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检索条件"主题词=Streaming Algorithms"

共 217 条记录，以下是141-150 订阅

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One Sketch to Rule Them All: Rethinking Network Flow Monitoring with UnivMon 16

One Sketch to Rule Them All: Rethinking Network Flow Monitor...

引用

ACM Conference on Special Interest Group on Data Communication (SIGCOMM)

作者： Liu, Zaoxing Manousis, Antonis Vorsanger, Gregory Sekar, Vyas Braverman, Vladimir Johns Hopkins Univ Baltimore MD 21218 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781450341936

Network management requires accurate estimates of metrics for many applications including traffic engineering (e.g., heavy hitters), anomaly detection (e.g., entropy of source addresses), and security (e.g., DDoS detection). Obtaining accurate estimates given router CPU and memory constraints is a challenging problem. Existing approaches fall in one of two undesirable extremes: (1) low fidelity general-purpose approaches such as sampling, or (2) high fidelity but complex algorithms customized to specific application-level metrics. Ideally, a solution should be both general (i.e., supports many applications) and provide accuracy comparable to custom algorithms. This paper presents UnivMon, a framework for flow monitoring which leverages recent theoretical advances and demonstrates that it is possible to achieve both generality and high accuracy. UnivMon uses an application-agnostic data plane monitoring primitive;different (and possibly unforeseen) estimation algorithms run in the control plane, and use the statistics from the data plane to compute application- level metrics. We present a proof-of-concept implementation of UnivMon using P4 and develop simple coordination techniques to provide a "one-bigswitch" abstraction for network-wide monitoring. We evaluate the effectiveness of UnivMon using a range of trace-driven evaluations and show that it offers comparable (and sometimes better) accuracy relative to custom sketching solutions across a range of monitoring tasks.

关键词： Flow Monitoring Sketching streaming algorithms

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A Minimal Variance Estimator for the Cardinality of Big Data Set Intersection 17

A Minimal Variance Estimator for the Cardinality of Big Data...

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23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Cohen, Reuven Katzir, Liran Yehezkel, Aviv Technion Dept Comp Sci IL-32000 Haifa Israel

ISBN: (纸本)9781450348874

In recent years there has been a growing interest in developing "streaming algorithms" for efficient processing and querying of continuous data streams. These algorithms seek to provide accurate results while minimizing the required storage and the processing time, at the price of a small inaccuracy in their output. A fundamental query of interest is the intersection size of two big data streams. This problem arises in many different application areas, such as network monitoring, database systems, data integration and information retrieval. In this paper we develop a new algorithm for this problem, based on the Maximum Likelihood (ML) method. We show that this algorithm outperforms all known schemes in terms of the estimation's quality (lower variance) and that it asymptotically achieves the optimal variance.

关键词： Data mining streaming algorithms Cardinality estimation Set intersection

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Sliding Window algorithms for Regular Languages 1

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12th International Conference on Language and Automata Theory and Applications (LATA)

作者： Ganardi, Moses Hucke, Danny Lohrey, Markus Univ Siegen Dept Elektrotech & Informat Holderlinstr 3 D-57076 Siegen Germany

This paper gives a survey on recent results for sliding window streaming algorithms for regular languages. Details can be found in the recent papers [18,19].

ISBN: (数字)9783319773131

ISBN: (纸本)9783319773131;9783319773124

This paper gives a survey on recent results for sliding window streaming algorithms for regular languages. Details can be found in the recent papers [18,19].

关键词： Automata theory streaming algorithms Sliding window algorithms Regular languages

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At-the-time and Back-in-time Persistent Sketches 21

At-the-time and Back-in-time Persistent Sketches

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ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Shi, Benwei Zhao, Zhuoyue Peng, Yanqing Li, Feifei Phillips, Jeff M. Univ Utah Salt Lake City UT 84112 USA

ISBN: (纸本)9781450383431

In the era of big data, more and more applications require the information of historical data to support rich analytics, learning, and mining operations. In these cases, it is highly desirable to retrieve information of previous versions of data. Traditionally, multi-version databases can be used to store all historical values of the data in order to support historical queries. However, storing all the historical data can be impractical due to its large space consumption. In this paper, we propose the concept of at-the-time persistent (ATTP) and back-in-time persistent (BITP) sketches, which are sketches that approximately answer queries on previous versions of data with small space. We then provide several implementations of ATTP/BITP sketches which are shown to be more efficient compared to existing state-of-the-art solutions in our empirical studies.

关键词： data sketching random sampling persistent data structure streaming algorithms

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Randomized Composable Core-sets for Distributed Submodular Maximization 15

Randomized Composable Core-sets for Distributed Submodular M...

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47th Annual ACM Symposium on Theory of Computing (STOC) held as part of the Federated Computing Research Conference

作者： Mirrokni, Vahab Zadimoghaddam, Morteza Google Res New York NY 10014 USA

ISBN: (纸本)9781450335362

An effective technique for solving optimization problems over massive data sets is to partition the data into smaller pieces, solve the problem on each piece and compute a representative solution from it, and finally obtain a solution inside the union of the representative solutions for all pieces. This technique can be captured via the concept of composable core-sets, and has been recently applied to solve diversity maximization problems as well as several clustering problems [7, 15, 8]. However, for coverage and submodular maximization problems, impossibility bounds are known for this technique [15]. In this paper, we focus on efficient construction of a randomized variant of composable core-sets where the above idea is applied on a random clustering of the data. We employ this technique for the coverage, monotone and non-monotone submodular maximization problems. Our results significantly improve upon the hardness results for non-randomized core-sets, and imply improved results for sub modular maximization in a distributed and streaming settings. The effectiveness of this technique has been confirmed empirically for several machine learning applications [22], and our proof provides a theoretical foundation to this idea. In summary, we show that a simple greedy algorithm results in a 1/3-approximate randomized composable core set for submodular maximization under a cardinality constraint. Our result also extends to non-monotone submodular functions, and leads to the first 2-round MapReduce-based constant-factor approximation algorithm with O(n) total communication complexity for either monotone or non monotone functions. Finally, using an improved analysis technique and a new algorithm PseudoGreedy, we present an improved 0.545-approximation algorithm for monotone sub modular maximization, which is in turn the first MapReduce-based algorithm beating factor 1/2 in a constant number of rounds.

关键词： Submodular Maximization Distributed algorithms streaming algorithms Core-sets Randomized Composable Core sets MapReduce algorithms

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Coresets remembered and items forgotten: submodular maximization with deletions 22

Coresets remembered and items forgotten: submodular maximiza...

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22nd IEEE International Conference on Data Mining (ICDM)

作者： Zhang, Guangyi Tatti, Nikolaj Gionis, Aristides KTH Royal Inst Technol Div Theoret Comp Sci Stockholm Sweden Univ Helsinki Dept Comp Sci HIIT Helsinki Finland

ISBN: (纸本)9781665450997

In recent years we have witnessed an increase on the development of methods for submodular optimization, which have been motivated by the wide applicability of submodular functions in real-world data-science problems. In this paper, we contribute to this line of work by considering the problem of robust submodular maximization against unexpected deletions, which may occur due to privacy issues or user preferences. Specifically, we consider the minimum number of items an algorithm has to remember, in order to achieve a non-trivial approximation guarantee against adversarial deletion of up to d items. We refer to the set of items that an algorithm has to keep before adversarial deletions as a deletion-robust coreset. Our theoretical contributions are two-fold. First, we propose a single-pass streaming algorithm that yields a (1 - 2 is an element of)/(4p)approximation for maximizing a non-decreasing submodular function under a general p-matroid constraint and requires a coreset of size k+ d/is an element of, where k is the maximum size of a feasible solution. To the best of our knowledge, this is the first work to achieve an (asymptotically) optimal coreset, as no constant-factor approximation is possible with a coreset of size sublinear in d. Second, we devise an effective offline algorithm that guarantees stronger approximation ratios with a coreset of size O( d log(k)/is an element of). We also demonstrate the superior empirical performance of the proposed algorithms in real-life applications.

关键词： robust optimization submodular maximization streaming algorithms approximation algorithms

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Non-adaptive Adaptive Sampling on Turnstile Streams 2020

Non-adaptive Adaptive Sampling on Turnstile Streams

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52nd Annual ACM SIGACT Symposium on Theory of Computing (STOC)

作者： Mahabadi, Sepideh Razenshteyn, Ilya Woodruff, David P. Zhou, Samson Toyota Technol Inst Chicago TTIC Chicago IL 60637 USA Microsoft Res Redmond WA USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781450369794

Adaptive sampling is a useful algorithmic tool for data summarization problems in the classical centralized setting, where the entire dataset is available to the single processor performing the computation. Adaptive sampling repeatedly selects rows of an underlying matrix A is an element of R-nxd, where n >> d, with probabilities proportional to their distances to the subspace of the previously selected rows. Intuitively, adaptive sampling seems to be limited to trivial multi-pass algorithms in the streaming model of computation due to its inherently sequential nature of assigning sampling probabilities to each row only after the previous iteration is completed. Surprisingly, we show this is not the case by giving the first one-pass algorithms for adaptive sampling on turnstile streams and using space poly(d, k, logn), where k is the number of adaptive sampling rounds to be performed. Our adaptive sampling procedure has a number of applications to various data summarization problems that either improve state-of-the-art or have only been previously studied in the more relaxed row-arrival model. We give the first relative-error algorithm for column subset selection on turnstile streams. We show our adaptive sampling algorithm also gives the first relative-error algorithm for subspace approximation on turnstile streams that returns k noisy rows of A. The quality of the output can be improved to a (1 +epsilon)-approximation at the tradeoff of a bicriteria algorithm that outputs a larger number of rows. We then give the first algorithm for projective clustering on turnstile streams that uses space sublinear in n. In fact, we use space poly (d, k, s, 1/epsilon, log n) to output a (1 +epsilon)-approximation, where s is the number of k-dimensional subspaces. Our adaptive sampling primitive also provides the first algorithm for volume maximization on turnstile streams. We complement our volume maximization algorithmic results with lower bounds that are tight up to lower orde

关键词： streaming algorithms computational geometry determinantal point processes volume maximization

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Space-Optimal Heavy Hitters with Strong Error Bounds 09

Space-Optimal Heavy Hitters with Strong Error Bounds

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28th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS)

作者： Berinde, Radu Indyk, Piotr Cormode, Graham Strauss, Martin J. MIT Cambridge MA 02139 USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781605585536

The problem of finding heavy hitters and approximating the frequencies of items is at the heart of many problems in data stream analysis. It has been observed that several proposed solutions to this problem can outperform their worst-case guarantees on real data. This leads to the question of whether some stronger bounds can be guaranteed. We answer this in the positive by showing that a class of counter-based algorithms (including the popular and very space-efficient FREQUENT and SPACESAVING algorithms) provides much stronger approximation guarantees than previously known. Specifically, we show that errors in the approximation of individual elements do not depend on the frequencies of the most frequent elements, but only on the frequency of the remaining tail. This shows that counter-based methods are the most space-efficient (in fact, space-optimal) algorithms having this strong error bound. This tail guarantee allows these algorithms to solve the sparse recovery problem. Here, the goal is to recover a faithful representation of the vector of frequencies, f. We prove that using space O(k), the algorithms construct an approximation f* to the frequency vector f so that the L-1 error parallel to f - f*parallel to(1) is close to the best possible error min f' parallel to f' - f parallel to(1), where f' ranges over all vectors with at most k non-zero entries. This improves the previously best known space bound of about O(k log n) for streams without element deletions (where n is the size of the domain from which stream elements are drawn). Other consequences of the tail guarantees are results for skewed (Zipfian) data, and guarantees for accuracy of merging multiple summarized streams.

关键词： algorithms Theory Frequency estimation heavy hitters streaming algorithms

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The Coin Problem with Applications to Data Streams 61

The Coin Problem with Applications to Data Streams

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61st IEEE Annual Symposium on Foundations of Computer Science (FOCS)

作者： Braverman, Mark Garg, Sumegha Woodruff, David P. Princeton Univ Dept Comp Sci Princeton NJ 08544 USA Harvard Univ Dept Comp Sci Cambridge MA 02138 USA CMU Dept Comp Sci Pittsburgh PA USA

ISBN: (纸本)9781728196213

Consider the problem of computing the majority of a stream of n i.i.d. uniformly random bits. This problem, known as the coin problem, is central to a number of counting problems in different data stream models. We show that any streaming algorithm for solving this problem with large constant advantage must use Omega(log n) bits of space. We extend our lower bound to proving tight lower bounds for solving multiple, randomly interleaved copies of the coin problem, as well as for solving the OR of multiple copies of a variant of the coin problem. Our proofs involve new measures of information complexity that are well-suited for data streams. We use these lower bounds to obtain a number of new results for data streams. In each case there is an underlying d-dimensional vector x with additive updates to its coordinates given in a stream of length m. The input streams arising from our coin lower bound have nice distributional properties, and consequently for many problems for which we only had lower bounds in general turnstile streams, we now obtain the same lower bounds in more natural models, such as the bounded deletion model, in which parallel to x parallel to(2) never drops by a constant fraction of what it was earlier, or in the random order model, in which the updates are ordered randomly. In particular, in the bounded deletion model, we obtain nearly tight lower bounds for approximating parallel to x parallel to(infinity) up to additive error 1/root k parallel to x parallel to(2), approximating parallel to x parallel to(2) up to a multiplicative (1 + epsilon) factor (resolving a question of Jayaram and Woodruff in PODS 2018), and solving the Point Query and l(2)-Heavy Hitters Problems. In the random order model, we also obtain new lower bounds for the Point Query and l(2)-Heavy Hitters Problems. We also give new algorithms complementing our lower bounds and illustrating the tightness of the models we consider, including an algorithm for approximating parallel to x

关键词： space lower bounds majority streaming algorithms coin problem information cost heavy hitters bounded deletion model

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QSketch: An Efficient Sketch for Weighted Cardinality Estimation in Streams 24

QSketch: An Efficient Sketch for Weighted Cardinality Estima...

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30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

作者： Qi, Yiyan Li, Rundong Wang, Pinghui Sun, Yufang Xing, Rui Int Digital Econ Acad IDEA Shenzhen Guangdong Peoples R China Xi An Jiao Tong Univ MOE KLINNS Lab Xian Peoples R China

ISBN: (纸本)9798400704901

Estimating cardinality, i.e., the number of distinct elements, of a data stream is a fundamental problem in areas like databases, computer networks, and information retrieval. This study delves into a broader scenario where each element carries a positive weight. Unlike traditional cardinality estimation, limited research exists on weighted cardinality, with current methods requiring substantial memory and computational resources, challenging for devices with limited capabilities and real-time applications like anomaly detection. To address these issues, we propose QSketch, a memory-efficient sketch method for estimating weighted cardinality in streams. QSketch uses a quantization technique to condense continuous variables into a compact set of integer variables, with each variable requiring only 8 bits, making it 8 times smaller than previous methods. Furthermore, we leverage dynamic properties during QSketch generation to significantly enhance estimation accuracy and achieve a lower time complexity of O(1) for updating estimations upon encountering a new element. Experimental results on synthetic and real-world datasets show that QSketch is approximately 30% more accurate and two orders of magnitude faster than the state-of-the-art, using only 1/8 of the memory.

关键词： streaming algorithms Sketch Weighted Cardinality Estimation

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