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检索条件"主题词=Streaming algorithms"

共 217 条记录，以下是181-190 订阅

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A High-Performance Algorithm for Identifying Frequent Items in Data Streams 17

A High-Performance Algorithm for Identifying Frequent Items ...

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ACM Internet Measurement Conference (IMC)

作者： Anderson, Daniel Bevan, Pryce Lang, Kevin Liberty, Edo Rhodes, Lee Thaler, Justin Georgetown Univ Washington DC 20057 USA Georgetown Univ Washington DC 20057 USA Amazon Washington DC 20057 USA Oath Washington DC 20057 USA

ISBN: (纸本)9781450351188

Estimating frequencies of items over data streams is a common building block in streaming data measurement and analysis. Misra and Gries introduced their seminal algorithm for the problem in 1982, and the problem has since been revisited many times due its practicality and applicability. We describe a highly optimized version of Misra and Gries' algorithm that is suitable for deployment in industrial settings. Our code is made public via an open source library called Data Sketches that is already used by several companies and production systems. Our algorithm improves on two theoretical and practical aspects of prior work. First, it handles weighted updates in amortized constant time, a common requirement in practice. Second, it uses a simple and fast method for merging summaries that asymptotically improves on prior work even for unweighted streams. We describe experiments confirming that our algorithms are more efficient than prior proposals.

关键词： streaming algorithms mergeable summaries frequent items

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Timely Reporting of Heavy Hitters using External Memory 20

Timely Reporting of Heavy Hitters using External Memory

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ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Pandey, Prashant Singh, Shikha Bender, Michael A. Berry, Jonathan W. Farach-Colton, Martin Johnson, Rob Kroeger, Thomas M. Phillips, Cynthia A. Carnegie Mellon Univ Pittsburgh PA 15213 USA Wellesley Coll Wellesley MA 02181 USA SUNY Stony Brook Stony Brook NY 11794 USA Sandia Natl Labs Livermore CA 94550 USA Rutgers State Univ New Brunswick NJ USA VMware Res Palo Alto CA USA

ISBN: (纸本)9781450367356

Given an input stream of size N, a phi-heavy hitter is an item that occurs at least phi N times in S. The problem of finding heavy-hitters is extensively studied in the database literature. We study a real-time heavy-hitters variant in which an element must be reported shortly after we see its T = phi Nth occurrence (and hence becomes a heavy hitter). We call this the Timely Event Detection (TED) Problem. The TED problem models the needs of many real-world monitoring systems, which demand accurate (i.e., no false negatives) and timely reporting of all events from large, high-speed streams, and with a low reporting threshold (high sensitivity). Like the classic heavy-hitters problem, solving the TED problem without false-positives requires large space (Omega(N) words). Thus in-RAM heavy-hitters algorithms typically sacrifice accuracy (i.e., allow false positives), sensitivity, or timeliness (i.e., use multiple passes). We show how to adapt heavy-hitters algorithms to external memory to solve the TED problem on large high-speed streams while guaranteeing accuracy, sensitivity, and timeliness. Our data structures are limited only by I/O-bandwidth (not latency) and support a tunable trade-off between reporting delay and I/O overhead. With a small bounded reporting delay, our algorithms incur only a logarithmic I/O overhead. We implement and validate our data structures empirically using the Firehose streaming benchmark. Multi-threaded versions of our structures can scale to process 11M observations per second before becoming CPU bound. In comparison, a naive adaptation of the standard heavy-hitters algorithm to external memory would be limited by the storage device's random I/O throughput, i.e., approximate to 100K observations per second.

关键词： Dictionary data structure streaming algorithms external-memory algorithms

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Space- and Time-Efficient Algorithm for Maintaining Dense Subgraphs on One-Pass Dynamic Streams 15

Space- and Time-Efficient Algorithm for Maintaining Dense Su...

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

作者： Bhattacharya, Sayan Henzinger, Monika Nanongkai, Danupon Tsourakakis, Charalampos E. Inst Math Sci Chennai Tamil Nadu India Univ Vienna Vienna Austria KTH Royal Inst Technol Stockholm Sweden Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9781450335362

While in many graph mining applications it is crucial to handle a stream of updates efficiently in terms of both time and space, not much was known about achieving such type of algorithm. In this paper we study this issue for a problem which lies at the core of many graph mining applications called densest subgraph problem. We develop an algorithm that achieves time- and space-efficiency for this problem simultaneously. It is one of the first of its kind for graph problems to the best of our knowledge. Given an input graph, the densest subgraph is the sub graph that maximizes the ratio between the number of edges and the number of nodes. For any > 0, our algorithm can, with high probability, maintain a (4 + 0-approximate solution under edge insertions and deletions using 0(n) space and 6(1) amortized time per update;here, n is the number of nodes in the graph and O hides the 0(poly n) term. The approximation ratio can be improved to (2 + 6) with more time. It can be extended to a (2 + 0-approximation sublinear-time algorithm and a distributed-streaming algorithm. Our algorithm is the first streaming algorithm that can maintain the densest subgraph in one pass. Prior to this, no algorithm could do so even in the special case of an incremental stream and even when there is no time restriction. The previously best algorithm in this setting required 0(log n) passes [6]. The space required by our algorithm is tight up to a polylogarithmic factor.

关键词： Dynamic graph algorithms streaming algorithms dense sub graphs

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A FAST AND ACCURATE FREQUENT DIRECTIONS ALGORITHM FOR LOW RANK APPROXIMATION VIA BLOCK KRYLOV ITERATION

A FAST AND ACCURATE FREQUENT DIRECTIONS ALGORITHM FOR LOW RA...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yi, Qianxin Wang, Chenhao Liao, Xiuwu Wang, Yao Xi An Jiao Tong Univ Ctr Intelligent Decis Making & Machine Learning Xian Peoples R China

ISBN: (纸本)9781509066315

It is known that frequent directions (FD) is a popular deterministic matrix sketching technique for low rank approximation. However, FD and its randomized variants usually meet high computational cost or computational instability in dealing with large-scale datasets, which limits their use in practice. To remedy such issues, this paper aims at improving the efficiency and effectiveness of FD. Specifically, by utilizing the power of Block Krylov Iteration and count sketch techniques, we propose a fast and accurate FD algorithm dubbed as BKICS-FD. We derive the error bound of the proposed BKICS-FD and then carry out extensive numerical experiments to illustrate its superiority over several popular FD algorithms, both in terms of computational speed and accuracy.

关键词： low rank approximation Block Krylov Iteration frequent directions randomized algorithms streaming algorithms

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A sketch-based algorithm for network-flow entropy estimation on programmable switches using P4 26

A sketch-based algorithm for network-flow entropy estimation...

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26th Euromicro Conference on Digital System Design (DSD) / 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

作者： Soto, Javier E. Vera, Sofia Fernandez, Yaime Yunge, Daniel Hernandez, Cecilia Figueroa, Miguel Univ Concepcion Dept Elect Engn Concepcion Chile Univ Concepcion Dept Comp Sci Concepcion Chile Pontificia Univ Catolica Valparaiso Dept Elect Engn Valparaiso Chile

ISBN: (纸本)9798350344196

The empirical Shannon entropy is a popular metric for anomaly detection in network traffic. However, computing its exact value in real time requires fast access to a large number of counters, which is unfeasible in high-speed networks. Approximate approaches using sketches can estimate the entropy with low memory usage. However, achieving good estimation accuracy still requires large data structures, making their implementation difficult in dedicated hardware and programable switches. In this paper, we present an entropy-estimation algorithm and its implementation in a programmable switch, which achieves good accuracy for large traffic traces with low memory usage. The algorithm uses sketches to track the packet count of only the most-frequent flows and models the rest of the traffic with a uniform distribution. The implementation operates within the restrictions imposed by the P4 switch programming language, achieving a 1.72% average estimation error on 12 real-world large traces from public repositories.

关键词： Shannon empirical entropy programmable switches hardware acceleration P4 streaming algorithms

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Concept Drift Detection via Improved Deep Belief Network 26

Concept Drift Detection via Improved Deep Belief Network

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26th Iranian Conference on Electrical Engineering (ICEE)

作者： Hatamikhah, Nafiseh Barari, Morteza Kangavari, Mohammad Reza Keyvanrad, Mohammad Ali Shahid Beheshti Univ Comp Engn Dept Tehran Iran Amirkabir Univ Technol Elect Engn Dept Tehran Iran Univ Sci & Technol Comp Engn Dept Tehran Iran Amirkabir Univ Technol Comp Engn Dept Tehran Iran

ISBN: (纸本)9781538649169

One of the issues raised in streaming data is concept drift detection. In fact, the process of concept drift comes from natural tendency events in the real world to change over time. For example, in data receiving from credit card transactions, detect when transactions rise suddenly, can help in identifying the fraud. In this paper regards to the importance of concept drift in streaming data, a solution to accurate diagnosis and timely is presented. This solution is based on ensemble algorithm and "streaming ensemble algorithm" (SEA) algorithm that SEA algorithm is used as one of the most commonly stream algorithms. This approach uses a deep belief network as a basic model in the SEA algorithm. In the method which is presented in this paper, we used the change of classification error on new data for concept drift detection. Analyzing the results shows that the proposed method compared with similar algorithms, in addition to a significant reduction in the runtime, improved F_measure criteria.

关键词： component streaming Data Concept Drift streaming algorithms Deep Learning Deep Belief Network

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Multi-pass geometric algorithms

Multi-pass geometric algorithms

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21st Annual Symposium on Computational Geometry

作者： Chan, Timothy M. Chen, Eric Y. Univ Waterloo Sch Comp Sci Waterloo ON N2L 3G1 Canada

We propose the study of exact geometric algorithms that require limited storage and make only a small number of passes over the input. Fundamental problems such as low-dimensional linear programming and convex hulls a... 详细信息

关键词： Convex hulls Linear programming streaming algorithms

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Scaling Graph Clustering with Distributed Sketches

Scaling Graph Clustering with Distributed Sketches

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IEEE High Performance Extreme Computing Conference (HPEC)

作者： Priest, Benjamin W. Dunton, Alec Sanders, Geoffrey Lawrence Livermore Natl Lab Ctr Appl Sci Comp Livermore CA 94550 USA Univ Colorado Dept Appl Math Boulder CO 80309 USA

ISBN: (纸本)9781728192192

The unsupervised learning of community structure, in particular the partitioning vertices into clusters or communities, is a canonical and well-studied problem in exploratory graph analysis. However, like most graph analyses the introduction of immense scale presents challenges to traditional methods. Spectral clustering in distributed memory, for example, requires hundreds of expensive bulk-synchronous communication rounds to compute an embedding of vertices to a few eigenvectors of a graph associated matrix. Furthermore, the whole computation might need to be repeated if the underlying graph changes some low percentage of edge updates. We present a method inspired by spectral clustering where we instead use matrix sketches derived from random dimension-reducing projections. We show that our method produces embeddings that yield performant clustering results given a fully-dynamic stochastic block model stream using both the fast Johnson-Lindenstrauss and CountSketch transforms. We also discuss the effects of stochastic block model parameters upon the required dimensionality of the subsequent embeddings, and show how random projections could significantly improve the performance of graph clustering in distributed memory.

关键词： streaming algorithms random matrices graph clustering

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Approximation algorithms for wavelet transform coding of data stream.

Approximation algorithms for wavelet transform coding of dat...

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17th ACM-SIAM Symposium on Discrete algorithms

作者： Guha, Sudipto Harb, Boulos Univ Penn Dept Comp Informat Sci Philadelphia PA 19104 USA Google Inc New York NY 10011 USA

ISBN: (纸本)9780898716054

This paper addresses the problem of finding a B-term wavelet representation of a given discrete function f is an element of R-n whose distance from f is minimized. The problem is well understood when we seek to minimize the Euclidean distance between f and its representation. The first-known algorithms for finding provably approximate representations minimizing general f, distances (including l(infinity)) under a wide variety of compactly supported wavelet bases are presented in this paper. For the Haar basis, a polynomial time approximation scheme is demonstrated. These algorithms are applicable in the one-pass sublinear-space data stream model of computation. They generalize naturally to multiple dimensions and weighted norms. A universal representation that provides a provable approximation guarantee under all p-norms simultaneously;and the first approximation algorithms for bit-budget versions of the problem, known as adaptive quantization, are also presented. Further, it is shown that the algorithms presented here can be used to select a basis from a tree-structured dictionary of bases and find a B-term representation of the given function that provably approximates its best dictionary-basis representation.

关键词： adaptive quantization best basis selection compactly supported wavelets nonlinear approximation sparse representation streaming algorithms transform coding universal representation

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Model Counting meets F₀ Estimation 21

Model Counting meets <i>F</i><sub>0</sub> Estimation

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40th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS)

作者： Pavan, A. Vinodchandran, N. V. Bhattacharyya, Arnab Meel, Kuldeep S. Iowa State Univ Ames IA 50011 USA Univ Nebraska Lincoln NE USA Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781450383813

Constraint satisfaction problems (CSP's) and data stream models are two powerful abstractions to capture a wide variety of problems arising in different domains of computer science. Developments in the two communities have mostly occurred independently and with little interaction between them. In this work, we seek to investigate whether bridging the seeming communication gap between the two communities may pave the way to richer fundamental insights. To this end, we focus on two foundational problems: model counting for CSP's and computation of zeroth frequency moments (F-0) for data streams. Our investigations lead us to observe striking similarity in the core techniques employed in the algorithmic frameworks that have evolved separately for model counting and F-0 computation. We design a recipe for translation of algorithms developed for F-0 estimation to that of model counting, resulting in new algorithms for model counting. We then observe that algorithms in the context of distributed streaming can be transformed to distributed algorithms for model counting. We next turn our attention to viewing streaming from the lens of counting and show that framing F-0 estimation as a special case of #DNF counting allows us to obtain a general recipe for a rich class of streaming problems, which had been subjected to case-specific analysis in prior works. In particular, our view yields a state-of-the art algorithm for multidimensional range efficient F-0 estimation with a simpler analysis.

关键词： Model Counting streaming algorithms F-0-computation DNF Counting

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