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检索条件"主题词=Streaming Algorithms"
217 条 记 录,以下是141-150 订阅
排序:
One Sketch to Rule Them All: Rethinking Network Flow Monitoring with UnivMon  16
One Sketch to Rule Them All: Rethinking Network Flow Monitor...
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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
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 dete... 详细信息
来源: 评论
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
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 whil... 详细信息
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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].
来源: 评论
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
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 ... 详细信息
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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
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 ... 详细信息
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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
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.... 详细信息
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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
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 s... 详细信息
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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
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 outper... 详细信息
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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
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 sh... 详细信息
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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
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... 详细信息
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