Fast Greedy Algorithms in MapReduce and Streaming

Kumar, Ravi; Moseley, Benjamin; Vassilvitskii, Sergei; Vattani, Andrea

doi:10.1145/2809814

Cited by 147 publications

(172 citation statements)

References 42 publications

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“…The needs of the applications, and in particular the sheer bulk of large data sets, have brought into focus the development of fast algorithms for submodular optimization. Recent work on the theoretical side include the development of faster worst-case approximation algorithms in the traditional sequential model of computation [BV14,IJB13,CJV15], algorithms in the streaming model [BMKK14,CK14] as well as in the map-reduce model of computation [KMVV13].…”

Section: Introductionmentioning

confidence: 99%

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Streaming Algorithms for Submodular Function Maximization

Chekuri

Gupta

Quanrud

2015

Automata, Languages, and Programming

138

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We consider the problem of maximizing a nonnegative submodular set function f : 2 N → R + subject to a p-matchoid constraint in the single-pass streaming setting. Previous work in this context has considered streaming algorithms for modular functions and monotone submodular functions. The main result is for submodular functions that are non-monotone. We describe deterministic and randomized algorithms that obtain a Ω( 1 p )-approximation using O(k log k)-space, where k is an upper bound on the cardinality of the desired set. The model assumes value oracle access to f and membership oracles for the matroids defining the p-matchoid constraint.

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Section: Introductionmentioning

confidence: 99%

“…Their algorithm extends to multiple passes, with an approximation bound of 1/(p + 1 + ) with O( −3 log p) passes. The main focus of [KMVV13] is on the map-reduce model although they claim some streaming results as well.…”

Section: Introductionmentioning

confidence: 99%

Streaming Algorithms for Submodular Function Maximization

Chekuri

Gupta

Quanrud

2015

Automata, Languages, and Programming

138

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“…The first three procedures (Table 1) implement the classical GR [9,[33][34][35] with a single objective (GR_S). The decision variable that provides the best objective function value is chosen first.…”

Section: The Proposed Proceduresmentioning

confidence: 99%

Greedy Algorithms for Sensor Location in Sewer Systems

Banik

Alfonso

Cristo

et al. 2017

Water

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Wastewater quality monitoring is receiving growing interest with the necessity of developing new strategies for controlling accidental and intentional illicit intrusions. In designing a monitoring network, a crucial aspect is represented by the sensors' location. In this study, a methodology for the optimal placement of wastewater monitoring sensors in sewer systems is presented. The sensor location is formulated as an optimization problem solved using greedy algorithms (GRs). The Storm Water Management Model (SWMM) was used to perform hydraulic and water-quality simulations. Six different procedures characterized by different fitness functions are presented and compared. The performances of the procedures are tested on a real sewer system, demonstrating the suitability of GRs for the sensor-placement problem. The results show a robustness of the methodology with respect to the detection concentration parameter, and they suggest that procedures with multiple objectives into a single fitness function give better results. A further comparison is performed using previously developed multi-objective procedures with multiple fitness functions solved using a genetic algorithm (GA), indicating better performances of the GR. The existing monitoring network, realized without the application of any sensor design, is always suboptimal.

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“…For maximizing submodular functions, there already exist a number of stream algorithms (Krause and Gomes, 2010;Badanidiyuru et al, 2014;Kumar et al, 2015). The heap substitution algorithm we designed in this work resembles the algorithm developed in (Krause and Gomes, 2010) that addresses cardinality constraints.…”

Section: Stream Data Processingmentioning

confidence: 99%

Content Selection for Real-time Sports News Construction from Commentary Texts

Yao

Zhang

Wan

et al. 2017

Proceedings of the 10th International Conference on Natural Language Generation

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We study the task of constructing sports news report automatically from live commentary and focus on content selection. Rather than receiving every piece of text of a sports match before news construction, as in previous related work, we novelly verify the feasibility of a more challenging setting to generate news report on the fly by treating live text input as a stream. We design scoring functions to address different requirements of the task and use stream substitution for sentence selection. Experiments suggest that our proposed framework can already produce comparable results compared with previous work that relies on a supervised learning-to-rank model.

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Fast Greedy Algorithms in MapReduce and Streaming

Cited by 147 publications

References 42 publications

Streaming Algorithms for Submodular Function Maximization

Streaming Algorithms for Submodular Function Maximization

Greedy Algorithms for Sensor Location in Sewer Systems

Content Selection for Real-time Sports News Construction from Commentary Texts

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