Better Sliding Window Algorithms to Maximize Subadditive and Diversity Objectives-Reference-Cited by-同舟云学术

Better Sliding Window Algorithms to Maximize Subadditive and Diversity Objectives

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems - PODS '19
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Author:

Borassi Michele¹,Epasto Alessandro²,Lattanzi Silvio¹,Vassilvitskii Sergei²,Zadimoghaddam Morteza¹

Affiliation:

1. Google Research, Zurich, Switzerland

2. Google Research, New York, NY, USA

Publisher

ACM Press

Reference39 articles.

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3. Arvind Arasu and Gurmeet Singh Manku. 2004. Approximate counts and quantiles over sliding windows. In Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. ACM, 286--296.

4. Brian Babcock, Mayur Datar, and Rajeev Motwani. 2002. Sampling from a moving window over streaming data. In Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics, 633--634.

5. Brain Babcock, Mayur Datar, Rajeev Motwani, and Liadan O'Callaghan. 2003. Maintaining Variance and K-medians over Data Stream Windows. In Proceedings of the Twenty-second ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS '03). ACM, New York, NY, USA, 234--243. https://doi.org/10.1145/773153.773176

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