An approximate dynamic programming approach for improving accuracy of lossy data compression by Bloom filters

DOI10.1016/j.ejor.2016.01.042zbMath1346.94058OpenAlexW2283826724MaRDI QIDQ323016

Laura Carrea, Alexei Vernitski, Xin'an Yang

Publication date: 7 October 2016

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: http://centaur.reading.ac.uk/53947/1/YangVernitskiCarrea-revision2_AV_18012016.pdf

zbMATH Keywords

heuristicsBloom filterapproximate dynamic programminginteger linear programlossy compression

Mathematics Subject Classification ID

Mixed integer programming (90C11) Dynamic programming (90C39) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Uses Software

• CPLEX

Cites Work

• Approximate dynamic programming with Bézier curves/surfaces for top-percentile traffic routing
• The multidimensional 0-1 knapsack problem: an overview.
• Optimizing data popularity conscious bloom filters
• An Approximate Dynamic Programming Approach to Multidimensional Knapsack Problems
• Solving the Top-percentile traffic routing problem by Approximate Dynamic Programming
• Less hashing, same performance: Building a better Bloom filter
• An approximate dynamic programing approach to the development of heuristics for the scheduling of impatient jobs in a clearing system
• Using the Power of Two Choices to Improve Bloom Filters
• Network Applications of Bloom Filters: A Survey
• Approximate Dynamic Programming
• Space/time trade-offs in hash coding with allowable errors
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: An approximate dynamic programming approach for improving accuracy of lossy data compression by Bloom filters