Date of Award

2010

Publication Type

Master Thesis

Degree Name

M.Sc.

Department

Computer Science

First Advisor

Xiaobu Yuan

Second Advisor

Luis Rueda

Keywords

Applied sciences, Bloom filter, Compression, Stochastic learning-based weak estimation, Delta Bloom filter, PPM

Rights

info:eu-repo/semantics/openAccess

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Abstract

Substantial research has been done, and sill continues, for reducing the bandwidth requirement and for reliable access to the data, stored and transmitted, in a space efficient manner. Bloom filters and their variants have achieved wide spread acceptability in various fields due to their ability to satisfy these requirements.

As this need has increased, especially, for the applications which require heavy use of the transmission bandwidth, distributed computing environment for the databases or the proxy servers, and even the applications which are sensitive to the access to the information with frequent modifications, this thesis proposes a solution in the form of compressed delta Bloom filter.

This thesis proposes delta Bloom filter compression, using stochastic learning-based weak estimation and prediction with partial matching to achieve the goal of lossless compression with high compression gain for reducing the large data transferred frequently.

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