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Data compression in dynamic systems
Descriptive
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Title
Data compression in dynamic systems
Name
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Chen
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Su
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Su Chen
Role
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author
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Muthukrishnan
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Shan
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Advisory Committee
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Shan Muthukrishnan
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chair
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Martin
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Richard
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Advisory Committee
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Richard Martin
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Ahmed
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Advisory Committee
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Ahmed Elgammal
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internal member
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Gao
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Lixin
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Advisory Committee
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Lixin Gao
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Rutgers University
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degree grantor
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Graduate School - New Brunswick
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theses
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DateCreated
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2008
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2008-05
Language
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English
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electronic
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application/pdf
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Extent
xii, 85 pages
Abstract
Data compression in dynamic systems has several applications in the real world. Unlike the compression of static data, both data and intrinsic data patterns may change over time. A good compression in dynamic systems should either keep compression accurate for dynamic data or change its compression strategies for dynamic data patterns. In this thesis, we study both scenarios with applications from the real world. First, as an example of compression in dynamic systems with changing data, we discuss a lossy compression in databases, called synopsis, which helps the query optimizer speed up the query process. We introduce new Haar wavelet synopsis for nonuniform accuracy and time-varying data that can be generated in near linear time and space, and updated in sublinear time. The effectiveness of our data synopsis is validated against other linear time methods by using both synthetic and real data sets. Second, as an example of compression in dynamic systems with changing data patterns, we propose a novel compression algorithm, called IPzip, which compresses IP network traffic both online and offline for efficient data transfer and storage. IPzip achieves better compression ratios by learning patterns residing in both data structures and content. We also propose a methodology to monitor over time the effectiveness of the current compression and start new pattern learning when intrinsic traffic structure changes. Finally, via trace-driven experiments on network traffic obtained from Tier-1 ISPs, we validate that IPzip achieves better performance compared to previous approaches.
Note
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Ph.D.
Note
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Includes bibliographical references (p. 80-83).
Subject
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Topic
Computer Science
Subject
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Topic
Data compression (Computer science)
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Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
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rucore19991600001
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17287
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ETD_819
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Identifier
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doi:10.7282/T3Q81DG0
Genre
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ETD doctoral
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Rights
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The author owns the copyright to this work.
Copyright
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Copyright protected
Availability
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Open
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Name
Su Chen
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Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
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Non-exclusive ETD license
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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Technical
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