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Streaming techniques for statistical modeling
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Title
Streaming techniques for statistical modeling
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Wu
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Yihua
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Yihua Wu
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Muthukrishnan
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S.
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Advisory Committee
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S. Muthukrishnan
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chair
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Madigan
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David
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Advisory Committee
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David Madigan
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internal member
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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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Srivastava
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Divesh
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Advisory Committee
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Divesh Srivastava
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outside member
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Rutgers University
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Graduate School - New Brunswick
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theses
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2007
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2007
Language
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English
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electronic
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xiii, 131 pages
Abstract
Streaming is an important paradigm for handling high-speed data sets that are too large to fit in main memory. Prior work in data streams has shown how to estimate simple statistical parameters, such as histograms, heavy hitters, frequent moments, etc., on data streams. This dissertation focuses on a number of more sophisticated statistical analyses that are performed in near real-time, using limited resources.
First, we present how to model stream data parametrically; in particular, we fit hierarchical (binomial multifractal) and non-hierarchical (Pareto) power-law models on a data stream. It yields algorithms that are fast, space-efficient, and provide accuracy guarantees. We also design fast methods to perform online model validation at streaming speeds.
The second contribution of this dissertation addresses the problem of modeling an individual's behaviors via ``signature'' for nodes in communication graphs. We develop a formal framework for the usage of signatures on communication graphs and identify fundamental properties that are natural to signature schemes. We justify these properties by showing how they impact a set of applications. We then explore several signature schemes in our framework and evaluate them on real data in terms of these properties. This provides insights into suitable signature schemes for desired applications.
Finally, the dissertation studies the detection of changes in models on data with unknown distributions. We adapt the sound statistical method of sequential probability ratio test to the online streaming case, without independence assumption. The resulting algorithm works seamlessly without window limitations inherent in prior work, and is highly effective at detecting changes quickly. Furthermore, we formulate and extend our streaming solution to the local change detection problem that has not been addressed earlier.
As concrete applications of our techniques, we complement our analytic and algorithmic results with experiments on network traffic data to demonstrate the practicality of our methods at line speeds, and the potential power of streaming techniques for statistical modeling in data mining.
First, we present how to model stream data parametrically; in particular, we fit hierarchical (binomial multifractal) and non-hierarchical (Pareto) power-law models on a data stream. It yields algorithms that are fast, space-efficient, and provide accuracy guarantees. We also design fast methods to perform online model validation at streaming speeds.
The second contribution of this dissertation addresses the problem of modeling an individual's behaviors via ``signature'' for nodes in communication graphs. We develop a formal framework for the usage of signatures on communication graphs and identify fundamental properties that are natural to signature schemes. We justify these properties by showing how they impact a set of applications. We then explore several signature schemes in our framework and evaluate them on real data in terms of these properties. This provides insights into suitable signature schemes for desired applications.
Finally, the dissertation studies the detection of changes in models on data with unknown distributions. We adapt the sound statistical method of sequential probability ratio test to the online streaming case, without independence assumption. The resulting algorithm works seamlessly without window limitations inherent in prior work, and is highly effective at detecting changes quickly. Furthermore, we formulate and extend our streaming solution to the local change detection problem that has not been addressed earlier.
As concrete applications of our techniques, we complement our analytic and algorithmic results with experiments on network traffic data to demonstrate the practicality of our methods at line speeds, and the potential power of streaming techniques for statistical modeling in data mining.
Note
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Ph.D.
Note
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Includes bibliographical references (p. 118-129).
Subject
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Topic
Computer Science
Subject
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Topic
Streaming technology (Telecommunications)
Subject
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Topic
Data transmission systems
Subject
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Topic
Data mining
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.16795
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doi:10.7282/T33T9HM0
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ETD doctoral
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Rights
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Open
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Yihua Wu
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Rutgers University. Graduate School - New Brunswick
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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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