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Towards large-scale network analytics

Abstract Details

2012, Doctor of Philosophy, Ohio State University, Computer Science and Engineering.
In this thesis, we present a framework for efficient analysis of large-scale network datasets. There are four important components in our framework: a) a high performance computing platform with Graphics Processing Units (GPUs) and efficient implementations of mining algorithms on top of the GPU platform. b) an efficient summarization method to compress the storage space of large-scale streaming and heterogeneous network data with textual content and network topology. c) a complex query engine that depends on the summarized input data and can help to discover new knowledge from network content and topology. d) a visual front-end to present mining results to users. In each of the above components, we propose new methods to either speed up mining tasks or reduce the data storage size in those tasks. We compare our methods with existing approaches on real datasets drawn from various domains.
Srinivasan Parthasarathy, PhD (Advisor)
Gagan Agrawal, PhD (Committee Member)
Ponnuswamy Sadayappan, PhD (Committee Member)
203 p.

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Citations

  • Yang, X. (2012). Towards large-scale network analytics [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1343680930

    APA Style (7th edition)

  • Yang, Xintian. Towards large-scale network analytics. 2012. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1343680930.

    MLA Style (8th edition)

  • Yang, Xintian. "Towards large-scale network analytics." Doctoral dissertation, Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1343680930

    Chicago Manual of Style (17th edition)