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Algorithms for Analyzing Spatio-temporal Data

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자료유형E-Book
개인저자Nath, Abhinandan.
단체저자명Duke University. Computer Science.
서명/저자사항Algorithms for Analyzing Spatio-temporal Data.
발행사항[S.l.] : Duke University., 2018
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항170 p.
소장본 주기School code: 0066.
ISBN9780438377257
일반주기 Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
Adviser: Pankaj K. Agarwal.
요약In today's age, huge data sets are becoming ubiquitous. In addition to their size, most of these data sets are often noisy, have outliers, and are incomplete. Hence, analyzing such data is challenging. We look at applying geometric techniques to
요약With the massive amounts of data available today, it is common to store and process data using multiple machines. Parallel programming frameworks such as MapReduce and its variants are becoming popular for handling such large data. We present th
요약We propose parallel algorithms in the MPC model for processing large terrain elevation data (represented as a 3D point cloud) that are too big to fit on one machine. In particular, we present a simple randomized algorithm to compute the Delaunay
요약We then look at comparing real-valued functions, by computing a distance function between their merge trees (a small-sized descriptor that succinctly captures the sublevel sets of a function). Merge trees are robust to noise in the data, and can
요약Finally we look at the problem of capturing shared portions between large number of input trajectories. We formulate it as a subtrajectory clustering problem - the clustering of subsequences of trajectories. We propose a new model for clustering
일반주제명Computer science.
언어영어
기본자료 저록Dissertation Abstracts International80-02B(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T14999630

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