자료유형 | E-Book |
---|---|
개인저자 | Nath, Abhinandan. |
단체저자명 | Duke University. Computer Science. |
서명/저자사항 | Algorithms for Analyzing Spatio-temporal Data. |
발행사항 | [S.l.] : Duke University., 2018 |
발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2018 |
형태사항 | 170 p. |
소장본 주기 | School code: 0066. |
ISBN | 9780438377257 |
일반주기 |
Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
Adviser: Pankaj K. Agarwal. |
요약 | In 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 |
요약 | With 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 |
요약 | We 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 |
요약 | We 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 |
요약 | Finally 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 |
일반주제명 | Computer science. |
언어 | 영어 |
기본자료 저록 | Dissertation Abstracts International80-02B(E). Dissertation Abstract International |
대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T14999630 |
인쇄
No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
---|---|---|---|---|---|---|---|---|
1 | WE00025475 | 004 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출가능 |