| LDR | | 02547nam u200421 4500 |
| 001 | | 000000419369 |
| 005 | | 20190215163624 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438377257 |
| 035 | |
▼a (MiAaPQ)AAI10838373 |
| 035 | |
▼a (MiAaPQ)duke:14816 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Nath, Abhinandan. |
| 245 | 10 |
▼a Algorithms for Analyzing Spatio-temporal Data. |
| 260 | |
▼a [S.l.]:
▼b Duke University.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 170 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B. |
| 500 | |
▼a Adviser: Pankaj K. Agarwal. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Duke University, 2018. |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0066. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a Duke University.
▼b Computer Science. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-02B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0066 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999630
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |