| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000435709 |
| 005 | | 20200228103700 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781085645492 |
| 035 | |
▼a (MiAaPQ)AAI27529086 |
| 035 | |
▼a (MiAaPQ)NCState_Univ18402036855 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Padia, Kalpesh. |
| 245 | 10 |
▼a Storyline Visualization Techniques for Linear, Non-linear, and Diegetic Narratives. |
| 260 | |
▼a [S.l.]:
▼b North Carolina State University.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 151 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-02, Section: B. |
| 500 | |
▼a Advisor: Doyle, Jon |
| 502 | 1 |
▼a Thesis (Ph.D.)--North Carolina State University, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a Existing storyline visualization techniques present narratives as a node-link graph where a sequence of links shows the evolution of causal and temporal relationships between characters in the narrative. These techniques make a number of simplifying assumptions about the narrative structure, however. They assume that all narratives progress linearly in time, with a well-defined beginning, middle, and end. They also assume that at least two participants interact at every event. Finally, they assume that all events in the narrative occur along a single timeline. Thus, while existing techniques are suitable for visualizing linear narratives, they are not well suited for visualizing narratives with multiple timelines, flashbacks, or for narratives that contain events with only one participant.In this dissertation, we contribute three novel storyline visualization techniques to address the above challenges and create suitable visualizations for real-world narratives. The first technique extends StoryFlow, an optimization strategy for fast generation of narrative visualizations. It supports both single as well as multi-participant events in a narrative. It also introduces a novel constraint-based filtering approach to visualize large narratives without any temporal separation between events.The second technique focuses on visualizing the alternate outcomes for choice points in a narrative with multiple timelines (diegetic narratives). Given a set of event descriptions for a narrative in the form of a hierarchical task network (HTN), our technique creates a storyline visualization depicting events on both the reality timeline as well as the possible diegetic timelines in the narrative.Our third technique is a novel approach for automatic narrative construction and visualization. Our technique supports both single-participant as well as multi-participant events in the narrative, both single-timeline narratives as well as diegetic narratives, and constructs both linear as well as non-linear narratives. Additionally, it enables pairwise comparison within a group of multiple narrative timelines.Together, we offer three techniques that effectively visualize complex real-world narratives and allow users to examine, discover, and explore different real and potential narrations within a story domain. |
| 590 | |
▼a School code: 0155. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a North Carolina State University. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-02B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0155 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15494130
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1816162 |
| 991 | |
▼a E-BOOK |