Publication Date

2019

Document Type

Dissertation/Thesis

First Advisor

Papka, Michael E.

Degree Name

M.S. (Master of Science)

Legacy Department

Department of Computer Science

Abstract

Visual summarization is the act of displaying the most important information in a single

view or on a single screen. There are many existing powerful and useful information visualization tools and techniques to visualize large datasets, but the major challenge in using

these visualization tools efficiently are assessing what is needed to create awareness,

where awareness is dependent on the situation and context of the user and surrounding

events.

For example, when a user is driving (context) and approaching an intersection (situation)

the traffic light being red creates an awareness that the driver needs to stop, or when a fire alarm is sounded, it creates an awareness that there is a fire somewhere and firefighters need

to be called. The desire to summarize the data based on a given situation and present it in

a unique form is key to increasing situational awareness.

What is needed in this case means successfully organizing, managing and filtering the

available dataset; determining what data should be displayed using which technique to enable

the extraction of meaningful and significant results

Moreover, traditional data summarization processes do not have any specific rule or principle for designing the visualization. This results in misspent resources and poorly designed

visualization. Adding to the challenge is assessing the data summarization and evaluating

if it is serving the place of the original data. Furthermore, how effectively can the data

be displayed so as the user can have a meaningful understanding of the current situation, where

effectively means a successful encoding of the original data in a new summary form.

This thesis proposes an exploration of existing information visualization tools and techniques to identify different approaches for data summarization and visualization of large time

dependent multi-dimensional data with the goal to address the already mentioned challenges.

Given the need for data summarization in many research fields, care is taken to ensure the

lessons learned with this work can easily be translated to other domains.

Extent

66 pages

Language

eng

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.

Media Type

Text

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