master thesis proposalcs.uccs.edu/~gsc/pub/master/pjvinton/doc/thesis.doc · web viewa thesis...

Information Visualization Engine

(iVE)

by

PETER JOHN VINTON

B.S., University of California, 1985

A thesis submitted to the Graduate Faculty of the

University of Colorado at Colorado Springs

in partial fulfillment of the

requirements for the degree of

Masters of Science

Department of Computer Science

2004

This thesis for Master of Science degree byPeter John Vinton

has been approved for the

department of Computer Science

by

_________________________________Chair, Marijke Augusteijn

_________________________________Jugal Kumar Kalita

_________________________________Edward Chow

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© Copyright By Peter John Vinton 2004All Rights Reserved

iii

CONTENTS

CHAPTER

I. Introduction..............................................................................................1

Introduction of the Problem.................................................................................1

Motivation............................................................................................................2

Solving the Problem............................................................................................2

Result Summary...................................................................................................3

II. Background Research............................................................................4

Value Bars...........................................................................................................4

Information visualization using 3D interactive animation..................................5

TileBars................................................................................................................6

Cone Trees...........................................................................................................6

The Perspective Wall...........................................................................................7

Generalized Fisheye Views.................................................................................8

SemNet................................................................................................................9

Database Navigation..........................................................................................10

The Information Visualizer................................................................................12

InfoCrystal.........................................................................................................12

Summary and Comparison................................................................................13

III. Information Visualization Engine (iVE)............................................15

iv

iVE, a User-Friendly Approach to Low-level Information Visualization.........15

Components of iVE...........................................................................................17

The Information Visualization Design......................................................17

Low-Level Vision..................................................................................18

High-Level Vision.................................................................................19

GUI Design............................................................................................21

iVE GUI Architecture............................................................................23

The Program/Algorithms...........................................................................25

Language Selection, Software Architecture, Software Setup................25

Software Narrative.................................................................................27

Pseudo-Code..........................................................................................28

Algorithm/Modules of Interest..............................................................32

IV. iVE Visualizations................................................................................36

iVE Visualization Explanations.........................................................................36

V. iVE Evaluation......................................................................................50

Usability Evaluation Design Background.........................................................50

Evaluation Design......................................................................................53

Evaluation Results.....................................................................................54

Evaluation Form........................................................................................56

VI. Conclusions and Future Research......................................................58

Accomplishment................................................................................................58

Problems Faced and Solutions...........................................................................58

Improving iVE...................................................................................................59

v

Impact of Research............................................................................................61

References....................................................................................................62

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FIGURES

Figure

1 iVE GUI Architecture..............................................................................................24

2 iVE Main GUI..........................................................................................................38

3 iVE Tutorial.............................................................................................................39

4 Actual iVE Results...................................................................................................40

5 A Least Relevant isualization and Associated URL Document..............................42

6 A Median Relevant Visualization and Associated URL Document........................43

7 A Most Relevant Visualization and Associated URL Document............................45

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Chapter 1

Introduction

Introduction of the Problem

Information visualization is the concept of shifting the interpretation of

information from the cognitive domain to the perceptual domain. It is similar to the idea

that "a picture is worth a thousand words".

Information visualization can occur in any of the phases of the information

retrieval process. Visualizations of the information retrieval process can be divided into

four areas:

1. The information environment can be visualized. This could be what is seen on the

standard desktop of a personal computer. This could be as simple as the icons displayed

on the desktop.

2. The information space itself can be visualized. For example, if a user has a set of

digitized journals on his/her personal computer, the information space could be

graphically visualized as a set of books on a bookshelf.

3. Navigating through the information space can be visualized. For example, this could

be done by scrolling left and right and up and down through the virtual bookshelf.

4. The resulting information units can be visualized. For example this could be done by

a virtual list of journals produced by a virtual librarian.

However a problem exists, how does a user know which journal is the most

relevant?

Motivation

As stated in the previous section, information visualization can be implemented

during the many phases of the information retrieval process. The focus of this thesis is in

applying information visualization to a collection of resulting information units (i.e.

collection of papers) to determine which information unit is the most relevant.

This can be a very time consuming process if the user is using a “pick and see

method” on the World Wide Web (WWW). However, by shifting the interpretation of

information to the perceptual domain a user can save a significant amount of time when

searching for relevant information.

Solving the Problem

The goal of this thesis, in terms of image processing and neural networks, is to

create a feature vector visualization to be inputted into the human neural network. The

feature vector visualization created is a graphical image of an information unit and will

2

give the user the means to quickly determine relevancy. The application developed to

create this feature vector visualization is called the Information Visualization Engine

(iVE, pronounced "ivy"). This feature vector visualization is based on a basic

understanding of human psychology/physiology and the concept of good GUI design.

Result Summary

The results are from an evaluation conducted on iVE and are contained in Section

5. The evaluation consisted of observations of participants using iVE and of statistical

information resulting from participants filling out an evaluation form. The evaluation

encompasses two categories of experiments. One category is Usability evaluation of a

tool and the other is Controlled experiments of comparing two or more tools (see Section

5).

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Chapter 2

Background Research

The following is a list of relevant papers and a synopsis of their content.

Value Bars

An information visualization and navigation tool of multi-attribute listings,

Richard Chimera1

A Value Bar displays information in the form of a long thin vertical bar. This bar

is sectioned, with each section corresponding to some unit of information (i.e. a file, a

document, etc.). The height/size of each section represents a weight with respect to some

attribute.

For example, if a user had a list of files and was interested in the size attribute of

the file, this tool would create a long thin vertical bar next to the list of files. The

size/height of each section that makes up this thin vertical bar would represent the length

attribute weight for the corresponding file.

Information visualization using 3D interactive animation

George G. Robertson, Stuart K. Card, and Jock D. Mackinlay2

Information visualization is the concept of creating visual objects to more easily

retrieve and assimilate information, thereby shifting the interpretation of information to

the perceptual domain. The whole process from initially retrieving the information to

attaining the specific information was discussed. The four main areas to accomplishing

this goal (on a computer) were:

1. Create a large workspace: create more screen space by using a concept of rooms and

a denser screen space by using animation and 3D.

2. Offload work to agents: querying/searching using search agents, organizing using

clustering agents, and interacting using interactive objects.

3. Maximize real-time interaction rates: rapid interaction using a cognitive coprocessor

scheduler and governor to tune the system for the human perceptual system.

4. Visually abstract information to speed pattern detection: information visualizations

using concepts of hierarchical structure (i.e. cone tree), linear structure (i.e. perspective

wall), continuous data (i.e. data sculpture), and spatial data (i.e. office floor plan).

5

TileBars

Visualization of terms distribution information in full-length document access,

Marti A. Hearst 3

The TileBar is a horizontal bar containing tiles that is used to show the features of a

document. The shade, location, and size of these tiles represent the frequency of the

desired search string. The tiles themselves represent a neighborhood of words such as a

paragraph of words or a chapter of words. The overall length of this bar represents the

relative length of the document. The TileBar is specifically designed for text documents

and focuses on items such as term frequency, term distribution, and subtopic boundaries.

Cone Trees

Animated 3D visualizations of hierarchical information, George G. Robertson,

Jock D. Mackinlay, and Stuart K. Card 4

The Cone Tree is a visualization tool that presents large information spaces in a

picture-like manner. The idea is to take advantage of the human perceptual system for

recognizing patterns in the data.

Cone Trees represent hierarchical information in the form of a root node that has

leaf nodes. Leaf nodes with the same root node form a ring, resulting in an overall shape

of a cone. In addition, each leaf node in the ring may have its own ring of leaf nodes.

The Cone Tree uses colors, shading, the third dimension, and movement (i.e. rotating the

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tree) to shift some of the cognitive load to the human perceptual system. This design also

allows more data to be displayed on a two dimensional monitor.

The Perspective Wall

Detail and context smoothly integrated, Jock D. Mackinlay, George G. Robertson,

and Stuart K. Card 5

The Perspective Wall efficiently deals with large information spaces. It takes into

account the biological characteristics of the human eye. There is a region of the eye that

perceives details and is in focus while the surrounding area is unfocused and has low

resolution.

One can imagine the Perspective Wall as a scroll of paper that is stretched over

the face of a box. The section of paper on the face of the box is in focus. The remaining

sections on each side of the box fade off to focal points in the backdrop. By using a

mouse or some other device, different parts of the scroll can be positioned onto the face

of the box. This scrolling action is done in a smooth and consistent fashion so that object

constancy remains intact.

Some linear feature of the information is used to organize the information on the

wall. For example, a set of documents with timestamps/dates could be organized so that

the oldest documents are on the far left end of the wall and the most current documents

are on the far right end of the wall.

7

Another reason for this design was the limited space on a computer monitor. By

having the less focused left and right sides of the wall fade off into the distance, a large

amount of information can be displayed. Information objects also remain in context with

the rest of the information space. The vertical direction can be used to display

hierarchical information.

Generalized Fisheye Views

Generalized Fisheye Views, George W. Furnas 6

'Fisheye Views' is a viewing strategy dealing with large information spaces. The

concept is to show the closest area (i.e. are of interest) of the information space in great

detail while at the same time keeping a sense of its relation to the whole global

information space.

This Fisheye View occurs naturally to humans. Examples of this natural

occurrence are:

- Newspaper articles focus on many local stories where the only 'distant stories' are the

ones of greater importance.

- In the management domain, people know with some detail who are their immediate

section heads and managers but very little of other departments.

Fisheye Views are naturally occurring so it seemed appropriate to apply this idea

to large information spaces. To formalize this concept a "Degree of Interest" function

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was created, which is based on "a priori" importance and distance. The resultant of this

function would determine the most interesting points that would need to be displayed.

The results of the Fisheye View were successful when compared to a flat view of

an information space. One example cited involved viewing source code. When

compared to a flat view the Fisheye View was more informative. In the Fisheye View

superfluous lines of code were removed, resulting in significant contextual information.

Only the significant 'while', 'if', and 'switch' statements of the algorithm surrounding the

source code lines were shown.

SemNet

Three-dimensional graphic representations of large knowledge bases, Kim M.

Fairchild, Steven E. Poltrock, George W. Furnas 7

SemNet is a 3 dimensional graphical user interface designed to help people

interact with large information spaces. It is hypothesized that three items must be

recognized for a user to comprehend an information space:

1. Identities of individual elements in the knowledge base.

2. Relative position of an element within a network context.

3. Explicit relationships between elements.

SemNet uses a 3 dimensional directed graph to visually represent a knowledge

base. SemNet positions the knowledge elements of a knowledge base using mapping

functions (i.e. heuristics, multidimensional scaling) specific to the knowledge base and

9

connects these elements using colored arcs. It is also possible for knowledge elements to

be positioned by the user if the user has information not contained in the knowledge base.

The result is a grouping of knowledge elements with arcs between them. SemNet also

uses a "generalized fisheye view" when there are too many arcs and knowledge elements

to be comprehensible.

To navigate through the knowledge base five methods were studied.

1. Relative movement: as a helicopter navigating through a landscape.

2. Absolute movement: having an overall map of the knowledge base.

3. Teleportation: immediately being able to go back to a position in the knowledge base

previously visited.

4. Hyperspace movement: moving between knowledge elements by a certain attribute.

5. Moving in space: moving the knowledge base as a whole rather than navigating

through it.

Debugging the knowledge base was performed by tracing a query through the arcs

and knowledge elements to verify their relation.

Database Navigation

An office environment for the professional, Robert Spence, Mark Apperly 8

Professional people spend much of their time working with information. The two

main tasks dealing with information handling are:

1. Specification of the information.

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2. Retrieval of the information item.

A proposed computer system design, called "Office of the Professional", was

researched with the goal to help the professional in information handling. The design of

the "Office of the Professional" took into account the following human factors:

1. Lack of enthusiasm to learn a complex language.

2. Appearance of their personal office.

3. Limitations set by human memory.

4. Highly developed spatial memory in humans.

5. Highly developed "search by visual scan" in humans.

A system consisting of virtual office objects was created. Besides obvious objects

such as a calculator and an in-basket, a complex index system was developed. This index

system (e.g. an index system for a set of journals) consisted of a bifocal display.

The bifocal display was divided into a section of focus in the middle of the display

surrounded by a section of less focus. The overall idea is similar to the "Generalized

Fisheye View" where the items of interest are more detailed than the surrounding items.

Higher zoom capabilities were also available depending on the type of information space

that was being navigated. A point and touch system for selecting the information items

was also studied since this was the most intuitive form of selecting items.

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The Information Visualizer

A 3D user interface for information retrieval, J. D. Mackinlay, G. G. Robertson,

S. K. Card 9

The Information Visualizer is a user interface that addresses the overall process of

acquiring relevant information. In addition to the main goal of information retrieval, the

cost of information from secondary storage to immediate use was also taken into account.

The three main components of the Information Visualizer are:

1. 3D/Rooms: These rooms accommodate 'Locality of Reference' and clustering,

resulting in larger and denser immediate information storage.

2. The Cognitive Coprocessor: An animated user interface architecture. This user

interface creates an animated view of the virtual information world and handles items

related to human physiology and psychology (i.e. perceptual processing time constant,

immediate response time constant, object constancy…).

3. Information Visualizations: Structure-oriented browsers for using different sets of

information; Cone Trees for hierarchical information and the Perspective Wall for linear

information.

InfoCrystal

A visual tool for information retrieval and management, Anselm Spoerri 10

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The InfoCrystal is an information visualization tool that attempts to show the

connection between different concepts in an information space. The InfoCrystal is based

on the concept of Venn Diagrams and uses shaped objects, colors, and location of objects

to represent the information space.

For example, assume there are four documents and three concepts of interest. Let

us also assume only one of the documents contains all three concepts. The InfoCrystal

would have an external shape of a triangle with each vertex representing a different

concept. Inside the external triangle would be four geometric shapes (i.e. circle, square,

rectangle, pentagon) corresponding to the four documents. The geometric shape

representing the document containing all three concepts would be mapped to the center of

the external triangle. The other geometric shapes would be pulled toward one vertex or

the other depending on how much of each concept it contained.

In connection to a Venn diagram, the center of the InfoCrystal would correspond to

the area where all three (concept) circles intersect.

Summary and Comparison

The visualization tools, mentioned in the above papers, fall into three categories.

The first category, which the bulk of the tools fall under (2.2 Information visualization

using 3D interactive animation, 2.4 Cone Trees, 2.5 The Perspective Wall, 2.6

Generalized Fisheye Views, 2.7 SemNet, 2.8 Database Navigation, and 2.9 The

Information Visualizer) are at a different scope than iVE. These category 1 tools deal

13

with the overall information space and are concerned with issues such as how to navigate

and organize the information space. In contrast, iVE is only concerned with information

units once the user gets to the area of interest in the information space.

The second category of tools, 2.10 InfoCrystal, is concerned with multiple

concepts. iVE on the other hand is concerned with the comparison and relevancy of

information units under one concept.

The third category of tools, 2.1 Value Bars and 2.3 TileBars, is concerned with

the comparison of information units and is the category which iVE falls under as well.

Though these two tools allow comparison of information units they did not seem to take

into account the properties of light, the human visual system, or general concepts of good

GUI design. These tools also appeared to visualize only one attribute or feature. Value

Bars is concerned with file size and TileBars is concerned with word

frequency/distribution. The domain iVE focuses on is concept relevancy, meaning that

the user is able to determine which document is most relevant in reference to some topic

of interest. In addition, visualization items such as color, shape, and labeling were not

used in the visualizations of these other two tools. The use and reasoning of these

visualization items in iVE is further explained in Section 3. This being the case, iVE has

incorporated very little, if any, feature(s) from any of these other tools.

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Chapter 3

Information Visualization Engine (iVE)

iVE, a User-Friendly Approach to Low-level Information Visualization

Information visualization can be applied to any or all of the phases of the

information retrieval process. The information world where this information retrieval

process occurs can be thought of as consisting of 4 areas. The first is the information

environment, the second is the information space, the third is the navigation through the

information space, and fourth is the display of the information units.

To further clarify these 4 areas of the information world, the following two

examples are given:

Example 1: Journals on a Perspective Wall:

The first area, the information environment, is visualized by what is seen on the

standard desktop on a personal computer. In the case of the Perspective Wall, this could

be as simple as an icon amongst many other icons on the desktop.

The second area, the body of data, is visualized by the Perspective Wall. The

Perspective Wall can be thought of as a bookshelf, where journals on the bookshelf are

categorized by date, going from left to right, the oldest journals on the left end and the

most current journal on the right end. Journals on the same vertical would have

approximately the same date.

The third area, the navigation through the Perspective Wall, is a visualization of

the method used to go to different sections of the Perspective Wall. In the case of the

Perspective Wall, the wall itself scrolls by from left to right or right to left depending on

which section is desired to be viewed.

The fourth area, the display of the information units, is visualized by the journals

sitting on this wall.

Example 2: Documents on the World Wide Web:

The first area, the information environment, is visualized by what is seen on the

standard desktop on a personal computer. In the case of the World Wide Web, this is an

icon amongst many other icons on the desktop.

The second area, the body of data, is not really visualized in the WWW, it is just

understood that a user can access a large nebulous body of data.

The third area, the navigation through the WWW, is visualized by the GUI

(Graphical User Interface) of the web browser.

The fourth area, the display of the information units, is visualized by the listing of

URLs resulting from your web search.

But the problem exists that after a set of journals or a set of URLs is collected,

which of the journals or URLs is the most relevant? iVE resolves this problem.

16

Components of iVE

iVE can be thought of as consisting of two major parts:

1. The information visualization design.

2. The program/algorithm.

The information visualization design is the design of the visualization that

represents the information unit. The program/algorithm is the code that produces this

visualization.

The Information Visualization Design

The visualization design of iVE was created by taking into account three areas of

study:

1. Low-Level components of the human visual system.

2. High-Level components of the human visual system.

3. GUI design.

Low-Level components of the human visual system are the characteristics and

properties of light and the biological components of the human visual system that react to

light11.

High-Level components of the human visual system are considered as the

cognitive processes involved in "seeing" this light. This involves how the mind interprets

what is seen and how we think about what is being seen. This level relates to the

17

psychology of the mind and how we learn, amongst other things.

GUI design is the design features in creating a GUI that contains the visualization

and takes into account the High and Low Level components.

Low-Level Vision

iVE has taken into account the various characteristics of light and how the

biological components of the human visualization system react to light.

The receptors of the human visualization system consist of rods and cones. The

cones of the human eye are what allow people to see color and allow people to determine

the overall brightness of images. But these cones are not equally sensitive to all the

colors. Cones are more sensitive to red and less sensitive to blue. This being the case,

red objects appear closer than blue objects and draw more attention12. iVE therefore uses

red to represent the most pertinent parameter in its visualization. In addition iVE uses

blue to represent the least pertinent parameter in its visualization and colors between red

and blue to represent parameters of intermediate importance.

Due to the structure of the human eye only a small part of the image people see is

in focus and in high resolution. The cones of the human eye are concentrated in the part

of the eye called the fovea which is near the optic nerve. The part of the image that falls

on the fovea is therefore the part of the image in focus and in high resolution. iVE has

taken this into account and has created an image whose size is small enough to be in

focus at a single glance.

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High-Level Vision

iVE has taken into account the many aspects and theories of high-level vision.

High-level vision, as stated above, is concerned with how the human visual system works

with the mind, such as memory, learning, and perception.

The human memory system can divided into 3 areas13. The first area is the

sensory buffer area which stores information input from the senses, such as vision and

touch. Information is retained in this area of memory for about 2 seconds. The second

area is short-term memory or working memory. Information is retained in this area for

about 18 to 20 seconds unless it is purposely rehearsed. The third area is long-term

memory which can last a lifetime. iVE is concerned with the sensory buffer area and

short-term/working memory and their respective theories.

MILLER'S THEORY

Miller's theory is that short-term/working memory works best with 7 (+/-) 2 small

chunks of data14.

For instance it is easier to retain

(617) 459 -1223

than

6174591223

and it is easier to retain

DEC IBM GMC

than

DECIBMGMC

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Taken this into account, iVE only displays 4 parameters/features of an

information unit.

NUMBER OF COLORS

The use of color was already explained in Low-Level Vision Section but it

appears from the high-level vision point of view it is best to limit the number of colors to

five15. iVE basically uses only four colors, one for each parameter/feature.

FITT'S LAW

Fitt's Law states that the speed at which a person can maneuver to different parts

of an image is based on distance, size and the relative precision of the objects in the

image. This being the case it is quicker to maneuver to objects positioned in a circular /

perimeter orientation than objects displayed in a list fashion16. Taking into account the

future enhancements of iVE, where a user may select/click on one of the feature dots of

iVE to bring up a similar image of sub-features, iVE uses a circular image.

BRAIN POWER

Vision accounts for approximately 40% of brain functioning. This functioning

has yet to be rivaled or duplicated by anything man-made17. This is probably the reason

why people are naturally and implicitly visually perceptive creatures. Because of this

processing power, people are able to pick out and see shapes extremely well. Examples

of this are their ability to recognize faces and the ability to see and pick out objects in a

cluttered background18. iVE uses this important aspect of the human visual system by the

fact that the overall shape/pattern, the shape/pattern created by the feature dots, is to be

used by the user when comparing the resulting visualizations. The rule of thumb for the

visualization is that if all the feature dots reside on the delimiting circle of the

20

visualization then this image and its corresponding information unit is the most relevant

document.

GUI Design

iVE has also taken into account several general features related to good GUI

design19:

First the background color of any GUI should not distract the user or be hard on

the user’s eyes. In this case, boring is good. iVE therefore has a gray background for its

GUIs20.

Second, since iVE is based on the concepts described in the High-Level Vision

Section and the Low-Level Vision Section, iVE is intuitive and easy to interpret, which is

another quality of a good GUI design.

Third, a straight forward tutorial is provided to the user so the user can be up and

running quickly.

Fourth, iVE has pertinent use of color by taking into account the various aspects

of Low-Level Vision and High-Level Vision.

Fifth, iVE provides reasonable feedback by providing a window that allows the

user to see the data that iVE is processing.

And sixth, since not all people are able to see in color, labels have been used as

secondary indicators.

Though GUI design is a bit of an art form, iVE implements most of the general

21

features for a good GUI design.

22

iVE GUI Architecture

The iVE GUI architecture consists of 3 levels shown on the following page.

When the application is executed, the first level of GUIs appears. There are two Level 1

GUIs: the Main GUI which accepts the user’s search phrase, and an accompanying

DOS/Perl shell GUI which displays the data as it is being processed along with any

warnings and errors.

After processing has completed the second level GUI appears. This Level 2 GUI

displays the visualization results as well as corresponding URL buttons. The user is then

free to select any of these URL buttons to bring up the third level of GUIs.

The Level 3 GUIs consists of the URL documents brought up in the browser.

23

Figure 1 iVE GUI Architecture

Level 1b: DOS/Perl shell, accompanies the

Main GUI, displays data/warnings while

processing.

Level 1a: Main GUI used for

entering the search phrase.

Level 2: iVE visualization results GUI, displays

visualizations and active URL buttons after processing is

completed. Active buttons are used to look at the actual

documents.

Level 3: URL documents displayed by clicking on the active URL buttons from the

iVE visualization results GUI.

24

The Program/Algorithms

Language Selection, Software Architecture, Software Setup

iVE was written in PERL because of its built in features for extracting URL

documents (i.e. user agents) and its ability to pattern-match. Perl also works relatively

well on a Microsoft Windows platform. In addition Perl/Tk provides the utilities for

creating the GUIs and the information visualizations.

The experimental setup is a personal computer system running with a Windows

Operating System (Windows 98 or higher) that is connected to the Internet. The software

must be run in a directory that contains a Windows Explorer executable.

The software is very flat and sequential. The approximate size of this algorithm is

2MB. The flow of the software is strictly sequential and has a polynomial time

complexity of O(n). A generalized view of the flow can be seen in the following

diagram.

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SOFTWARE FLOW

Query and search the WWW for potentially relevant information units

Extract features for each information unit

Calculate the visual parameters for each information unit

Collect the information units for feature extraction

Present the visualizations in a user-friendly GUI for the user to access

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Software Narrative

When the application is first executed, a GUI appears to accept search terms.

Once the search terms are entered and the Search button is selected, a user agent (a built-

in Perl object) connects to a search engine on the WWW. The user agent then submits

the search terms to the search engine and retrieves the resultant URL pages. The URL

pages are then stored in a data structure on the local computer.

Using Perl's pattern matching utilities, search words are tagged in each resultant

URL page. A visualization is then created using these tagged words in combination with

three other features of the document. The four features of the visualization are:

1. search phrases:

This feature is the total number of search phrases in the URL document. To be counted

as a search phrase all words of the search phrase must exist within a certain neighborhood

of words.

2. search words:

This feature is the sum total of each of search word in the document.

3. links:

This feature is the total number of relevant WWW links in the document. To be counted

as a relevant link, at least one of the search words must be in the link.

4. words:

This feature is total number of all words in the document.

27

Pseudo-Code

The pseudo-code for iVE is as follows and is sectioned by the modules of

the code:

Main

{

create gui for user to enter search terms and view the tutorial;

call driver subprogram;

loop;

}

The driver subprogram below calls all the necessary subprograms used in

creating the information visualization images. Each of the subprograms is further

described following this driver subprogram.

sub driver

{

do_search;

get_urls;

get_htmls;

initialize_parameter_image_hashed;

initialize_image_parameters;

splitting_out_keywords;

no_tags;

analysis_num_words;

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analysis_search_word_count;

analysis_num_phrases;

analysis_num_links;

find_max_parameter_value;

create_rations;

trouble_shoot;

another_gui;

}

Pseudo-code/description of each module called by the driver subprogram:

sub do_search

{

create user agent;

connect to WWW;

submit search words to search engine;

collect results of search;

}

sub get_urls

{

extract pertinent URLs from the collected results;

}

sub get_htmls

{

29

have the user agent retrieve each extracted URLs document;

}

sub initialize_parameter_image_hashes

{

initial hash tables used in creating the image;

}

sub initialize_image_parameters

{

initialize parameters for creating the image;

}

sub splitting_out_keywords

{

put each search word in its own index of an array;

determine the number of search words entered by the user;

}

sub no_tags

{

filter out all html tags, leaving only document content;

}

sub analysis_num_words

{

determine the number of words in each html document;

}

30

sub analysis_search_word_count

{

determine the number of search words in each html document;

}

sub analysis_num_phrases

{

determine the number of phrases in each html document;

//all search words taken together are considered a phrase

}

sub analysis_num_links

{

determine the number of related url links in each html document;

}

sub find_max_parameter_value

{

determine the maximum value of each feature;

//will be used in creating images that are relative to each other.

}

sub create_ratios

{

for each document determine the percentage of each feature relative to the max value of

each feature;

//again, these values are used when creating each image so that all images are relative

31

//to each other.

}

sub troubeshoot

{

output each filtered document to a file as well as the corresponding features, for use in

troubleshooting;

}

sub another_gui

{

creates the visualization for each document as well as an active link to each document;

}

sub tutorial_gui

{

creates the tutorial gui;

//activated when the user clicks on the corresponding button.

}

Algorithm/Modules of Interest

The only module of interest is the "analysis_num_phrases". All the other

modules contain standard algorithms that use standard data and control structures.

"analysis_num_phrases" pseudocode

32

The following algorithm determines the number of search phrase occurrences in a

document and uses a technique similar to convolution in image processing. Each

document can be thought of as a 1-Dimensional image, each word in the document can be

thought of as a pixel (i.e. word pixel).

First, each word of the document is stored in documentWordArray[ ] and each

word of the search phrase is stored in searchWordArray[ ]. Corresponding to the

documentWordArray[ ] is the documentWordTagArray[ ], which is used to store a

number tag for each word. This array is initialized to all zeroes. Corresponding to the

searchWordArray[ ] is the uniqueNumberArray[1, 3, 5, 7, 17], which contains the

number tags used when a search words are found in the documentWordArray[ ]. The

list of numbers in uniqueNumberArray was chosen due to their unique summed value.

Currently this algorithm can accept a search phrase of up to five words, corresponding to

the five numbers in the uniqueNumberArray[ ].

Next the phraseDeterminer is calculated by summing up the unique numbers

corresponding to the search words. For example if the search phrase is climbing1 mount3

everest5, the phraseDeterminer is 1+3+5 = 9.

The neighborhood is then calculated, which is numberOfSearchWords + 3.

This equation is based on the idea that sometimes filler words like to and and can be

interjected between the search words. In addition, a neighborhood greater than 5 words,

corresponding to a search phrase of two words, can run the risk of a repeat search word

being found in the neighborhood, resulting in a missed a search phrase (see next

paragraph).

33

Last, the number of search phrase occurrences is determined. Starting at the

beginning of documentWordTagArray[ ] a neighborhood of contiguous indexes is

summed. This summed value is compared to the phraseDeterminer and if equal the

numberOfPhrases for the document is incremented. This neighborhood is then shifted

one array location right (toward the end of the array) and the process is repeated until the

end of the documentWordTagArray[ ] is reached.

//analysis_num_phrases

uniqueNumberArray = [1, 3, 5, 7, 17]

-put each word of the Search Phrase into searchWordArray[ ]

-put each word of the document in documentWordArray[ ]

-initialize the corresponding documentWordTagArray[ ] to contain zeroes

for i = 0, i <= numberWordsInDocument, i = i+1

for j = 0, j <= numberOfSearchWords, j = j+1

if documentWordArray[i] = searchWordArray[j]

documentWordTagArray[i] = uniqueNumberArray[j]

end if

end for

end for

//neighborhood is the range of indices of documentWordTagArray[ ]

//to search for a phrase

neighborhood = numberOfSearchWords + 3

for k = 0, k <= numberOfSearchWords, k=k+1

//phraseDeterminer is the value used to determine if a phrase

34

//has been found in a neighborhood of words

phraseDeterminer = phraseDeterminer + uniqueNumberArray[k]

end for

//determine the number of phrases

for m = 0, m <= numberOfWordsInDocument, m = m+1

if documentWordTagArray[m] not equal 0

for n = m, n <= neighborhood + m, n = n+1

phraseSum = phraseSum + documentWordTagArray[n]

end for

if phraseSum = phraseDeterminer

numberOfPhrases = numberOfPhrases + 1

end if

end if

end for

35

Chapter 4

iVE Visualizations

iVE Visualization Explanations

Figure 2 Main GUI

Figure 2 is the Main GUI that appears once the iVE application is started.

Figure 3 iVE Tutorial

Figure 3 is the Tutorial GUI that appears when the Tutorial Button in the Main

GUI is clicked. The information in this GUI should be read before submitting a search in

order to understand the results of the iVE visualization.

Figure 4 Actual iVE Results

This figure illustrates the results of an actual iVE query. This particular GUI was

the result of entering the search phrase machu picchu hiking. There is a large amount of

perceptual information. The perceptual information is quite evident; look for the result

that has the most dots on or near the outer circle. There is also significant cognitive

information upon further examination. The cognitive information can be perceived by

examining which type of dot is closest to the outer circle (each dot represents a different

feature of the document as stated in the Figure 1 iVE Tutorial).

Figures 5 through 7, Least, Median, and Most Relevant iVE Results and their

corresponding URL documents, respectively:

The next set of figures shows what iVE considers the least relevant URL

document (figure 5), the median relevant URL document (figure 6), and the most relevant

URL documents (figure 7). The individual visualizations were cropped from figure 4

Actual iVE Results. The URL document below the visualization is the corresponding

URL document activated by clicking on the associated URL button. There is some

subjectivity as to exactly which of most relevant documents is the most relevant, but

there is a definite objective difference between the least relevant documents and the most

relevant documents.

37

Figure 2 iVE Main GUI, appears when iVE application is started

38

Figure 3 iVE Tutorial, activated by the Tutorial Button in the Main GUI

39

Figure 4 Actual iVE Results, resulting from entering the search term machu picchu hiking in the Main GUI

40

Figure 4 cont.

41

Figure 5 A Least Relevant Visualization and Associated URL Document, this visualization is cropped from Figure 4 Actual iVE Results, third from the bottom

42

Figure 6 A Median Relevant Visualization and Associated URL Document, this visualization is cropped from Figure 4 Actual iVE Results, sixth from the top

43

Figure 6 cont.

44

Figure 7 A Most Relevant Visualization and Associated URL Document, this visualization is cropped from Figure 4 Actual iVE Results, second from the bottom

45

Figure 7 cont.

46

Figure 7 cont.

47

Figure 7 cont.

48

Figure 7 cont.

49

Chapter 5

iVE Evaluation

Usability Evaluation Design Background

Usability evaluation for GUIs and websites are somewhat standardized. GUIs and

websites are usually evaluated in regard to some task being performed. For example

people intending or planning to buy a book first search for a book, review the results of

the search, and then select and purchase the book. Each of these actions can have many

levels or steps.

Looking at just the purchasing step, a typical process is for the customer to first

select a book, add it to some sort of shopping cart, check out, go to a purchasing form,

and then confirm the purchase. There is usually a separate GUI associated with each of

these sub-steps. As you can see, from the viewpoint of website/GUI design, the overall

accomplishment of a task is quite involved.

In this task based domain the items usually evaluated are: ease of navigating

through the website, ability to redo/undo actions, smooth flow from GUI to GUI,

knowledge of relative location within the website21 etc. However, in regards to

information visualization, there is currently no standardized usability testing22 and

usability testing in this domain is considered an ad-hoc process23. This is due to several

reasons. First information visualization is still a very young area of research. During the

years of 1995-1997 only 6% of the papers about information visualization dealt with

usability testing24. Also how a person interprets a visualization relies on factors such as

their socio-demographic profile, cognitive abilities, competency in understanding the

visualization, knowledge base25, and amount of training26, etc. Trying to take all these

items into account in the form of a standardized usability test is a daunting task and has

yet to be done.

Although there is no standardized usability testing for information visualization,

some papers have categorized and structured testing into certain groups.

One paper has categorized usability testing for information visualization into 3 main

groups27.

1. visual representation usability, referring to the expressiveness and quality of the

resulting image.

2. interface usability, related to the set of interaction mechanisms provided to users

so they can interact with the data through the visualization

3. data usability, devoted mainly to the quality of data for supporting users’ tasks

In addition a recent paper has broken down testing of information visualization into 4

types of experiments/studies28:

1. Controlled experiments comparing design elements such as specific widgets in a

GUI such as scroll bars or alpha sliders or types of buttons, etc.

2. Usability evaluation of a tool by itself where feedback is provided by users.

3. Controlled experiments consisting of comparing two or more tools such as

comparing types of tree visualization tools.

51

4. Case studies of tools in a realistic setting where users are able to spend a lot of

time with a tool in an actual environment of use (i.e. not just in a lab) and then

give feedback.

There are also some general usability testing concepts:

First tests should be made simple enough to be accomplished is a short period of time

and the items or questions in the test need to be specific enough to measure performance.

The same input data should be used for each evaluation to provide some uniformity and

standardization29 30.

Second, a captive audience should be used31. In general an online survey form is not

a good idea since people will sometimes fill out the survey before using the website/GUI

or, after using the website/GUI, will often leave the website/GUI without filling out the

survey.

Third the evaluator performing the test should watch what the people actually do

rather than rely on their feedback after the test32. This is because people in general will

tend to bend the truth toward what they think the evaluator wants to hear. Also in telling

the evaluator what they did people are actually telling the evaluator what they remember

doing. Finally people will rationalize their actions when all that is known for certain is

what they actually did.

Taking all the above factors into account any statistical results from a usability test

should be taken with caution.

It should also be noted that iVE focuses on a very specific and narrow segment of the

information retrieval task, it is in a sense a one-two punch in contrast with a whole 15-

round fight of the information retrieval task.

52

Evaluation Design

Taking all these factors into account a usability test for iVE has been tailored.

First no question is directly asked to participants in the evaluation. Second, observations

are recorded on how the participants respond and interact with the visualization tools.

And third an evaluation sheet is given to the participants after they have used the tool.

The evaluation questions consist of a combination of two experiment categories, one

category is the usability evaluation of the tool and the other category is a controlled

experiment of comparing iVE to another tool (www.kartoo.com). All users give each

tool the same input and there are only 10 questions on the evaluation sheet to keep the

evaluation short and hopefully concise.

53

Evaluation Results

The actual evaluation form can be seen in the next section. The statistical data

collected between iVE and kartoo is listed below:

Rating Scale: 5 – good / works well……………..1 – needs work

iVE kartoo

1. Tutorial: 4.00 4.42

2. Visual Organization: 4.00 4.54

3. Meaningful Use of Color: 3.82 4.00

4. Data Interaction: 3.64 4.64

5. Learning Curve: 3.91 4.63

6. Visualization Content: 3.36 4.55

7. Visualization Comparison: 3.81 3.72

8. Use in Other Domains: 3.72 4.09

9. Speed of Use: 3.63 3.91

10. Overall Functionality: 3.91 3.91

The were 11 participants in this study and all the participants were college

students.

As you can tell from the statistical data, the participants preferred kartoo in almost

every category, but there appears to be a contradiction between liking the tool versus the

functionality of the tool. This is evident by the ‘10. Overall Functionality’ rating of iVE

and kartoo being equal. The other category where participants preferred iVE was ‘7.

Visualization Comparison’. Both of these categories are more functional in nature.

54

The general comments noted about kartoo were: “…it was more interactive”, “…

the background coloring is better”, “…it was more fun to use” (because of its animation).

The general comments noted about iVE were: “…it was more old-school” and “…it was

more precise”.

Overall, in this demographic, the entertainment factor appears to have been a

determining factor in rating a visualization tool. And again it should be noted that

statistical information about information visualization testing should be taken with

caution.

Observations of the participants using iVE revealed the following functional

deficiencies: the scrolling resolution was too course, the tutorial would benefit with some

simple pictorial comparisons at the beginning, participants often hit the enter key on the

keyboard before having to click on the submit button, and the participants preferred

having all the visualizations visible in one window instead of having to scroll in the

window to see all the visualizations.

55

Evaluation Form

Comparison / Usability Evaluation

RATING SCALE: 5 – GOOD / WORKS WELL……………..1 – NEEDS WORK

1. Tutorial: Was the tutorial or help file understandable / informative20p229?

iVE Rating: __________ kartoo Rating: __________

2. Visual Organization: Were the visualizations presented in an orderly manner and were the visualizations evident (one visualization did not hide or obscure the another) 18slide10?


3. Meaningful Use of Color: Did the colors help in interpreting/examining the visualization 18slide11?


4. Data Interaction: Were you able to examine/interact with the data the visualization represented 15section1para4?


5. Learning Curve: Was it easy to learn how to use the visualization tool?


56

6. Visualization Content: By just looking at the visualization with no further action did the visualization provide useful characteristics of the document?


7. Visualization Comparison: Did the visualization provide you with a means of comparing on document to another?


8. Use in Other Domains: Based on the design of the visualization could it be applied to other areas, such as determining which photographs are more relevant, or which bacteria are the most relevant (probable cause), or which drugs are the most relevant (remedy for certain conditions)?


9. Speed of Use: Was the image quick to interpret15section 2?


10. Overall Functionality: Did the visualization tool save you time in determining which document was more relevant?


57

Chapter 6

Conclusions and Future Research

Accomplishment

Based on the evaluation study of iVE it appears iVE has accomplished the task of

document relevancy through information visualization. iVE is quick to interpret, and

gives the user meaningful cognitive information. The user is able to quickly select and

view just the most relevant documents.

Problems Faced and Solutions

The following two standardization issues were encountered:

1. One problem encountered is URLs do not have a standardized format: some URLs

end in a '/', some do not, some start with 'www.', some do not …By analyzing the valid

URLs that did not appear in the visualization, it was apparent how the URL differed from

what was already being 'pattern-matched'. To compensate, another pattern was added for

matching (though there are probably more variations).

2. There are no standardized usability tests for information visualization, so though an

evaluation was performed, its statistical results should be viewed with some caution.

Improving iVE

Reviewing the results of the evaluation there are several areas iVE can improve

upon and several areas where there appears to have been some confusion. The apparent

existence of areas of confusion is based on the participants’ comments and actions.

The first items to be covered will be the areas of improvement followed by a

discussion of the areas of confusion. For clarity, each evaluation area is italicized and

followed by the corresponding question number used in the evaluation. In addition many

of these areas are somewhat coupled, and an improvement in one area may improve

another. For example, a better tutorial (1.)could improve the speed of use (9.)as well as

the learning curve (5.).

The tutorial (1.) and learning curve (5.) areas can be improved by putting the

visualization examples at the beginning of the tutorial instead of the end. Only two types

of visualizations should be shown, one visualization representing a relevant document

and the other visualization representing an irrelevant document. Several sets of these

visualizations could be shown to give the user a quick visualization training session.

The visual organization (2.) and speed of use (9.) can be improved by first

discarding the scrollbar paradigm in favor of displaying all the visualizations in one

window. A hierarchical ordering could be designed wherein the most important

visualization would reside on an inner delimiting ring and the visualizations of less

importance would reside on concentric outer delimiting rings. Also, the visualizations

59

near the center of this display would have a localized reddish background to key the

user’s eye in on the more relevant documents. Visualizations residing further from the

center would have colors gradated towards the blue end of the light spectrum to

downplay their relevancy. This design would also be in agreement with Fitt’s Law.

Furthermore, participants did not use the attribute of mentally creating a shape

based on the positions of the feature dots as a relevancy factor. This overall shape could

be made more explicit by enveloping all the indicator dots together in a single outline and

filling all the ‘non-feature dot’ areas within the outline with a color different than the

background. This would result in an image whose shape would be easier to view.

An additional enhancement to iVE, not directly related to the evaluation, would

be to make the visualizations expandable and collapsible so that more cognitive

information would be captured in the visualization. For example, a user would click on

one of the existing a high-level feature dots and this would bring up another visualization

of the same design that breaks down the selected feature into sub-features.

The first area of confusion to be discussed is visualization content (6.). The result

in this area directly conflicts with the area of visualization comparison (7.) where iVE

had a higher rating than kartoo. The question to pose is, “How can an item of less

visualization content produce better visualization comparison?” Due to this confusion an

improvement in this area may not be unnecessary.

The next two areas of confusion are data interaction (4.) and meaningful use of

color (3.). Based on the participants comments such as “…it was more interactive”, “…I

like the blue background color”, “…it was more fun to use”, and “…its just like neurons

firing” and based upon the participants action of moving the cursor around the kartoo

60

visualizations to view the animation, it appears that there was confusion between data

interaction vs. visualization animation/action and between meaningful use of color vs.

aesthetically pleasing color. Again due to this confusion, it may not be necessary to

change iVE in these areas.

In the area of use in other domains (8.) no comments or actions were observed.

But due to the characteristic that ‘if features in other domains are measurable or

quantifiable, iVE can visualize them’ and due to the fact that iVE is already better in the

area of visualization comparison, improvement in this area may not be necessary.

A drawback to iVE is that it has only been tested in the domain of the written word.

iVE should theoretically work in any domain but further testing is needed for

confirmation. A challenge exists in finding meaningful features to extract for the other

domains.

Impact of Research

The impact of this research is that iVE can significantly reduce the time a user

spends searching for relevant information. There could also be the side effect that the

overall shape of the iVE visualization could reveal other qualities/groupings of

information units just like molecules with similar shapes having similar properties.

61

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65

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66

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