Evaluation

and

Evolution

of

a

Browse

and

Search

Interface

Relation

Browser

We

present

in

this

paper

the

design

and

an

evaluation

of

a

novel

interface

called

the

Relation

Browser

-LRB-

RB

-RRB-

for

searching

and

browsing

large

information

collections

RB

provides

visualized

category

overviews

of

an

information

space

and

allows

dynamic

filtering

and

exploration

of

the

result

set

by

tightly

coupling

the

browsing

and

searching

functions

A

user

study

was

conducted

to

compare

the

effectiveness

efficiency

and

user

satisfaction

of

completing

various

types

of

searching

and

browsing

using

the

RB

interface

and

a

traditional

form-fillin

interface

for

a

video

library

An

exploration

set

of

tasks

was

also

included

to

examine

the

effectiveness

of

and

user

satisfaction

with

the

RB

when

applied

to

a

large

federal

statistics

website

The

comparison

study

strongly

supported

that

RB

was

more

effective

efficient

and

satisfying

for

completing

data

exploration

tasks

Based

on

the

results

efforts

to

automatically

populate

the

underlying

database

using

machine

learning

techniques

are

underway

Preliminary

implementations

for

two

large-scale

federal

statistical

websites

have

been

installed

on

government

servers

for

internal

evaluation

H.

-LSB-

Information

Interfaces

and

presentation

-LRB-

e.g.

HCI

-RRB-

-RSB-

User

Interface

interaction

style

graphical

user

interfaces

-LRB-

GUI

-RRB-

H.

-LSB-

Information

Storage

and

Retrieval

-RSB-

Information

Search

and

Retrieval

query

formulation

search

process

selection

process

H.

-LSB-

Information

Storage

and

Retrieval

-RSB-

Digital

Libraries

User

issues

Systems

issues

General

Terms

Design

Human

Factors

Experimentation

Performance

The

size

and

breadth

of

large

government

websites

and

digital

libraries

makes

it

difficult

for

people

to

quickly

grasp

what

content

is

and

is

not

available

Dynamic

overviews

and

previews

of

collections

can

help

people

decide

if

it

is

worthwhile

to

look

further

-LSB-

-RSB-

As

they

do

look

further

it

is

helpful

for

their

searching

and

browsing

to

quickly

see

partitions

of

the

collection

and

how

many

items

are

available

in

different

partitions

We

believe

that

government

website

users

will

be

well-served

by

highly

interactive

user

interfaces

that

support

alternative

views

of

the

collections

partitions

and

results

sets

This

paper

describes

a

user

interface

that

aims

to

provide

agile

control

for

browse

and

search

reports

results

from

a

user

study

comparing

this

interface

to

a

typical

WWW

search

interface

and

describes

the

ongoing

evolution

of

the

interface

including

efforts

to

automate

discovery

of

topical

categories

and

assignment

of

webpages

to

those

categories

Faceted

category

structure

is

one

way

to

help

people

understand

the

composition

of

an

information

collection

A

faceted

approach

provides

different

ways

to

slice

and

dice

the

information

space

which

allows

people

to

look

at

the

information

space

from

different

perspectives

Allowing

people

to

explore

the

relationships

among

different

facets

may

further

deepen

their

understanding

and

support

new

insights

The

relation

browser

-LRB-

RB

-RRB-

is

an

interface

which

provides

an

overview

of

the

collection

by

displaying

different

categories

and

enables

people

to

explore

the

relationships

among

these

categories

-LSB-

-RSB-

The

different

facet

values

also

serve

as

selectable

objects

that

may

be

employed

as

query

widgets

for

a

search

so

that

the

entire

space

can

quickly

be

partitioned

with

simple

mouse

moves

and

with

consequent

immediate

display

of

the

resulting

partition

in

the

results

panel

Figure

shows

the

mock-up

interface

of

an

early

version

of

the

relation

browser

in

the

domain

of

U.S.

federal

statistics

websites

The

web

pages

in

the

site

were

sliced

into

four

different

facets

by

topic

data

type

region

and

date

The

numbers

beside

the

bars

indicate

the

number

of

websites

associated

with

the

attributes

By

mousing

over

any

of

the

topics

the

distribution

of

the

specific

topics

in

other

facets

are

visualized

as

graphic

bars

The

underlying

data

for

this

instance

of

the

interface

was

manually

extracted

from

a

small

set

of

webpages

contained

in

more

than

federal

statistical

agency

websites

Permission

to

make

digital

or

hard

copies

of

all

or

part

of

this

work

for

personal

or

classroom

use

is

granted

without

fee

provided

that

copies

are

not

made

or

distributed

for

profit

or

commercial

advantage

and

that

copies

bear

this

notice

and

the

full

citation

on

the

first

page

To

copy

otherwise

or

republish

to

post

on

servers

or

to

redistribute

to

lists

requires

prior

specific

permission

and/or

a

fee

Conference

Month

City

State

Country

Copyright

ACM

Copyright

held

by

the

author

Figure

Relation

Browser

-LRB-

RB

-RRB-

This

early

version

of

the

RB

has

been

redesigned

based

on

user

studies

and

experience

applying

the

interface

to

more

than

a

dozen

different

database

instances

-LSB-

-RSB-

The

new

version

is

called

RB

which

improves

the

RB

significantly

in

several

ways

-LRB-

see

Figure

-RRB-

-LSB-

-RSB-

First

RB

displays

multiple

facets

-LRB-

categories

-RRB-

visually

and

on

the

same

screen

rather

than

only

two

facets

with

tab

options

to

others

The

multiple

facets

provide

an

overview

of

the

information

space

The

facet

values

are

visually

represented

by

graphic

bars

with

different

lengths

which

indicate

the

number

of

items

associated

with

them

Second

RB

allows

more

flexibility

to

explore

relationships

One

of

the

features

of

RB

is

that

you

can

restrict

the

information

items

-LRB-

partition

the

information

space

-RRB-

by

mousing

over

any

bars

and

other

bars

are

proportionally

highlighted

to

show

the

conditional

distribution

across

all

the

facets

Note

that

the

previous

RB

was

limited

to

visualizing

pairwise

relationships

with

one

main

facet

Third

the

RB

added

a

dynamic

filtering

function

for

the

result

set

-LRB-

see

Figure

-RRB-

Once

the

search

results

are

displayed

in

the

table

further

filtering

can

be

done

by

typing

in

keywords

-LRB-

string

patterns

-RRB-

in

the

boxes

located

immediately

above

the

result

fields

The

filtering

is

dynamic

which

means

that

with

each

character

typed

in

or

removed

from

the

boxes

RB

matches

the

string

patterns

in

the

boxes

with

the

corresponding

field

of

the

results

Only

the

matched

results

are

then

displayed

immediately

in

the

results

panel

and

the

matched

string

in

the

results

is

highlighted

This

dynamic

feature

gives

users

instant

and

constant

feedback

about

the

filtered

results

and

how

many

items

they

will

get

with

different

keywords

which

allows

users

to

try

out

different

filtering

keywords

very

easily

and

efficiently

Fourth

the

RB

provides

an

overview

of

the

results

set

and

tightly

couples

the

overview

and

results

set

panels

The

overview

panel

is

dynamically

updated

to

give

users

a

contextualized

overview

of

the

updated

result

set

These

new

features

give

users

more

power

to

understand

and

explore

the

information

collection

and

give

them

a

flexible

and

rapid

way

to

find

the

information

they

want

A

linguistic

model

of

BNF

grammar

to

model

the

user

interaction

with

the

interface

is

provided

in

section

to

help

reveal

the

dynamic

nature

of

the

RB

In

the

paper

we

argue

that

the

RB

interface

will

bring

users

added

values

beyond

simple

searching

and

browsing

by

in

fact

combining

these

search

strategies

seamlessly

In

the

next

section

the

methodology

of

a

user

study

is

described

The

results

of

the

user

study

are

then

presented

and

discussed

Limitations

of

the

interface

and

current

efforts

to

deal

with

data

classification

are

then

described

Figure

Initial

display

of

RB

with

visualized

category

overview

on

the

top

Figure

RB

with

dynamic

filtering

of

the

results

-LRB-

note

the

changes

in

the

overview

and

updated

results

-RRB-

The

purpose

of

the

user

study

was

two-fold

first

we

wanted

to

compare

the

effectiveness

efficiency

and

user

satisfaction

associated

with

completing

certain

tasks

using

RB

against

that

obtained

by

the

traditional

form-fillin

search

interface

-LRB-

baseline

interface

-RRB-

Second

we

wanted

to

explore

if

the

RB

interface

would

lead

to

new

interaction

patterns

with

the

interface

and

if

so

to

determine

what

these

new

interaction

patterns

might

be

Seventeen

undergraduate

and

graduate

students

were

recruited

from

the

UNC-Chapel

Hill

campus

for

this

study

They

came

from

various

schools

and

departments

There

were

females

and

males

with

an

age

range

from

to

-LRB-

were

in

their

and

all

were

familiar

with

www

browsers

The

participants

were

given

for

their

participation

The

data

from

the

first

two

participants

was

used

as

a

pilot

test

based

on

which

the

experimental

protocol

and

instruments

were

revised

The

data

from

the

other

participants

was

used

for

the

data

analysis

The

study

proceeded

in

two

phases

a

within

subjects

comparison

across

two

different

interfaces

for

the

same

database

and

an

exploratory

investigation

with

a

single

government

statistical

website

instance

of

RB

Phase

One

RB

to

Baseline

Comparison

for

a

Film

Database

The

first

phase

was

a

comparison

study

in

which

participants

used

both

the

RB

interface

and

the

baseline

interface

The

order

of

using

these

interfaces

was

counter

balanced

The

domain

of

the

information

items

in

both

interfaces

was

the

video

collection

in

the

UNC-CH

library

-LRB-

y

-RRB-

that

contains

about

films

The

library

online

video

search

interface

-LRB-

Filmfinder

-RRB-

was

used

as

our

baseline

interface

FilmFinder

is

a

fairly

typical

www

form-fillin

search

interface

-LRB-

see

Figure

and

Figure

-RRB-

where

users

can

specify

queries

within

fields

such

as

title

release

year

director

description

genre

origin

and

format

Figure

Filmfinder

with

Form-fillin

Interface

Figure

Results

Page

of

Filmfinder

All

participants

were

run

individually

in

sessions

ranging

from

minutes

and

all

sessions

were

video

taped

The

protocol

for

the

first

phase

was

as

follows

First

a

demographic

pre-test

questionnaire

was

completed

Second

the

participant

was

trained

for

the

first

interface

assigned

in

their

condition

The

training

consisted

of

an

introduction

to

the

features

of

the

interface

a

demo

of

each

type

of

task

with

the

interface

and

participant

practice

using

the

interface

until

s/he

was

comfortable

with

it

Third

the

participant

used

the

interface

to

complete

search

tasks

Tasks

were

assigned

to

participants

one

by

one

by

handing

them

pieces

of

paper

for

each

task

A

timer

was

used

to

count

time

used

to

complete

each

task

except

for

task

-LRB-

see

description

of

task

below

-RRB-

After

each

task

a

short

satisfaction

questionnaire

was

completed

by

the

participant

Fourth

a

usability

questionnaire

was

filled

out

after

the

participant

finished

using

the

first

interface

Next

the

participant

was

trained

for

the

second

interface

and

the

same

procedures

were

used

to

complete

more

search

tasks

Finally

an

open-ended

questionnaire

about

perceived

differences

and

preferences

for

the

two

interfaces

was

completed

The

tasks

were

classified

into

three

different

types

Simple

look

up

task

Tasks

to

in

each

task

set

were

of

this

type

For

example

Check

if

the

movie

titled

The

Matrix

is

in

the

library

movie

collection

Data

exploration

and

analysis

tasks

Tasks

to

in

each

task

set

were

of

this

type

This

kind

of

task

requires

users

to

understand

and

make

sense

of

the

information

collection

which

could

be

a

starting

point

for

them

to

further

their

searching

or

browsing

Two

examples

of

this

type

are

In

which

decade

did

Steven

Spielberg

direct

the

most

movies

and

How

many

movie

titles

does

the

library

hold

that

were

released

in

the

year

Task

was

a

free

exploration

task

which

asked

participants

to

find

five

favorite

videos

without

any

time

constraints

The

tasks

assigned

for

the

two

interfaces

were

different

but

comparable

For

example

the

comparable

tasks

for

two

interfaces

simply

substituted

different

video

titles

or

directors

Phase

Two

Explore

RB

for

EIA

Website

The

second

phase

was

an

exploratory

study

of

the

RB

applied

to

roughly

pages

in

the

Energy

Information

Administration

-LRB-

EIA

-RRB-

website

Based

on

intensive

manual

inspection

of

the

EIA

website

four

facets

were

identified

with

associated

facet

values

fuel

type

-LRB-

with

the

facet

values

alternatives

coal

electricity

natural

gas

nuclear

petroleum

and

renewable

-RRB-

geography

-LRB-

state

level

regional

level

national

level

and

international

level

-RRB-

sector

-LRB-

commercial

electric

utility

industrial

and

residential

-RRB-

and

process

-LRB-

delivery

import/export

price/cost

production

resources/reserves

and

usage

-RRB-

All

the

facets

were

displayed

on

the

overview

panel

-LRB-

see

Figure

-RRB-

The

results

panel

displayed

the

title

page

size

and

description

of

the

web

pages

Figure

RB

interface

applied

to

EIA

website

The

protocol

of

the

second

phase

was

as

follows

First

the

RB

EIA

application

was

introduced

to

the

participant

Second

the

participant

practiced

using

the

interface

until

s/he

was

comfortable

with

it

Third

the

participant

used

the

interface

to

complete

four

tasks

The

process

was

recorded

and

a

short

satisfaction

questionnaire

was

filled

out

after

finishing

each

task

Fourth

an

open-ended

questionnaire

was

completed

after

finishing

all

the

tasks

Lastly

the

participant

was

briefly

interviewed

Data

collected

included

both

quantitative

and

qualitative

data

from

the

two

phases

of

the

study

Data

collected

for

the

first

phase

included

performance

data

-LRB-

time

spent

finishing

tasks

-RRB-

error

rates

of

tasks

ratings

on

the

satisfaction

questionnaire

after

finishing

each

task

ratings

on

the

usability

questionnaire

after

finishing

each

interface

and

comments

on

the

open

questionnaire

about

perceived

differences

and

preferences

for

the

two

interfaces

Data

collected

for

the

second

phase

included

ratings

on

the

satisfaction

questionnaire

after

finishing

each

task

comments

on

the

post-session

questionnaire

and

the

verbal

comments

made

in

the

interview

Tasks

for

the

second

phase

study

I

want

to

learn

the

current

status

of

Chinese

nuclear

energy

Find

the

most

recent

weekly

data

on

petroleum

prices

in

the

USA

Find

the

statistical

data

on

coal

production

across

different

states

in

the

year

What

kinds

of

information

can

I

and

can

I

not

find

from

the

website

Modeling

User

Interaction

To

help

us

form

hypotheses

and

analyze

and

make

sense

of

the

experimental

data

we

employed

a

linguistic

model

called

BNF

grammar

to

model

the

user

's

interaction

with

the

interface

BNF

grammar

was

originally

used

by

Reisner

to

describe

the

dialog

grammar

of

an

interactive

graphics

system

-LSB-

-RSB-

where

the

user

's

interaction

with

a

system

was

seen

as

an

action

language

and

BNF

grammar

was

used

to

formally

describe

the

language

The

BNF

grammar

consists

of

a

set

of

rules

which

define

higher

level

user

behaviors

in

terms

of

lower

level

ones

Each

rule

can

be

composed

of

terminals

non-terminals

and

a

set

of

symbols

Terminals

usually

represent

the

lowest

level

of

user

behavior

such

as

pressing

a

key

or

clicking

a

mouse

button

and

can

not

be

further

defined

Non-terminals

represent

a

high

level

abstraction

and

can

be

defined

in

terms

of

other

non-terminals

and

terminals

Terminals

are

written

with

upper

case

letters

and

non-terminals

are

written

with

lower

case

letters

The

symbol

is

read

as

is

defined

as

The

and

symbols

are

used

at

the

right

hand

side

of

rules

to

connect

respectively

sequence

of

user

behavior

set

of

options

and

concurrent

user

behaviors

With

the

BNF

grammar

we

can

describe

the

user

's

interaction

with

the

RB

as

follows

information

seeking

explore

collection

-LRB-

-RRB-

-LRB-

formulate

query

-LRB-

-RRB-

CLICK

SEARCH

BUTTON

navigate

results

-LRB-

-RRB-

-RRB-

formulate

query

-LRB-

explore

collection

-LRB-

-RRB-

form

query

-LRB-

-RRB-

-RRB-

form

query

-LRB-

-RRB-

explore

collection

-LRB-

CLICK

VISUAL

BAR-OBSERVE

VISUAL

BAR

explore

collection

-LRB-

-RRB-

-RRB-

-LRB-

MOUSE

OVER

VISUAL

BAR-OBSERVE

VISUAL

BAR

explore

collection

-LRB-

-RRB-

-RRB-

form

query

-LRB-

CLICK

VISUAL

BAR

form

query

-LRB-

-RRB-

-RRB-

-LRB-

TYPE

IN

KEYWORD

form

query

-LRB-

-RRB-

-RRB-

navigate

results

-LRB-

browse

results

-LRB-

-RRB-

navigate

results

-LRB-

-RRB-

-RRB-

-LRB-

CLICK

RESTART

BUTTON

information

seeking

-LRB-

-RRB-

-RRB-

browse

results

-LRB-

show

results

-LRB-

-RRB-

OBSERVE

RESULTS

browse

results

-LRB-

-RRB-

-RRB-

-LRB-

CLICK

RESULT

ITEM

browse

results

-LRB-

-RRB-

-RRB-

-LRB-

CLICK

SORTING

BUTTON

browse

results

-LRB-

-RRB-

-RRB-

-LRB-

explore

results

-LRB-

-RRB-

browse

results

-LRB-

-RRB-

-RRB-

show

results

CLICK

SIDEBAR

explore

results

-LRB-

observe

system

state

-LRB-

-RRB-

explore

results

-LRB-

-RRB-

-RRB-

-LRB-

filter

results

-LRB-

-RRB-

explore

results

-LRB-

-RRB-

-RRB-

observe

system

state

-LRB-

OBSERVE

VISUAL

BAR

observe

system

state

-LRB-

-RRB-

-RRB-

-LRB-

OBSERVE

NUMBER

observe

system

state

-LRB-

-RRB-

-RRB-

filter

results

CLICK

VISUAL

BAR

MOUSE

OVER

VISUAL

BAR

TYPE

IN

KEYWORD

The

interaction

with

baseline

interface

can

be

described

as

information

seeking

formulate

query

-LRB-

-RRB-

CLICK

SEARCH

BUTTON

navigate

results

-LRB-

-RRB-

formulate

query

-LRB-

TYPE

IN

KEYWORD

formulate

query

-LRB-

-RRB-

-RRB-

-LRB-

select

item

-LRB-

-RRB-

formulate

query

-LRB-

-RRB-

-RRB-

select

item

CLICK

PULL

DOWN

MENU

CLICK

ITEM

navigate

results

-LRB-

browse

results

-LRB-

-RRB-

navigate

results

-LRB-

-RRB-

-RRB-

-LRB-

CLICK

NEW

SEARCH

LINK

information

seeking

-LRB-

-RRB-

-RRB-

browse

results

-LRB-

show

results

-LRB-

-RRB-

OBSERVE

RESULTS

browse

results

-LRB-

-RRB-

-RRB-

-LRB-

show

results

-LRB-

-RRB-

COUNT

RESULTS

browse

results

-LRB-

-RRB-

-RRB-

-LRB-

CLICK

ITEM

browse

results

-LRB-

-RRB-

-RRB-

-LRB-

CLICK

SORTING

LINK

browse

results

-LRB-

-RRB-

-RRB-

show

results

CLICK

SIDEBAR

-LRB-

CLICK

SIDEBAR

CLICK

NEXT

PAGE

LINK

-RRB-

The

number

of

rules

and

options

within

rules

reflects

the

interactive

nature

and

number

of

alternative

choices

provided

by

these

two

interfaces

Note

that

we

used

the

terminals

such

as

CLICK

SEARCH

BUTTON

and

CLICK

VISUAL

BAR

which

strictly

speaking

are

not

the

lowest

level

of

user

behaviors

however

using

higher

level

abstraction

as

terminals

is

suitable

for

interactive

display-based

systems

-LSB-

-RSB-

and

ensures

later

data

analysis

Many

rules

are

defined

recursively

and

consist

of

several

options

which

essentially

reflect

the

interactivity

of

the

graphical

user

interface

-LRB-

GUI

-RRB-

For

example

a

fairly

interactive

user

behavior

in

RB

browse

results

-LRB-

-RRB-

consists

of

either

OBSERVE

RESULTS

CLICK

RESULT

ITEM

CLICK

SORTING

BUTTON

explore

results

or

any

combination

of

the

above

From

the

BNF

definition

we

can

see

that

RB

is

a

more

interactive

interface

than

the

baseline

because

it

involves

more

rules

and

recursive

definitions

However

it

is

not

necessarily

a

complicated

interface

since

the

rules

for

the

RB

interface

are

largely

composed

of

a

set

of

options

instead

of

a

sequence

of

user

behaviors

which

means

that

many

rules

are

not

executed

for

some

types

of

tasks

Based

on

the

BNF

grammars

we

hypothesize

that

for

the

simple

search

tasks

the

RB

interface

will

not

necessarily

be

significantly

different

from

the

baseline

interface

but

for

complicated

searching

and

browsing

tasks

that

require

more

interaction

or

collection

exploration

the

RB

will

be

significantly

more

effective

efficient

and

satisfying

than

the

baseline

For

simple

look

up

type

tasks

both

interfaces

involve

the

sequence

of

user

actions

formulate

query

CLICK

SEARCH

BUTTON

and

navigate

results

-LRB-

see

rule

and

-RRB-

Navigation

of

results

is

simple

for

this

type

of

task

in

that

it

only

involves

the

judgment

of

zero

or

non-zero

results

which

is

trivial

in

both

interfaces

Formulation

of

the

query

in

this

case

involves

typing

in

keywords

and/or

selecting

the

items

from

the

interfaces

-LRB-

see

rule

and

-RRB-

Even

though

item

selection

in

the

baseline

interface

involves

two

clicks

-LRB-

see

rule

-RRB-

which

means

a

slightly

longer

time

to

execute

than

in

RB

which

only

needs

one

click

on

the

visual

bar

for

item

selection

-LRB-

see

rule

-RRB-

we

expected

no

significant

difference

For

type

tasks

that

involve

data

exploration

and

analysis

interaction

with

the

visual

bars

of

the

RB

interface

provides

an

effective

and

efficient

interaction

style

Two

typical

sequences

of

user

behaviors

to

complete

type

tasks

are

explore

the

collection

by

clicking

-LRB-

or

mousing

over

-RRB-

and

observing

the

visual

bars

-LRB-

see

rule

and

-RRB-

or

formulate

a

query

and

then

explore

the

results

by

observing

the

visual

bars

-LRB-

see

rule

and

-RRB-

With

the

traditional

interface

to

finish

type

tasks

users

have

to

formulate

a

query

and

then

literally

scan

and

count

all

the

results

-LRB-

see

rule

and

-RRB-

which

is

time

consuming

We

also

hypothesized

that

users

would

exhibit

rich

interaction

during

their

navigation

of

the

results

with

RB

-LRB-

see

rule

to

-RRB-

Actions

of

typing

in

keywords

and

clicking

visual

bars

to

filter

results

-LRB-

rule

-RRB-

would

be

used

frequently

and

interchangeably

by

the

users

to

finish

complex

search

tasks

especially

when

large

numbers

of

results

are

returned

Table

lists

the

average

time

-LRB-

in

seconds

-RRB-

across

all

the

participants

to

finish

tasks

to

using

the

two

different

interfaces

Notice

that

we

allowed

the

participants

to

stop

the

task

if

they

felt

that

the

task

was

too

hard

or

too

time-consuming

to

finish

It

turned

out

that

there

were

five

participants

who

stopped

task

and

eight

participants

stopped

task

before

completion

when

they

used

the

FilmFinder

Performance

data

of

these

participants

were

discarded

for

the

unfinished

tasks

Table

Performance

data

-LRB-

in

seconds

-RRB-

Task

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

RB

FilmFinder

Task

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

RB

FilmFinder

Paired

sample

t

tests

on

the

performance

data

were

computed

and

the

p

values

are

shown

in

the

parenthesis

for

each

task

We

can

see

that

except

for

the

first

two

tasks

-LRB-

which

were

type

tasks

-RRB-

the

performance

differences

between

the

two

interfaces

were

all

statistically

significant

at

the

level

Clearly

RB

supported

superior

performance

for

type

tasks

We

also

counted

error

rates

for

tasks

to

which

are

listed

in

Table

The

error

rate

was

calculated

as

the

number

of

participants

who

gave

the

wrong

answer

to

the

task

divided

by

the

total

number

of

participants

We

can

see

that

except

for

the

task

no

participants

got

wrong

answers

for

any

of

the

tasks

using

the

RB

interface

The

error

rates

of

the

baseline

interface

were

much

higher

than

that

of

the

RB

interface

especially

for

tasks

and

Notice

that

we

did

not

consider

those

participants

who

gave

up

the

task

or

using

the

Filmfinder

so

the

actual

denominators

used

for

calculating

the

error

rates

for

these

tasks

were

smaller

than

the

total

number

of

participants

Table

Error

rates

Task

RB

FilmFinder

Task

RB

FilmFinder

We

also

did

paired

sample

t

tests

on

the

three

satisfaction

questions

which

were

completed

after

each

task

Each

response

was

given

on

a

point

scale

from

strongly

agree

-LRB-

-RRB-

to

strongly

disagree

-LRB-

-RRB-

For

the

first

three

tasks

-LRB-

simple

lookups

-RRB-

there

were

no

statistically

significant

differences

between

the

two

interfaces

on

any

of

the

questions

On

the

exploratory

tasks

-LRB-

-RRB-

statistically

significant

differences

favoring

the

RB

were

found

on

all

three

of

the

satisfaction

questions

We

also

compared

the

results

for

the

seven

overall

usability

questions

on

each

interface

asked

after

participants

had

done

the

tasks

with

each

interface

Their

responses

were

also

given

on

a

five

point

scale

from

strongly

agree

to

strongly

disagree

In

each

of

the

seven

ratings

statistically

significant

differences

were

found

favoring

the

RB

interface

Clearly

satisfaction

with

the

RB

was

greater

than

that

with

the

Filmfinder

There

were

also

open-ended

questions

that

the

participants

answered

after

finishing

both

interfaces

All

of

the

participants

considered

the

RB

interface

to

be

easier

to

use

especially

for

the

complex

searches

They

commented

on

the

easy

use

of

the

visual

display

with

the

multiple

categories

which

made

it

easy

to

combine

the

search

criteria

and

narrow

down

the

data

and

they

also

thought

it

was

good

to

be

able

to

manipulate

the

search

results

in

multiple

ways

Thirteen

out

of

participants

indicated

that

the

RB

interface

gave

them

more

confidence

to

complete

the

tasks

It

was

easy

to

go

back

and

forth

and

to

verify

the

results

and

the

informative

overview

panel

gave

the

participants

more

confidence

to

finish

tasks

There

was

one

participant

who

thought

that

both

interfaces

gave

equal

confidence

and

there

was

one

participant

who

thought

that

the

Filmfinder

interface

gave

more

confidence

since

he

was

more

familiar

with

the

Filmfinder

and

he

felt

somewhat

confused

by

the

dynamic

feature

of

the

RB

but

he

acknowledged

the

usefulness

of

the

dynamic

feature

in

narrowing

the

results

in

the

results

panel

When

asked

which

interface

better

helped

them

gain

an

understanding

of

the

library

movie

collection

the

RB

interface

was

chosen

by

all

the

participants

Again

the

visual

display

of

the

multiple

categories

and

the

cross

reference

of

these

categories

was

considered

to

be

useful

features

for

them

to

understand

the

whole

collection

In

addition

out

of

the

participants

indicated

that

they

were

more

likely

to

use

the

RB

interface

if

both

were

available

Three

participants

chose

both

interfaces

depending

on

the

type

of

tasks

and

two

participants

chose

the

FilmFinder

because

of

its

familiarity

and

aesthetic

appeal

For

the

question

on

the

best

thing

about

the

RB

participants

pointed

out

the

visual

display

of

the

multiple

categories

its

cross

reference

ability

the

dynamic

matching

ability

of

the

searching

boxes

and

the

one

screen

display

of

the

results

as

opposed

to

the

multiple

page

display

of

results

in

Filmfinder

As

the

worst

thing

about

the

RB

participants

indicated

that

it

was

not

as

It

is

easy

to

use

the

interface

I

feel

satisfied

with

the

results

I

got

I

feel

confident

with

the

results

I

got

aesthetically

appealing

as

the

Filmfinder

and

not

quite

as

intuitive

to

use

as

the

Filmfinder

Two

participants

specifically

mentioned

that

the

constant

changing

and

updating

of

the

interface

made

it

a

bit

confusing

Phase

Two

Results

During

the

second

phase

we

also

asked

participants

to

fill

out

the

satisfaction

questionnaires

after

finishing

each

task

and

these

ratings

were

predictably

high

-LRB-

all

means

above

-RRB-

More

importantly

participants

were

also

required

to

answer

a

set

of

open-ended

questions

after

finishing

the

second

phase

For

the

first

question

What

is

your

overall

impression

of

this

interface

for

finding

the

statistical

data

the

overall

impression

was

positive

Participants

used

phrases

such

as

fairly

easy

to

use

very

helpful

in

finding

the

information

good

for

quick

searching

There

were

also

a

couple

of

negative

comments

such

as

interface

still

came

up

with

many

results

after

filtering

title

of

the

results

are

not

descriptive

enough

Only

one

participant

said

that

he

did

not

like

the

interface

because

of

the

poor

categorization

of

information

items

under

some

categories

which

made

him

frustrated

When

answering

the

second

question

Was

it

helpful

to

understanding

what

is

available

at

EIA

all

the

participants

thought

the

interface

was

helpful

in

that

regard

which

was

largely

attributed

to

the

visual

display

of

the

categories

which

gave

them

a

sense

of

what

the

website

covered

One

participant

wished

that

there

were

more

categories

displayed

The

questionnaire

also

asked

if

the

search

boxes

were

helpful

in

completing

the

tasks

Participants

gave

high

praise

to

this

feature

with

comments

such

as

it

's

great

to

be

taken

directly

to

the

page

but

not

to

have

your

results

lost

I

like

the

way

it

narrows

the

focus

and

sort

of

guides

a

person

to

the

info

sought

I

did

n't

have

to

be

concerned

with

performing

a

complex

search

that

may

return

a

set-the

results

reflected

my

search

string

instantly

Two

participants

also

commented

that

the

feature

was

somewhat

limited

in

use

since

relevant

information

may

not

appear

in

the

title

or

description

The

results

strongly

support

that

the

RB

interface

was

more

effective

and

efficient

in

completing

type

tasks

than

the

baseline

interface

and

that

users

felt

more

confident

and

satisfied

with

the

RB

in

completing

type

tasks

The

higher

effectiveness

efficiency

and

satisfaction

gained

in

the

RB

resulted

mainly

from

two

aspects

the

visual

display

of

the

statistical

summary

of

the

information

items

and

the

dynamic

keyword

searching

capability

in

the

results

panel

The

visualization

bars

helped

the

users

understand

relative

proportions

of

items

at

a

glance

and

use

the

posting

numbers

directly

which

is

much

faster

than

literally

counting

If

we

look

at

the

BNF

grammar

completion

of

type

tasks

in

RB

only

required

participants

to

explore

the

collection

-LRB-

see

first

option

of

rule

-RRB-

without

submitting

queries

to

the

database

and

then

observing

and

counting

returned

results

which

are

necessary

steps

for

the

baseline

interface

to

complete

the

same

tasks

-LRB-

see

rule

-RRB-

The

dynamic

search

boxes

allow

users

do

further

filtering

based

on

certain

criteria

and

give

users

feedback

on

the

filtered

results

instantly

and

continuously

which

not

only

encourages

the

users

to

use

this

function

but

also

improves

their

efficiency

Another

interface

feature

displaying

all

the

results

on

one

screen

might

also

help

improve

the

efficiency

and

satisfaction

as

several

users

mentioned

Several

components

were

tightly

coupled

in

the

interface

with

displayed

search

results

The

search

boxes

are

tightly

coupled

with

the

results

which

means

that

any

input

in

the

search

boxes

will

invoke

instant

filtering

on

the

results

The

visual

bars

are

tightly

coupled

with

the

results

and

as

such

they

support

two

functions

One

is

that

any

operations

on

the

visual

bars

such

as

mouse

over

and

selection

invoke

the

instant

filtering

of

the

results

The

other

is

that

any

update

of

the

results

also

updates

the

summary

statistics

in

the

visualization

on

the

bars

Coupling

provides

users

more

ways

to

interact

with

the

system

and

make

the

interaction

more

natural

and

smooth

-LRB-

see

rule

-RRB-

which

suggests

a

different

interaction

style

for

finding

information

than

traditional

search

interfaces

which

tend

to

require

discrete

well-defined

turn-taking

between

the

user

and

system

Traditionally

when

users

get

to

the

results

page

all

they

can

do

is

browse

the

results

If

they

want

to

refine

the

results

they

have

to

go

back

to

the

search

interface

type

in

the

refined

keywords

click

the

search

and

browse

the

new

results

which

not

only

interrupts

the

normal

results

browsing

interaction

but

also

loses

the

current

result

set

RB

encourages

users

to

get

an

initial

manageable

result

set

and

then

refine

it

using

one

interface

window

without

the

need

to

go

back

and

forth

Instead

of

displaying

a

set

of

static

results

RB

offers

an

effective

and

efficient

means

for

users

to

understand

the

results

by

displaying

summary

statistics

bars

which

give

both

visual

and

numeric

data

-LRB-

see

rule

-RRB-

and

to

explore

results

by

providing

ways

to

dynamically

and

continuously

filter

-LRB-

see

rule

-RRB-

The

result

set

can

be

as

large

as

displaying

the

whole

collection

or

as

small

as

only

one

item

which

depends

on

the

initial

query

on

the

collection

In

the

second

phase

study

most

of

the

participants

completed

their

search

tasks

without

doing

a

second

query

on

the

initial

interface

The

study

showed

that

participants

could

utilize

the

initial

interface

to

get

an

initial

result

set

by

selecting

relevant

categories

and

then

narrow

down

results

and

find

relevant

web

pages

by

exploring

the

results

set

Typing

in

keywords

-LRB-

or

string

patterns

-RRB-

in

search

boxes

was

found

to

be

the

most

frequently

used

means

to

explore

and

filter

result

set

These

features

were

highly

appreciated

by

the

participants

as

seen

from

their

comments

Many

information

access

interfaces

try

to

provide

a

starting

point

for

users

by

presenting

overviews

of

the

collection

-LSB-

-RSB-

Overviews

can

help

users

understand

the

whole

collection

and

select

or

eliminate

sources

from

consideration

Overviews

can

direct

users

to

subcollections

quickly

where

they

can

explore

the

details

Usually

two

types

of

overviews

are

employed

category

overview

and

graphic

overview

The

category

approach

of

Yahoo

is

a

good

example

for

the

category

overview

The

HiBrowse

interface

for

viewing

category

labels

hierarchically

based

on

the

facets

is

another

example

-LSB-

-RSB-

A

more

recent

information

access

interface

using

the

category

overview

by

presenting

faceted

metadata

is

the

Flamenco

interface

-LSB-

-RSB-

The

last

two

interfaces

not

only

present

the

category

labels

to

the

users

but

also

inform

the

users

of

the

number

of

documents

under

each

category

However

these

interfaces

do

not

allow

users

to

employ

simple

mouse

moves

to

quickly

explore

the

relationship

between

different

categories

-LRB-

or

facets

-RRB-

The

Flamenco

interface

could

do

this

as

part

of

its

browsing

and

searching

efforts

but

it

requires

many

commitments

from

users

such

as

clicking

the

category

and

waiting

The

previous

version

of

the

relation

browser

-LSB-

-RSB-

presented

various

categories

and

allowed

users

to

explore

the

relations

by

mouse

over

operation

but

the

interface

only

allowed

the

users

to

mouse

over

the

main

category

The

graphic

overview

is

another

type

of

overview

which

usually

employs

various

information

visualization

techniques

Lin

-LSB-

-RSB-

used

the

Kohenen

map

to

visually

present

a

topical

overview

of

the

collection

Each

block

on

the

map

represents

a

subcollection

with

similar

topics

which

are

labeled

by

one

or

two

salient

words

extracted

from

the

subcollection

The

adjacency

of

blocks

indicates

the

topic

similarity

between

subcollections

Wise

et

al.

-LSB-

-RSB-

developed

a

three

dimensional

interface

to

visually

present

various

topics

Zhang

et

al.

-LSB-

-RSB-

exacted

the

key

concepts

from

a

collection

and

visually

presented

the

concepts

in

a

spring-embedded

graph

Similar

concepts

were

clustered

together

and

usually

represented

as

subtopics

The

graphical

overview

is

visually

appealing

but

the

usability

of

this

kind

of

interface

has

yet

to

be

explored

interfaces

are

more

problematic

than

interface

in

terms

of

ease

of

use

and

learnability

It

seems

that

textual

labels

of

category

structure

are

more

understandable

than

graphical

representation

Some

research

has

been

conducted

on

how

to

present

the

retrieved

results

in

context

Hearst

-LSB-

-RSB-

used

clustering

techniques

to

cluster

retrieval

results

on

the

fly

and

presented

different

clusters

with

labeled

words

to

the

users

to

help

them

understand

of

the

results

Chen

and

Dumais

-LSB-

-RSB-

employed

classification

techniques

to

categorize

retrieved

results

based

on

the

existing

category

structure

and

displayed

them

in

hierarchical

categories

Zamir

and

Etzioni

-LSB-

-RSB-

developed

an

interface

that

used

on-the-fly

clustering

of

metasearch

results

These

interfaces

cluster

or

categorize

the

retrieval

results

on

the

fly

so

scaling

is

problematic

The

RB

categorizes

the

collection

offline

and

uses

a

uniform

category

structure

to

present

overviews

of

the

collection

and

the

retrieval

results

Consequently

RB

can

be

scaled

up

easily

However

because

RB

depends

on

the

metadata

to

reside

on

the

client

side

to

achieve

its

dynamics

it

also

suffers

a

different

kind

of

scalability

limitation

To

date

we

have

had

good

success

and

response

with

data

sets

with

tens

of

thousands

of

records

and

a

dozen

or

so

facets

however

data

sets

with

millions

of

records

and

scores

of

facets

are

problematic

There

also

has

been

some

work

on

fast

location

of

specific

information

items

Sorting

is

a

prevalent

means

to

help

users

locate

a

specific

item

However

users

still

need

to

visually

go

through

a

list

of

items

The

Alphaslider

-LSB-

-RSB-

is

a

visual

component

to

help

users

quickly

locate

a

known

string

of

items

but

it

's

not

very

easy

to

use

especially

for

novice

users

Besides

The

Alphaslider

can

only

locate

the

information

items

based

on

the

first

letter

alphabetically

RB

provides

an

easy

and

flexible

way

to

locate

the

information

items

by

typing

in

string

patterns

and

the

patterns

can

be

matched

anywhere

in

the

information

items

A

similar

technique

is

actually

used

in

some

applications

such

as

the

address

box

of

Internet

Explorer

Various

RB

examples

are

available

at

browser

but

the

patterns

are

limited

to

matching

from

the

beginning

of

the

query

string

Dynamic

query

was

a

new

type

of

interface

-LSB-

-RSB-

that

inspired

the

original

relation

browser

work

The

interface

visually

displays

the

information

items

and

provides

the

visual

controlling

components

to

explore

the

information

items

by

tightly

coupling

search

and

visual

display

of

results

RB

uses

this

design

concept

but

instead

of

providing

a

visual

interface

RB

employs

a

more

understandable

-LRB-

especially

for

topical

overview

-RRB-

category

structure

for

the

information

items

Moreover

the

search

box

is

a

very

effective

and

efficient

component

for

the

non

categorized

attributes

of

the

items

while

the

visual

controlling

components

such

as

sliders

or

check

boxes

can

only

be

used

for

controlling

categorical

attributes

of

the

items

Query

preview

-LSB-

-RSB-

attribute

explorer

-LSB-

-RSB-

and

other

interfaces

-LSB-

-RSB-

and

-LSB-

-RSB-

provide

similar

ways

to

explore

the

relationships

between

different

facets

of

the

classification

These

interfaces

worked

for

structured

information

such

as

that

found

in

databases

Of

course

all

these

types

of

interfaces

depend

on

good

underlying

categorization

of

data

Our

long-term

goal

is

to

make

the

interface

work

for

unstructured

textual

information

The

search

boxes

provided

are

a

first

step

in

this

direction

although

they

are

currently

limited

to

search

within

the

fields

specified

in

the

results

display

One

constraint

in

RB

is

the

limited

number

of

categories

that

can

be

displayed

which

is

affected

by

two

factors

One

factor

is

screen

real

estate

We

can

partially

alleviate

the

issue

of

screen

real

estate

by

utilizing

a

Zoomable

User

Interface

-LRB-

ZUI

-RRB-

to

display

the

categories

We

have

experimented

with

integrating

the

Jazz

toolkit

-LSB-

-RSB-

into

the

interface

and

this

provides

aproximately

a

ten-fold

increase

in

the

number

of

facet

values

that

can

be

supported

within

each

facet

although

at

the

expense

of

some

of

the

mouseover

dynamics

since

the

mouse

must

now

be

used

for

zooming

as

well

as

normal

hovering

Another

factor

is

size

of

the

memory

to

hold

the

client-side

distribution

counts

data

the

number

of

which

increases

exponentially

with

increased

number

of

displayed

categories

One

way

to

solve

the

issue

is

to

only

calculate

part

of

the

distribution

counts

data

which

hopefully

are

most

frequently

used

during

the

user

's

interaction

with

the

interface

Other

approaches

such

as

employing

novel

data

structures

were

also

suggested

by

Doan

et

al.

-LSB-

-RSB-

However

all

these

solutions

have

to

sacrifice

the

interactivity

of

the

interface

that

depends

on

client-side

metadata

to

support

rapid

mouse

activities

For

example

preloading

partial

distribution

counts

data

for

large

numbers

of

categories

make

some

distributional

data

and

visualization

unavailable

when

users

try

to

re-partition

the

information

space

by

mouse

moves

At

present

the

best

development

path

seems

to

be

hierarchical

partitioning

of

very

large

information

spaces

with

multiple

RB

instances

that

require

a

new

download

for

each

of

the

cascading

subsets

Another

constraint

of

RB

is

the

limited

matching

function

of

the

search

boxes

The

interface

currently

matches

input

string

patterns

to

the

corresponding

result

fields

on

the

lexical

level

Matching

in

this

level

is

sufficient

in

many

cases

such

as

matching

with

fields

with

numbers

or

short

textual

strings

such

as

titles

but

for

the

fields

with

more

semantic

bearing

strings

such

as

descriptions

of

web

pages

a

more

sophisticated

match

function

based

on

semantics

might

be

needed

perhaps

a

kind

of

full-text

engine

in

each

text

field

although

the

close

coupling

with

the

facet

panel

will

then

be

in

doubt

Currently

the

interface

provides

a

uniform

category

structure

for

both

the

entire

collection

and

the

retrieved

results

set

This

is

good

for

its

consistency

However

for

the

retrieved

result

set

a

more

fine-grained

category

structure

might

be

better

for

users

to

understand

it

and

conduct

string

searches

Overall

the

RB

represents

an

example

of

a

highly

interactive

user

interface

that

offers

improved

performance

and

satisfaction

for

users

of

large

websites

and

digital

libraries

It

can

find

application

as

the

entry

point

for

a

large

website

or

as

a

way

to

work

with

large

results

sets

returned

from

search

engines

as

long

as

the

data

is

structured

in

advance

Over

the

past

few

years

we

have

created

more

than

two

dozen

instances

of

data

sets

using

RB

and

RB

demonstrating

its

applicability

as

an

interface

to

many

different

types

of

data

If

the

data

resides

in

a

database

it

is

possible

to

map

the

scheme

to

the

underlying

RB

scheme

-LRB-

see

-LSB-

-RSB-

for

details

on

the

system

architecture

-RRB-

and

simply

import

the

data

automatically

For

many

WWW

applications

this

is

not

possible

so

we

have

been

developing

ways

to

automate

facet

discovery

and

webpage

assignment

to

those

facets

The

basic

approach

is

to

crawl

the

website

-LRB-

s

-RRB-

create

term-document

representations

and

then

use

machine

learning

techniques

to

cluster

the

webpages

and

extract

candidate

labels

use

human

judgment

to

select

the

best

labels

and

then

classify

the

webpages

into

those

categories

using

the

statistical

model

produced

in

the

process

See

Efron

et

al

-LSB-

-RSB-

for

details

of

the

techniques

we

have

applied

to

date

To

illustrate

the

current

state

of

development

consider

the

EIA

example

used

in

the

study

reported

here

The

categories

displayed

on

the

EIA

RB

instance

were

originally

created

manually

which

certainly

did

not

scale

well

to

many

other

large

information

collections

such

as

various

other

government

statistical

web

sites

Figure

is

a

screen

shot

of

the

RB

instance

for

the

Bureau

of

Labor

Statistics

web

site

that

uses

topic

facets

and

webpage

assignment

that

were

automatically

determined

About

HTML

web

pages

were

crawled

from

web

site

and

a

soft

clustering

method

was

then

applied

to

those

pages

For

each

webpage

the

statistical

model

yielded

a

probability

of

belonging

to

every

cluster

Thus

every

page

belongs

to

every

cluster

at

some

level

of

probability

The

first

topic

column

contains

all

the

pages

with

highest

probability

of

belonging

to

those

facet

values

The

second

column

contains

all

the

pages

with

the

second

highest

probability

values

for

those

facet

values

The

third

and

fourth

columns

are

the

months

and

years

of

page

update

extracted

from

web

pages

themselves-facets

that

are

know

to

be

problematic

but

used

for

illustration

since

our

primary

emphasis

was

on

topic

discovery

The

display

of

two

topical

columns

reflects

the

underlying

characteristic

of

soft

clustering

where

items

can

appear

in

multiple

clusters

However

our

discussions

with

BLS

staff

and

demonstrations

to

other

potential

users

show

that

this

two-column

display

is

confusing

Therefore

only

the

first

topical

column

was

kept

in

a

later

version

of

the

RB

BLS

instance

-LRB-

see

figure

-RRB-

An

additional

facet

geographical

coverage

which

is

an

important

facet

of

government

statistical

web

sites

was

added

in

this

version

The

assignments

were

made

using

a

rule

based

classification

method

to

classify

the

web

pages

into

categories

of

various

geographical

coverages

Figure

First

version

of

RB

instance

for

BLS

web

site

Figure

Latest

version

of

RB

instance

for

BLS

web

site

In

addition

to

the

BLS

instance

we

have

used

these

techniques

to

create

RB

instances

for

the

FedStats

website

and

are

working

to

create

instances

for

other

federal

statistical

agencies

At

present

the

FedStats

and

BLS

instances

are

installed

on

FedStats

servers

and

are

being

tested

by

federal

statistical

agency

personnel

We

also

hope

to

address

the

important

facet

of

time

coverage

of

data

itself

in

future

work

Overall

the

RB

user

interface

has

evolved

to

a

state

where

it

can

be

easily

applied

to

many

kinds

of

well-structured

data

The

user

testing

reported

here

demonstrates

the

efficacy

of

the

interface

for

search

and

browse

tasks

that

would

be

very

difficult

to

execute

with

SQL

syntax

or

form

fillin

interfaces

Our

current

efforts

are

to

develop

techniques

for

automatically

populating

the

RB

database

with

unstructured

data

from

the

WWW

To

date

these

efforts

have

led

to

promising

prototypes

for

several

federal

statistical

websites

This

work

was

supported

by

NSF

Grant

EIA

The

authors

wish

to

thank

Paul

Solomon

and

other

anonymous

reviewers

for

their

valuable

comments

on

the

paper

and

Tim

Shearer

for

designing

the

underlying

database

structure

of

the

interface

We

also

thank

Jonathan

Elsas

and

Miles

Efron

for

developing

the

clustering

software

and

techniques

-LSB-

-RSB-

Ahlberg

C.

and

Shneiderman

B

The

alphaslider

a

compact

and

rapid

selector

In

Proceedings

of

the

SIGCHI

conference

on

human

factors

in

computing

systems

Boston

Massachusetts

-LSB-

-RSB-

Bederson

B.

Meyer

J.

and

Good

L.

Jazz

An

Extensible

zoomable

user

interface

graphics

toolkit

in

java

In

ACM

-LSB-

-RSB-

Chen

H

and

Dumais

S.

Bringing

order

to

the

web

Automatically

categorizing

searching

results

In

Proceedings

of

the

SIGCHI

conference

on

human

factors

in

computing

systems

The

Hague

Amsterdam

-LSB-

-RSB-

Dix

A.

Finlay

J.

Abowd

G.

Beale

R

and

Finley

J.

Human-Computer

Interaction

-LRB-

nd

Ed

-RRB-

Prentice

Hall

Hillsdale

NJ

-LSB-

-RSB-

Doan

K.

Plaisant

C.

Shneiderman

B.

and

Bruns

T.

Interface

and

Data

Architecture

for

Query

Preview

in

Networked

Information

Systems

ACM

Transactions

on

Information

Systems

July

Vol

No.

-LSB-

-RSB-

Efron

M.

Marchionini

G.

and

Zhang

J.

Implications

of

the

recursive

representation

problem

for

automatic

concept

identification

in

on-line

governmental

information

ASIST

SIG-CR

Workshop

Long

Beach

CA

-LSB-

-RSB-

Efron

M.

Elsas

J.

Marchionini

G.

and

Zhang

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Machine

learning

for

information

architecture

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a

large

governmental

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Joint

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on

Digital

Libraries

-LRB-

Tuscon

AZ

June

-RRB-

-LSB-

-RSB-

Greene

S.

Marchionini

G.

Plaisant

C.

Shneiderman

B.

-LRB-

-RRB-

Previews

and

overviews

in

digital

libraries

Designing

surrogates

to

support

visual

information

seeking

Journal

of

the

American

Society

for

Information

Science

-LRB-

-RRB-

-LSB-

-RSB-

Hearst

M.

User

interfaces

and

visualization

In

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retrieval

Ed

by

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and

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Chapter

ACM

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York

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1999 257-324

-LSB-

-RSB-

Hearst

M.

and

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Reexamining

the

cluster

hypothesis

Scatter/Gather

on

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Proceedings

of

Annual

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ACM/SIGIR

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Zurich

-LSB-

-RSB-

Lanning

T.

Wittenburg

K.

Heinrichs

M.

Fyock

C.

and

Li

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Multidimensional

information

visualization

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rods

AVI

Palermo

Italy

-LSB-

-RSB-

Lin

X.

Map

displays

for

information

retrieval

Journal

of

the

American

society

for

information

science

-LRB-

-RRB-

-LSB-

-RSB-

Marchionini

G.

and

Brunk

B.

Toward

a

general

relation

browser

A

GUI

for

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architects

Journal

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Information

Article

No.

-LRB-

-RRB-

-LSB-

-RSB-

Pollitt

A.

S.

Ellis

G.

P.

and

Smith

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P.

HIBROWSE

for

Bibliographic

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Journal

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Science

-LRB-

-RRB-

-LSB-

-RSB-

Reisner

P.

Formal

Grammar

and

human

factor

design

of

an

interactive

graphics

system

IEEE

Trans

on

Software

Engineering

-LRB-

-RRB-

-LSB-

-RSB-

Shneiderman

B.

Dynamic

queries

for

visual

information

seeking

IEEE

Software

-LRB-

-RRB-

-LSB-

-RSB-

Spence

R

and

Tweedie

L

The

attribute

explore

information

synthesis

via

exploration

Interacting

with

Computers

-LSB-

-RSB-

Tanin

E.

Lotem

A.

Haddadin

I.

Shneiderman

B.

Plaisant

C.

and

Slaughter

L.

Facilitating

data

exploration

with

query

previews

a

study

of

user

performance

and

preference

Behaviour

information

technology

-LRB-

-RRB-

-LSB-

-RSB-

Wise

J.

Thomas

J.

Pennock

K.

Lantrip

D.

Pottier

M.

and

Schur

A.

Visualizing

the

non-visual

spatial

analysis

and

interaction

with

information

from

text

documents

In

Proc

of

the

Information

visualization

Symposium

pages

IEEE

Computer

Society

Press

O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O B I O O O O O B O B O B I O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I I I O O O B O O O O O O B O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B O B O O O O O B I O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B 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B O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O

facets
visual display
faceted category structure
user study
searching
user satisfaction
visualization
user interface
human factor
browsing
browse
search
category overview
dynamic query
efficiency
satisfaction
modeling user interaction
information storage and retrieval
effectiveness

interface design
interactive system
interaction patterns with interface
rb++
information browsing
browse and search interface
relation browser++
information search