Generalized Formal Models
for Faceted User Interfaces
Edward Clarkson, Sham Navathe and Jim Foley
College of Computing, Georgia Tech
Overview
1. Survey of faceted navigation frameworks

Observe some common design variances
2. Entity-relationship and relational models
for faceted data and queries

Generalize to observed design variances
Motivation


Provide a structured view of faceted navigation
design space
Conceptual models provide concrete basis for
developing new systems in practical terms
(RDBMS)


Suggest ways for extending state-of-the-art
(Is this the right venue?)
Background
Focus
Categorization
Categorization

Faceted Classification
(Ranganathan):

Facets
Items

Categorization
Categorization
Classification of items into
multiple independent
(maybe hierarchical)
categorizations
Faceted Navigation:
software UI on FC data


“Focus Items”
“Facet Values”
Faceted Navigation Survey

8 Faceted Browsing systems:

Relation Browser, mSpace, Flamenco, Elastic Lists,
Humboldt, Parallax, Bungee View and Nested
Faceted Browser


MS FacetLens (CHI 09) too new
Rationale
1.
Typical faceted software features (no Tabulator)

2.
3.
I.e., filtering via value selection
Recency (no Allen, Pollitt)
Framework rather than components (no Endeca)
Faceted Navigation Survey
1. Visual Design
2. Interaction Design
3. Structural Design
Faceted Navigation Survey
Visual Design


Vertical vs.
Horizontal
Facet Layout
Cardinality
Data/Preview
Faceted Navigation Survey
Interaction Design

Selection Cardinality

Selection Cascades
Faceted Navigation Survey
Structural Design

Facet Hierarchy

Indirect Facets

Ind.
Facet
Facet
Ind.
Facet
Facet
Focus
Facet
Single- vs. MultiFocus
Focus
Facet
Ind.
Facet
Facet
Ind. Facet
Ind.
Facet
Facet
Ind.
Facet
Facet
Faceted Navigation Survey
Structural Design

Facet Hierarchy

Indirect Facets

Single- vs. MultiFocus
Modeling Faceted Metadata

Task: can we model the data/queries
apparent in faceted navigation software?


Take a DB-centric approach: EntityRelationship, relational models
Goal: generate a data/query model that
generalizes to the variances in our survey

nteraction, structural design variance
Modeling Faceted Metadata
Related Work

McGuffin and schraefel (HT 2004)

Zhang and Marchionini (JCDL 2004)

Dimensional Modeling

XFML
Modeling Faceted Metadata
Background

“Some of the discussion on database design is
too basic” vs. “Some guidance [reading ER
models] would help the layman”
attribute
Entity
relationship
Entity
Modeling Faceted Metadata
Intra-entity ER Data Model

Models single entity features (focus or facet):
implicit form of entities to follow…

Accounts for hierarchical facets, arbitrary item data
fields
id
Name
ENTITY
Attr1
N child
1 parent
Attrn
Modeling Faceted Metadata
Basic ER Data Model
Facet

Focus

Facet
Facet
Facet
Entities model focus
items and facet data
Relationships model
classification of focus
items into facets
Faceted Models
Extended ER Data Model


Accounts for indirect
facets
Ind. Facet
Ind. Facet
Facet
Straightforward
translation to
relational models
Focus
Facet
Facet
Ind. Facet
Facet
Modeling Faceted Metadata
Relational Query Model

Construct relational calculus models that retrieve
appropriate


Focus items
Facet values


Account for selection cascades
Model Input: facet value selection tuples
{(C1, (E1,1,…,E1,k1)),…,(Cn, (En,1,…,En,kn)}



Ci = {ci,1,…,ci,ji} and ci,l  Ci is the lth selection from FACETi
(Ei,1,…,Ei,ki) is the selection path for FACETi
Accounts for conjunctive/disjunctive value selections
Modeling Faceted Metadata
Relational Query Model

Iterative development in paper:
1. Queries over basic model
2. Queries with indirect facets

Separate queries for focus/facet data
3. Generalized Queries


Unified query model for focus/facet data
(accounts for for multi-focus environment
Accounts for hierarchical selections in focus data
Modeling Faceted Metadata
Extended Query Model









Q : t.Id











 (e1,1 )  (e1,k1 )  (en ,1 )  (en ,k n )





FACET1 (e1,1 )    FACETk1 (e1,k`1 )
 


Name the facets that

 
 

have
selections
 

FACET
(
e
)



FACET
(
e
)

m

k
n
,
1
m
n
,
k
 
n
n

 

(t. parent
 c p ,1   constraint
t. parent  c p , j p ) 
Hierarchical
 

e1,1.Id
1  e1with
 e1, 2 .Id 1  to

e1,k1 1.Id relation
2  e1,k1 .Id 1   
  t.Id Join
facet
interest
,1 .Id 2 selection
 

(
e
.
Id
2

c



e
.
Id
2

c
)
 
1,Facet
k1
1,1
1, k1
1, j1

selection
constraint
 




…for
each
facet
selection
 

  t.Id  en ,1.Id 1  en ,1.Id 2  en , 2 .Id 1    en ,k n 1.Id 2  en ,k n .Id 1   


 

(
e
.
Id
2

c



e
.
Id
2

c
)
n , k1
n ,1
n , k1
n , jn
 

INTEREST (t ) 
Ind. Facet
Ind. Facet
Facet
Focus
Facet
Facet
Ind. Facet
Facet
Future Work

Extend the model:


Expand notion of classifying an item:
probabilistic classification?
Improve the systems:

Performance analysis of interface features
Acknowledgements

Colleagues



Daniel Tunkelang
Ian Dickinson/Georgi Kobilarov; Rob
Capra/Gary Marchionini; Max Wilson/m.c.
schraefel; Marti Hearst
Funding

Steven Fleming Chair in Telecommunications
and DHS/NVAC/SRVAC