1. Generalized Formal Models for Faceted User Interfaces Edward Clarkson, Sham Navathe and Jim Foley College of Computing, Georgia Tech

2. Overview 1.Survey of faceted navigation frameworks Observe some common design variances Observe some common design variances 2.Entity-relationship and relational models for faceted data and queries Generalize to observed design variances Generalize to observed design variances

3. Motivation Provide a structured view of faceted navigation design space Provide a structured view of faceted navigation design space Conceptual models provide concrete basis for developing new systems in practical terms (RDBMS) Conceptual models provide concrete basis for developing new systems in practical terms (RDBMS) Suggest ways for extending state-of-the-art Suggest ways for extending state-of-the-art (Is this the right venue?) (Is this the right venue?)

4. Background Faceted Classification (Ranganathan): Faceted Classification (Ranganathan): Classification of items into multiple independent (maybe hierarchical) categorizations Faceted Navigation: software UI on FC data Faceted Navigation: software UI on FC data “Focus Items” “Facet Values” Focus Facets

5. Faceted Navigation Survey 8 Faceted Browsing systems: 8 Faceted Browsing systems: Relation Browser, mSpace, Flamenco, Elastic Lists, Humboldt, Parallax, Bungee View and Nested Faceted Browser Relation Browser, mSpace, Flamenco, Elastic Lists, Humboldt, Parallax, Bungee View and Nested Faceted Browser MS FacetLens (CHI 09) too new MS FacetLens (CHI 09) too new Rationale Rationale 1. Typical faceted software features (no Tabulator) I.e., filtering via value selection I.e., filtering via value selection 2. Recency (no Allen, Pollitt) 3. Framework rather than components (no Endeca)

6. Faceted Navigation Survey 1.Visual Design 2.Interaction Design 3.Structural Design

7. Faceted Navigation Survey Visual Design Vertical vs. Horizontal Facet Layout Vertical vs. Horizontal Facet Layout Cardinality Data/Preview Cardinality Data/Preview

8. Faceted Navigation Survey Interaction Design Selection Cardinality Selection Cardinality Selection Cascades Selection Cascades

9. Faceted Navigation Survey Structural Design Facet Hierarchy Facet Hierarchy Indirect Facets Indirect Facets Single- vs. Multi- Focus Single- vs. Multi- Focus

10. Faceted Navigation Survey Structural Design Facet Hierarchy Facet Hierarchy Indirect Facets Indirect Facets Single- vs. Multi- Focus Single- vs. Multi- Focus

11. Modeling Faceted Metadata Task: can we model the data/queries apparent in faceted navigation software? Task: can we model the data/queries apparent in faceted navigation software? Take a DB-centric approach: Entity- Relationship, relational models Take a DB-centric approach: Entity- Relationship, relational models Goal: generate a data/query model that generalizes to the variances in our survey Goal: generate a data/query model that generalizes to the variances in our survey nteraction, structural design variance nteraction, structural design variance

12. Modeling Faceted Metadata Related Work McGuffin and schraefel (HT 2004) McGuffin and schraefel (HT 2004) Zhang and Marchionini (JCDL 2004) Zhang and Marchionini (JCDL 2004) Dimensional Modeling Dimensional Modeling XFML XFML

13. Modeling Faceted Metadata Background “Some of the discussion on database design is too basic” vs. “Some guidance [reading ER models] would help the layman” “Some of the discussion on database design is too basic” vs. “Some guidance [reading ER models] would help the layman”

14. Modeling Faceted Metadata Intra-entity ER Data Model Models single entity features (focus or facet): implicit form of entities to follow… Models single entity features (focus or facet): implicit form of entities to follow… Accounts for hierarchical facets, arbitrary item data fields Accounts for hierarchical facets, arbitrary item data fields

15. Modeling Faceted Metadata Basic ER Data Model Entities model focus items and facet data Entities model focus items and facet data Relationships model classification of focus items into facets Relationships model classification of focus items into facets

16. Faceted Models Extended ER Data Model Accounts for indirect facets Accounts for indirect facets Straightforward translation to relational models Straightforward translation to relational models

17. Modeling Faceted Metadata Relational Query Model Construct relational calculus models that retrieve appropriate Construct relational calculus models that retrieve appropriate Focus items Focus items Facet values Facet values Account for selection cascades Account for selection cascades Model Input: facet value selection tuples Model Input: facet value selection tuples {(C 1, (E 1,1,…,E 1,k 1 )),…,(C n, (E n,1,…,E n,k n )} C i = {c i,1,…,c i,j i } and c i,l  C i is the l th selection from FACET i C i = {c i,1,…,c i,j i } and c i,l  C i is the l th selection from FACET i (E i,1,…,E i,k i ) is the selection path for FACET i (E i,1,…,E i,k i ) is the selection path for FACET i Accounts for conjunctive/disjunctive value selections Accounts for conjunctive/disjunctive value selections

18. Modeling Faceted Metadata Relational Query Model Iterative development in paper: Iterative development in paper: 1.Queries over basic model 2.Queries with indirect facets Separate queries for focus/facet data Separate queries for focus/facet data 3.Generalized Queries Unified query model for focus/facet data (accounts for for multi-focus environment Unified query model for focus/facet data (accounts for for multi-focus environment Accounts for hierarchical selections in focus data Accounts for hierarchical selections in focus data

19. …for each facet selection Modeling Faceted Metadata Extended Query Model Hierarchical constraint Name the facets that have selections Join facet with selection to interest relation Facet selection constraint

20. Future Work Extend the model: Extend the model: Expand notion of classifying an item: probabilistic classification? Expand notion of classifying an item: probabilistic classification? Improve the systems: Improve the systems: Performance analysis of interface features Performance analysis of interface features

21. Acknowledgements Colleagues Colleagues Daniel Tunkelang Daniel Tunkelang Ian Dickinson/Georgi Kobilarov; Rob Capra/Gary Marchionini; Max Wilson/m.c. schraefel; Marti Hearst Ian Dickinson/Georgi Kobilarov; Rob Capra/Gary Marchionini; Max Wilson/m.c. schraefel; Marti Hearst Funding Funding Steven Fleming Chair in Telecommunications and DHS/NVAC/SRVAC Steven Fleming Chair in Telecommunications and DHS/NVAC/SRVAC