Information Architecture

1
Information Architecture Design

• Week 11 Schedule
• IA and Web Verification/Evaluation and
  Maintenance
• Rosenfeld Chapters 20 21
• Other Primary Readings
• BREAK
• Research Topic Presentations (2)
• 10 Minute Drill
• 3 Every Week
• Other 3 Next Week
• Design Critiques Returned Discussed

2
IA Methodology
Planning
Analysis
Design
Verification
Construction
Maintenance
3
The Verification Phase

• Verification is ensuring the usefulness of the
  product.
• Testing the product with the target user to
  uncover weaknesses in the product.
• Implementing solutions to iron out these
  weaknesses
• Planning when to return to the Construction phase
  to iron out these weaknesses.

4
Verification/Evaluation

• Error Tracking
• Logging
• Notification
• User Testing
• Test Plan
• Functional tests
• Completeness tests
• Evaluating Test Results
• Metrics

5
The Maintenance Phase

• Maintenance is providing for future releases of
  the product.
• Establishing some intervals and responsibilities
  to keep the product up to date.
• Deciding if it is necessary to return to or
  modify other phases to improve the product or the
  methodology itself.

6
Maintenance

• Support
• Post-Mortem
• Versions
• Mixed Lifecycle Versioning
• Maintenance is always more difficult than planned

7
MS Web Intranet Study

• 3 Million Pages
• 50,000 (Potential) Users
• 74 Countries
• 8,000 Separate Intranet Sites
• 2.3 Hours a Day Used
• 50 of Users Time Looking for Information

8
MS Web Intranet Problems

• Starting Points
• Navigation Systems
• Labels
• Answers Resolution
• Portal Design
• Diverse Authoring Tools
• Diverse Authorship
• Age of Information
• Massive Team Approach To Solving Problems

9
MS Web Taxonomies

• The Language of Clients
• Descriptive Vocabularies
• Server Log Analysis
• Pre-Existing Work
• Political and Content Experts
• Universal Applicability
• Metadata
• Basics (URL, Desc, Dates, Contact, Status)
• Extensions (Importance, Categories, Keywords)
• Category Labels
• Site Maps
• Page Terms

10
MS Web Construction/Evaluation

• Search Log Analysis for Taxonomy Development
• Controlled Vocabulary Use
• Set of Tools
• Metadata Registry
• Vocabulary Manager
• URL Catalog
• Tools Enforce Processes
• What Other Tools Would Be Appropriate for
  Construction, Evaluation and Maintenance?

11
MS Web Verification For Improvement

• Helping Where It Hurts (p 403)
• Fix Major Broken Areas
• Search
• Often the Most Broken
• Often the First To Be Fixed
• Collection and Analysis Services
• Portable Search Technologies
• Any Tool With Import and Export
• XML
• Analysis Fixes Problems and Helps Future Design
• Best Bets Most Likely Applicable Result
• Interaction Analysis Before and After

12
evolt.org Adaptive Verification

• Online Community
• Atypical Users
• Atypical Development?
• Different Possible Users Tasks
• Site Functions Added Variably
• Gradual Shift in User Functions
• IA Should Support Community by Sharing and
  Monitoring
• Let Members Verify IA Structures and Construct
  Content
• Use Determines What Gets Fixed or Added

13
IA Evaluation Using Heuristics

• Nielsens Discount Usability Engineering
• Quick
• Dependent on Experience of Eval Team
• Done Throughout the IA Methodology (_at_Design)
• Group Work Different People Find Different
  Problems
• Follow Basic Usability Principles
• Find More Problems Than Time To Fix
• IA Plan Determines Ranking Problems to Fix
• Severity Ratings Good, But Ranking is Better
• Often Too Arbitrary
• Tie to IA Plan and User Analysis

14
Web Usage Mining

• VL Verification
• Data Mining to Discover Patterns of Use
• Pre-Processing
• Pattern Discovery
• Pattern Analysis
• Site Analysis, Not User Analysis
• Srivastava, J., Cooley, R., Deshpande, M., Tan,
  P.N. - 2000

15
Web Usage Discovery

• Content
• Text
• Graphics
• Features
• Structure
• Content Organization
• Templates and Tags
• Usage
• Patterns
• Page References
• Dates and Times
• User Profile
• Demographics
• Customer Information

16
Web Usage Collection

• Types of Data
• Web Servers
• Proxies
• Web Clients
• Data Abstractions
• Sessions
• Episodes
• Clickstreams
• Page Views
• The Tools for Web Use Verification

17
Web Usage Preprocessing

• Usage Preprocessing
• Understanding the Web Use Activities of the Site
• Extract from Logs
• Content Preprocessing
• Converting Content Into Formats for Processing
• Understanding Content (Working with Dev Team)
• Structure Preprocessing
• Mining Links and Navigation from Site
• Understanding Page Content and Link Structures

18
Web Usage Pattern Discovery

• Clustering for Similarities
• Pages
• Users
• Links
• Classification
• Mapping Data to Pre-defined Classes
• Rule Discovery
• Rule Rules
• Computation Intensive
• Many Paths to the Similar Answers
• Pattern Detection
• Ordering By Time
• Predicting Use With Time

19
Web Usage Applications

• Application Goals
• Improved Design
• Improved Delivery
• Improved Content
• Personalization (XMod Data)
• System Improvement (Tech Data)
• Site Modification (IA Data)
• Business Intelligence (Market Data)
• Usage Characterization (User Behavior Data)

20
Real Life Information Retrieval

• 51K Queries from Excite (1997)
• Search Terms 2.21
• Number of Terms
• 1 31
• 2 31
• 3 18 (80 Combined)
• Logic Modifiers (by User)
• Infrequent
• AND, , -
• Logic Modifiers (by Query)
• 6 of Users
• Less Than 10 of Users
• Lots of Mistakes

21
Real Life Information Retrieval

• Sessions
• Flawed Analysis (User ID)
• Some Revisits to Query (Result Page Revisits)
• Page Views
• Accurate, but not by User
• Use of Relevance Feedback
• Not Used Much (11)
• Terms Used Typical
• Mistakes
• Typos
• Misspellings
• Bad (Advanced) Query Formulation
• Jansen, B. J., Spink, A., Bateman, J.,
  Saracevic, T. (1998)

22
Analysis of a Very Large Search Log

• 280 GB Six Weeks of Web Queries
• 1 Billion Search Requests
• 285 Million User Sessions
• Web Users
• Use Short Queries
• Mostly Look at the First Ten Results only
• Seldom Modify Queries
• Traditional IR Isnt Accurately Describing Web
  Search
• Phrase Searching Could Be Augmented
• Silverstein, Henzinger, Marais, Moricz (1998)

23
Analysis of a Very Large Search Log

• 2.35 Average Terms Per Query
• 0 20.6 (?)
• 1 25.8
• 2 26.0 72.4
• Operators Per Query
• 0 79.6
• Terms Predictable
• First Set of Results Viewed Only 85
• Some (Single Term Phrase) Query Correlation
• Augmentation
• Taxonomy Input
• Robots vs. Humans

24
Scent of a (Web) Site

• Exploring Hypotheses About Web Site Use
• Goals Analysis and Prediction
• Predicting Usability of Alternate Designs
• What is the Overall Site Traffic Flow?
• Where Do Visitors Come From?
• What Pages Are Related?
• What Are the User Interests for a Page?
• Information Foraging and Information Scent
• Paths of Web Use Captures User Goals and Behavior

25
Scent of a (Web) Site

• Look for Longest Repeating Subsequences
• Among Different Users
• The Same User Over Time
• For One Web Site Only
• Assume User Has Information Goal
• Users Like Ants Exploring and Foraging
• Paths are Links from Page to Page
• Analyze All the Paths and What Were Used
• Visualization Methods
• Prediction

26
Using Web Use Evaluation for IA

• How Can These Ideas Be Used for IA?
• Verification for Design and Construction
• Web Usage Clustering and Classification
• Web Site Design Rules
• Web Searching
• Web Scent and Foraging
• Web Use Goal Prediction

27
Evaluation the Utility Usability of Adaptive
Hypermedia System

• As Web Sites, Web Users IA Advance How Do You
  Evaluate Them?
• Help With Large Info Structures
• Somewhere between System User Control
• Adaptive Systems Influence User Behavior
• Less Actions
• Less Decisions
• Preferred

28
Adaptive Systems Evaluation

• Ways to Evaluate
• Part of Iterative Design Process
• Time to Task Measurement
• Diagnostic Testing
• Goal Measurement
• How Is This Different?
• User Perceptions of Adaptation
• Variable Experience for Each User
• Longer Evaluation Times
• Selected Goals and Tasks That Show Adaptation
• Interfaces and Content Changes!
• More Users and Evaluations May Be Needed
• Work Environments, Not Labs
• Real Content

29
Bonus Points IA Tool Reviews

• Each Review Contributes 1 point to Final Grade
• Up to Five IA Tool Reviews
• Submit Notice of IA Tool Review and Name of Tool
• IA Tool Review Format
• One IA Tool (or class of tool)
• Key IA Functions of Tool
• Describe Interface and Unique Features
• Menus
• Commands
• Formats
• Screen Capture of Main Tool Interface
  Additional Noteworthy Features
• Example of Tool Output (graphic, search results,)

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Next Week

• Non-Web IA
• Readings
• Two Questions for Class Discussion
• Class Participation
• Presentations
• 10-Minute Drills Continue Project Group Check-In