Information Architecture
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  • Information Mapping Never write a information module twice. Never include an information module twice. Just refer to it. Replace the Paragraph as the Base Unit of Information. Fewer than seven sentences Clearly labeled by type ( Headings are the most common way of doing this) Each block has one main point or lesson Use similar descriptions for similar concepts Version Control Turning over the file to another developer Edits Prototypes Alpha Test Beta Test Version Formats

Information Architecture Presentation Transcript

  • 1. Information Architecture & Design
    • Construction of IA and Web
    • Rosenfeld Chapters
    • Other Readings
    • Presentations
  • 2. IA Methodology Analysis Design Verification Construction Maintenance Planning
  • 3. The Construction Phase
    • Construction is building the product.
      • Using all of the information from the preceding phases to make a product suited to the users and their environment.
      • Following structured information engineering principles to provide rigor and metrics.
  • 4. Construction
    • Developing Content
    • Organizing Content
      • Version Control
      • Conventions
    • Construction Methods
        • Templates
        • File Names
    • Cooperative Development
    • Backups
    • Revision to Project Plan
  • 5. Construction Tips
    • Don’t Use “Bad” Tools
      • Interfaces
      • Functionality
      • Import – Exports – File Formats
    • Don’t Rely on Tool-Generated Markup
    • Fine-Tune Generated Markup
    • Save Multiple (All) Versions
    • Prepare Different Formats
    • Backups
    • Coordination
      • Version Control
      • Serial Development
      • Communication
  • 6. Construction Resources
    • http://www.dynamicdrive.com/
    • http://perso.wanadoo.fr/ablavier/TidyGUI/
    • http://builder.cnet.com/webbuilding/0-7600.html
    • http://www.webdeveloper.com/
    • http://www.ddj.com/topics/xml/
    • http://info.med.yale.edu/caim/manual/contents.html
  • 7. More Construction Resources
    • http://www.apple.com/developer/
    • http://msdn.microsoft.com/
    • http://devedge.netscape.com/
    • http://www.w3.org/Style/CSS/
    • http://oreilly.com/
    • http://www.onjava.com/
    • http://www.onlamp.com/
    • http://sourceforge.net/
    • http://www.w3.org/
  • 8. Constructing the IA Product
    • Planning and Designing Are Over – What’s Next?
    • Construction (Depending on Size) Uses the Most Project Resources
      • Time
      • People
    • Selling the Project
      • “Making the Case” To Management
      • “Business Strategy” for Developers & IA Project Team
    • IA Is A Resource
  • 9. Making the Case for IA
    • The Methods Save Resources
      • Less Design Errors
      • Faster Construction
      • Following the Plan
    • Over 90% of Software Projects Are Never Completed
    • Requirements Aren’t Tracked for Subsequent Versions
  • 10. IA ROI (Construction Goals)
      • Find Information Faster
      • Find Information (!)
      • Make Sense of Information Found
      • Less Time Searching for Documents
      • More Completed Purchases
      • Fewer Navigation Errors
      • Better Understanding of Information (Context and Content)
      • Fewer System Resources
      • Less Technical Support
      • Higher Quality Final Design
      • Difficult To Verify and Measure
  • 11. IA Value (& Design) Checklist
    • Reduces the Cost of Finding Information
    • Reduces the Cost of Finding Wrong Information
    • Reduces the Cost of Not Finding Information at All
    • Provides a Competitive Advantage
    • Increases Product Awareness
    • Increases Sales
    • Makes Using a Site A More Enjoyable Experience
            • (IAv2 p 344-5)
  • 12. IA Value Checklist, Part Two
    • Improves Brand Loyalty
      • Ease of Use
      • Strong, Unique Design
    • Reduces Reliance on Documentation
    • Reduces Maintenance Costs
      • Sensible IA Structures
        • For the User
        • For the Developers
    • Reduces Training Costs
    • Reduces Staff Turnover
      • Better Development Methods
          • Comments
          • File Sharing
          • Backups
  • 13. IA Value Checklist, Part Three
    • Reduces Organizational Upheaval
      • Design Goals are Explained and Agreed Upon Early
      • Good Development Reduces Surprises
    • Reduces Organizational Politicking
    • Improves Knowledge Sharing
      • Group Communication
      • File Sharing
      • Development Standards
        • Templates
        • Tools
    • Reduces Duplication of Effort
    • Solidifies Business Strategies
            • (IAv2 p 344-5)
  • 14. IA & Business Strategy
    • Business Goals vs. IA
    • IA Exposes Business Goals
      • New Models for Organizing Information
      • New Tasks With Old Information
      • New Ways of Working
    • The IA Project Plan as a Business Plan
      • Focus on the Users/Customers
      • Focus on Goals (in addition to Tasks)
    • Corporate Sponsorship
      • Business Needs
      • Executive Clarity
  • 15. 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.
  • 16. Verification/Evaluation
    • Error Tracking
      • Logging
      • Notification
    • User Testing
      • Test Plan
        • Functional tests
        • Completeness tests
        • Evaluating Test Results
      • Metrics
  • 17. 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.
  • 18. Maintenance
    • Support
    • Post-Mortem
    • Versions
    • Mixed Lifecycle Versioning
    • Maintenance is always more difficult than planned
  • 19. 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 User’s Time Looking for Information
  • 20. 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
  • 21. 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
  • 22. 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?
  • 23. 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
  • 24. 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
  • 25. IA Evaluation Using Heuristics
    • Nielsen’s Discount Usability Engineering
      • Quick
      • Dependent on Experience of Eval Team
      • Done Throughout the IA Methodology (@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
  • 26. 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
  • 27. 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
  • 28. Web Usage Collection
    • Types of Data
      • Web Servers
      • Proxies
      • Web Clients
    • Data Abstractions
      • Sessions
      • Episodes
      • Clickstreams
      • Page Views
    • The Tools for Web Use Verification
  • 29. 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
  • 30. 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
  • 31. 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)
  • 32. 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
  • 33. 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)
  • 34. 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 Isn’t Accurately Describing Web Search
    • Phrase Searching Could Be Augmented
            • Silverstein, Henzinger, Marais, Moricz (1998)
  • 35. 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
  • 36. 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
  • 37. 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
  • 38. 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
  • 39. Evaluation the Utility & Usability
    • For 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
  • 40. 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
  • 41. Let’s Talk about IA Tools
    • What Are You Using?
      • HTML/XML
      • Graphics
      • Navigation
      • Image Maps
      • Javascripts
      • Forms
      • Site Maps
      • Directories
  • 42. Class Work: Card Sorting
    • Open Card Sorting
      • No established groups
      • Show all the cards
    • Task = Navigation & Understanding the Site
    • Put the cards in groups that seem similar
    • Name the groups
    • Put the groups in “order”
    • Describe what you understand from the cards
  • 43. Card Sorting Analysis
    • What were the groups?
    • Were labels unclear?
    • What was the general understanding of the site?
    • Did you get more groups or less?
    • What tasks does this sorting support?
      • Navigation
      • Understanding
      • Wayfinding (Mental Model)
      • Metaphor