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How We Incrementally Improved
           Search




        Ravi Mynampaty
         @ravimynampaty
Agenda


   Background
•   Roadmap
•   Implementation
•   Analytics
•   Benefits
•   Challenges
•   Next Steps
Background: A few years ago…

•   Out-of-the-box Ultraseek
•   No optimization, no customization
•   Fraction of HBS content indexed / searchable
•   Many dead ends
•   Proliferation of different search tools
•   User sentiment
     • “search sucks”
     • “why can’t it be more like Google”
Background: Our Vision

•   One Search Box to Rule Them All
•   The long term goal: enterprise search
•   One-stop searching
•   Google-like simplicity
•   Handle refinement / navigation on results page
Agenda


•   Background
   Roadmap
•   Implementation
•   Analytics
•   Benefits
•   Challenges
•   Next Steps
Roadmap: Preliminary Steps

• Inventory document collections

• Inventory search-type tools

• Of the above, identify
      – most heavily used
      – strategically significant
      – high impact
      – Low Hanging Fruit (LHF)
Roadmap: Implementation Plan

•   Prioritize tasks by ease of content access
    and implementation (LHF)

•   Develop timeline

•   Build prototypes and iterate the design
Agenda


•   Background
•   Roadmap
   Implementation
•   Analytics
•   Benefits
•   Challenges
•   Next Steps
Implementation: How we built it

• Customized Ultraseek’s results display code
• Worked with owners of software apps
      –Provided JSON APIs
      –Allowed us to spider their app/repository
• HTML is the API !!
• In other words:
      No rocket science involved
Implementation: Three Integration Approaches


• Blended Search (e.g., Faculty/Staff Directory)

• Brokered Query (e.g., Video Catalog)

• Query Resubmit (e.g., Alumni Directory)
Implementation: Blended Search

Spider HBS web content outside of HBS.EDU
• Harbus.org (student newspaper)
• Club and affiliated sites




Spider HBS content located in other applications
• Faculty and staff phone book
• Alumni Class Notes application
Implementation : Optimize and clean up search indexes

 Work with content owners to create good HTML page titles
   • Faculty Publications pages
   • 20th Century Leadership database
   • Address MS-Office / PDF files too

 Eliminating duplicate search results / use filters

 Adjusting Relevance per collection / source / file path
Implementation : Create Best Bets




Top 10 Queries
Oct – Dec
Implementation: Unify Blended Search + Query Resubmit
Query refinement options
(Blended Search)




Query resubmit options
“Integration-lite”
Implementation: Expanding the Net w/ Brokered Search


• When direct indexing isn’t practical
    Harvard.edu search
    HBS VideoTools (intranet only)
    MBA Event Calendar (intranet only)
• A query is handed off to another search engine
• Results are returned “behind the scenes” as
  JavaScript Object Notation (JSON) / Python
• Ajax-like support of asynchronous search
  processes
Implementation: Brokered Query in Action
Implementation: Brokered Query in Action
Implementation: Brokered Query in Action
Implementation: One-offs

• Software Dev Docs (cmd line)

$ find ./software/docs –name ‘*html’
         | xargs grep -i oracle | less

(returns 100s of docs)

• Built web-based search UI
Agenda


•   Background
•   Roadmap
•   Implementation
   Analytics
•   Benefits
•   Challenges
•   Next Steps
Analytics: Tracking Usage of Features
Analytics: Tracking Best Bets
Analytics: Tracking Best Bets
Agenda


•   Background
•   Roadmap
•   Implementation
•   Analytics
   Benefits
•   Challenges
•   Next Steps
Benefits

• Single point of access for various repositories

• Shortcomings of underlying tools overcome

• Better access to content from rest of Harvard

• Traffic boost to e-commerce site
Agenda


•   Background
•   Roadmap
•   Implementation
•   Analytics
•   Benefits
   Challenges
•   Next Steps
Challenges


• Search is never done

• Complex permissions issues

• SERP design convergence

• SharePoint
Agenda


•   Background
•   Roadmap
•   Implementation
•   Analytics
•   Benefits
•   Challenges
   Next Steps
Next Steps

• Tackling the mixed-mode situation
• Integration with taxonomies
• Search experience within HBS applications
• Faceted search where rich metadata
  available
• Analytics feeding website design and
  vocabulary development
Conclusion


• Tactical, iterative approach enabled
  significant progress

• Implementing simpler features/tweaks may
  have higher impact

• Your existing search engine may have more
  gas in it than you realize

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How We Incrementally Improved Search

  • 1. How We Incrementally Improved Search Ravi Mynampaty @ravimynampaty
  • 2. Agenda  Background • Roadmap • Implementation • Analytics • Benefits • Challenges • Next Steps
  • 3. Background: A few years ago… • Out-of-the-box Ultraseek • No optimization, no customization • Fraction of HBS content indexed / searchable • Many dead ends • Proliferation of different search tools • User sentiment • “search sucks” • “why can’t it be more like Google”
  • 4. Background: Our Vision • One Search Box to Rule Them All • The long term goal: enterprise search • One-stop searching • Google-like simplicity • Handle refinement / navigation on results page
  • 5. Agenda • Background  Roadmap • Implementation • Analytics • Benefits • Challenges • Next Steps
  • 6. Roadmap: Preliminary Steps • Inventory document collections • Inventory search-type tools • Of the above, identify – most heavily used – strategically significant – high impact – Low Hanging Fruit (LHF)
  • 7. Roadmap: Implementation Plan • Prioritize tasks by ease of content access and implementation (LHF) • Develop timeline • Build prototypes and iterate the design
  • 8. Agenda • Background • Roadmap  Implementation • Analytics • Benefits • Challenges • Next Steps
  • 9. Implementation: How we built it • Customized Ultraseek’s results display code • Worked with owners of software apps –Provided JSON APIs –Allowed us to spider their app/repository • HTML is the API !! • In other words: No rocket science involved
  • 10. Implementation: Three Integration Approaches • Blended Search (e.g., Faculty/Staff Directory) • Brokered Query (e.g., Video Catalog) • Query Resubmit (e.g., Alumni Directory)
  • 11. Implementation: Blended Search Spider HBS web content outside of HBS.EDU • Harbus.org (student newspaper) • Club and affiliated sites Spider HBS content located in other applications • Faculty and staff phone book • Alumni Class Notes application
  • 12. Implementation : Optimize and clean up search indexes Work with content owners to create good HTML page titles • Faculty Publications pages • 20th Century Leadership database • Address MS-Office / PDF files too Eliminating duplicate search results / use filters Adjusting Relevance per collection / source / file path
  • 13. Implementation : Create Best Bets Top 10 Queries Oct – Dec
  • 14. Implementation: Unify Blended Search + Query Resubmit
  • 15. Query refinement options (Blended Search) Query resubmit options “Integration-lite”
  • 16. Implementation: Expanding the Net w/ Brokered Search • When direct indexing isn’t practical  Harvard.edu search  HBS VideoTools (intranet only)  MBA Event Calendar (intranet only) • A query is handed off to another search engine • Results are returned “behind the scenes” as JavaScript Object Notation (JSON) / Python • Ajax-like support of asynchronous search processes
  • 20. Implementation: One-offs • Software Dev Docs (cmd line) $ find ./software/docs –name ‘*html’ | xargs grep -i oracle | less (returns 100s of docs) • Built web-based search UI
  • 21. Agenda • Background • Roadmap • Implementation  Analytics • Benefits • Challenges • Next Steps
  • 25. Agenda • Background • Roadmap • Implementation • Analytics  Benefits • Challenges • Next Steps
  • 26. Benefits • Single point of access for various repositories • Shortcomings of underlying tools overcome • Better access to content from rest of Harvard • Traffic boost to e-commerce site
  • 27. Agenda • Background • Roadmap • Implementation • Analytics • Benefits  Challenges • Next Steps
  • 28. Challenges • Search is never done • Complex permissions issues • SERP design convergence • SharePoint
  • 29. Agenda • Background • Roadmap • Implementation • Analytics • Benefits • Challenges  Next Steps
  • 30. Next Steps • Tackling the mixed-mode situation • Integration with taxonomies • Search experience within HBS applications • Faceted search where rich metadata available • Analytics feeding website design and vocabulary development
  • 31. Conclusion • Tactical, iterative approach enabled significant progress • Implementing simpler features/tweaks may have higher impact • Your existing search engine may have more gas in it than you realize