Web 3.0 - Concepts, Technologies, and Evolving Business Models

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A dramatic shift in business and technology is taking place as the Social Web (Web 2.0) evolves into the Semantic Web (Web 3.0) of the future. Networks link smartphones, in-car computers, televisions and home media networks to collectively provide instant and universal access to personal information and entertainment media. Integrated marketing campaigns feature an enticing mix of content and location-based and contextual-aware advertising delivered through digital signs and billboards. Highly targeted advertising is generated based digital profiles that describe the habits and preferences of an individual without revealing personal identifiable information, and then delivered through entertainment systems and mobile applications. Vast interconnected systems of distributed applications ingest data, generate feeds, and intelligently filter content based on usage patterns and preferences. This presentation, part one of three, covers the evolution of the Web, business models on the Web, and core elements of the semantic Web. Part two highlights existing products and systems that contain semantic Web elements. Part three covers 17 semantic Web application scenarios and forecasts the impact of Web 3.0 on marketing, advertising and business models.

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Web 3.0 - Concepts, Technologies, and Evolving Business Models

  1. 1. Web 3.0 - Concepts, Technologies, and Evolving Business Models UCLA Extension | Course x861.409 Charles G. Hollins | cgh@ucla.edu
  2. 2. Introduction2
  3. 3. The Future of Advertising? What aspects do you think well see in the near future? Which do you think are far off or unlikely to ever materialize?Are there any aspects ofthese scenes similar to applications that you have used? If these methods of advertising existed today do you think they would be effective? Why or why not? 3
  4. 4. The Future of Advertising? What are the similarities and differences between the real NEC system and the one depicted in Minority Report? In an average week how many public digital screens are within your view? What are the benefits and constraints of this type of advertising?4
  5. 5. Outline Introduction Evolution of Web  How has the Web changed?  What features have stayed the same? Semantic Web Concepts  What technologies are being used to make the Web smarter? Revenue Models  How will the semantic Web impact revenue models? The Future of the Web  What applications are on the horizon? 5
  6. 6. About the CourseSections Format Part 1 - Concepts  Terms and Concepts (this deck)  Online Videos Part 2 - Existing Products  Group Discussion and Applications Questions Part 3 - Future Semantic  Application Scenarios Web Applications  Q&A 6
  7. 7. Evolution of the Web7
  8. 8. See the Web Evolve in 4 Minutes What terms were you already familiar with? What terms have you heard of any would like to know more about? How are users helping computers to be more intelligent?8
  9. 9. The Pre-Web Internet  Global Network  Email and Messaging  File Sharing  Remote Applications  Document Publishing  Resource Sharing9
  10. 10. The Static Web  Publication Medium  Platform Independence  Explosion of Hyperlinked Content and Media  Search Engines and Indexes  Graphical User Interface  Internet Software for the Masses10
  11. 11. The Dynamic Web  Application Platform  Web-Based Applications  Internet Services  Data-Driven Applications  Interactive Web Pages  Location Independent Computing  Streaming Media11
  12. 12. The Social Web  Extensible, Interoperable Applications  Customization and Personalization  User Generated Content Tools  Tagged and Syndicated Content  Commenting, Feedback, Reviews  Data Feeds and Tools  Community and Collaboration12
  13. 13. The Mobile Web  Mobile Computing Platforms  Robust Mobile Devices  Access to the Web from Anywhere  Digital Entertainment  Device Feature Integration (camera, GPS)  Augmented Reality  Encoded Data13 Capture
  14. 14. Evolution of Business Models14
  15. 15. Brokerage Connect buyers and sellers Simple and focused function Transaction commission Demand Collection System Combinations  B2B, B2C, C2C  Payment: Buyer/Seller/Hybrid Virtual Marketplace  Flat-Rate/Percentage  Features: auction, etc. Web-Suited and Scalable Transaction BrokerSource: http://digitalenterprise.org/models/models.html Auction Broker 15
  16. 16. Advertising Based on traditional media  Broadcaster and advertiser  Simple and focused function Models  CPM - impression  CPC - click  CPA – action (register, install) Web-Suited and Scalable  Content-driven Page Ad Units  Profile and context targeting  Metrics and analytics Paid Placement Highly Targeted AdsSource: http://digitalenterprise.org/models/models.html 16
  17. 17. Sponsorship Similar to advertising Integrating brands into content  Featured placements  Page takeovers and UI skins  Branded logotrademarks Featured Placements  Video strips and bugs Web-Suited and great for content driven sites Page Takeovers 17
  18. 18. Infomediary  Broker of consumer data  Collect and aggregate usage data  Common uses:  Enhance marketing and advertising campaigns Ad Networks  Target ads based on behavior  Web-Suited and Scalable Market ResearchSource: http://digitalenterprise.org/models/models.html 18
  19. 19. Merchant  More overhead and bigger risk  Supply chain management  Inventory investment  Online sales and return processing Virtual Merchant  Multifaceted  Buying  Storage  Selling  Shipping Bit Vendor  Variations  Digital-only products  Extended storefronts Catalog MerchantSource: http://digitalenterprise.org/models/models.html 19
  20. 20. Manufacturer  Direct sales from manufacturer to consumer  Similar to merchant but seller has more control over inventory  Inventory can be digital  Example: the $10,000 email Direct Sales  Many companies use EBay and Amazon to implement this modelSource: http://digitalenterprise.org/models/models.html 20
  21. 21. Affiliate  Third-party that provides a purchase lead (CPA) or add click- through (CPC)  Purchase point click-through from Purchase Affiliate one site to the merchant site  Merchant provides affiliate with a percentage of transaction  Very well-suited to the Web Conversion Affiliate Ad Words AffiliateSource: http://digitalenterprise.org/models/models.html 21
  22. 22. Open Source  Collaborative product development  Freely available source code and Open Source Platform tools  Results in robust platforms  Revenue generated from  Donations  Support  Documentation Open Content  Integration  Commercial versions  Plug-ins and extensions Open Source SoftwareSource: http://digitalenterprise.org/models/models.html 22
  23. 23. Subscription  Periodic fee for a service  Free with limited scale Free Trial then Subscription  Free with limited features  Free for trial period  Free samples Subscription or Limited Scale  Well-suited for:  Cloud services and applications  Music and Video  Consumer value often depends on usage patterns Free Samples then Subscription  Predictable revenue stream  (subscribers * rate) Subscription or Limited FeaturesSource: http://digitalenterprise.org/models/models.html 23
  24. 24. On-Demand  Usage metered pay-as-you-go  Popular for movie rentals  Microtransactions & virtual currency  Popular in gaming Video on Demand  Consumer must have a strong (VOD) interest in the content to pay Microtransactions andSource: http://digitalenterprise.org/models/models.html Virtual Currency 24
  25. 25. Business Models: QuizWhat business models do the following sites employ? 25
  26. 26. Semantic Web Concepts26
  27. 27. Collective Data and IntelligenceElements Outcome User Generated  Massive Storehouse of Content Information Content and Data  Multiple Viewpoints of a Aggregation Subject or Object  Framework for Blog Posts and Understanding and Comments Evaluating Content Wikis  Object Meta Data Data feeds  Examples: Social Bookmarking, Photosynth 27
  28. 28. Merging UGC into a Single View Photosynth combines related images into a single view which can be navigated. The same technology was originally used to combinethousands of images of Venice from various people into a cohesive presentation. What other types of content would this type of aggregation and synthesis be well suited for? 28
  29. 29. Entity Definition and ProcessingElements Outcome Massive Noun  Universal Catalog Database  Context Reference consolidation  Disambiguation Item Roles  Links and Associations Item Attributes  Examples: Metaweb, Modeling of Freebase Relationships 29
  30. 30. A Database of Everything What is Metaweb and what problems does it aim to solve? Who maintains it? How does Metaweb make Web applications more powerful? How does Metaweb enable applications and systems to better integrate?30
  31. 31. XML and Derived LanguagesElements Uses Text-Based  Defining Languages Meta Markup Language  Exchanging Data Elements and Attributes  Importing & Exporting Syntax and Parsing  Data Transformation Structure and Validation  Data in a Web Page  example database record  Configuration Settings Parsers 31
  32. 32. Descriptive Meta DataElements Outcome Geotagging  Associating an object Microformats with its meaning Tag-Based  Self-defined structured Folksonomies content Autotagging Systems  Objects that work well with other applications and tools  Examples: Flickr Image Mapping, ALIPR 32
  33. 33. Microformats Add Meaning to Content What are microformats? How do the help make data more portable and meaningful? What types of common microformats do you think exist? What previously discussed elements of the Web are being depicted here?33
  34. 34. Explicit Data CollectionMethods Outcome Items rated on a sliding  Digital DNA of Interests scale and Preferences Ranked collection of items Item marked as the best of two or more options Wish list created 34
  35. 35. Implicit Data CollectionMethods Outcome Items viewed, added to a  Digital DNA of Interests cart or purchased and Preferences Time spend on page or item Items listened to or watched Analyzing social network likes and dislikes Analyzing feedback sentiment 35
  36. 36. A Peek Under the Hood of Netflix What was required in order to win the Netflix Prize? Why was Napoleon Dynamite an exception al movie for the collaborative filtering algorithm? What data does Netflix collect from it’s users order to improve it’s recommendations?36
  37. 37. Information FilteringTypes Outcomes Profile-based  Recommendation Content-based engines Location-based  Targeting advertising Usage-based  Examples: Collaborative  Google Ad Sense  Facebook Ads  Netflix Recommendations 37
  38. 38. Recommendations Help Filter Content How does Digg determine what articles a user would like enjoy reading? What applications have you used that have similar recommendation features? How do you think recommendations affect user engagement?38
  39. 39. Cloud ComputingTypes Outcome Online Data Storage  Scalable, location Online Web Applications independent computing  Higher level of Web Online Commercial service provision Applications  Focus on function not system  Examples  Dropbox  Goggle Docs  Twilio + Google Voice 39
  40. 40. Dropbox Cloud File Storage What common problem does Dropbox solve, and how? How does Dropbox simplify the process of backing up information and sharing files? What other types of Cloud services have you heard of or used?40
  41. 41. Extensible SoftwareTypes Outcome Browser Extensions  Ability to extend without Server Extensions rebuilding Social Network Platform  Interconnectivity Apps between systems, Web Office Applications applications and devices Widgets and Gadgets  Examples Open Source Platforms  WordPress and Joomla  FireFox and Chrome  LinkedIn Apps 41
  42. 42. LinkedIn Application Platform What is a LinkedIn application and why might a user want to install one on their profile? How are these applications a win-win deal for both the social network and the application developer? An application used to refer to a program installed on a computer. Now there are many different types of applications. How many can you identify?42
  43. 43. Smart DevicesCharacteristics Outcome Radio-Frequency  Ubiquitous Computing identification  Context Awareness Network Support  State Awareness Common, Extensible  Cooperative Processing Operating Systems 43
  44. 44. An Internet of Things What is a system of systems? What is The Internet of Things? What does the DIKW Triangle represent? How does this relate to the "Collective Data andIntelligence slide covered earlier? What scenarios involving the Internet of Things might make your life easier? 44
  45. 45. Examples45
  46. 46. Freebase Paralax How do entities provide a framework for working with data in more powerful ways?What Web services were used to visualize datagenerated from Freebase? This video showed a human using an Web interface to query and present data. What possibilities emerge when systems are integrated into freebase? 46
  47. 47. Evri and Daylife What are the main features of Evri and Daylife? How do these services relate to the semantic Web? What types of companies would benefit from services offered by DayLife?47
  48. 48. The Future of the Semantic Web What aspects of the semantic Web were mentioned in this video? Why are things such as meta data, extensible applications, smart devices, information filtering, and cloud computing important aspects of the semantic Web? How would you describe the difference between the social Web and the semantic Web?48
  49. 49. Q&A49
  50. 50. Thanks50

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