Presentation by Yana Kane-Esrig at theIxDA Northern NJ / NNJ UX Meetup on September 30, 2009

1. “Web 3.0”
Yana Kane-Esrig
September 30, 2009
Defining Web 3.0
Web 1.0: access to information
Linked text, search engines
Web 2.0: Web 1.0 + : Social networking, User Generated Content, multimedia,
entertainment, mash-up applications
Broadband, Internet as platform ( “read-write web”: pages or screens that the
user can edit and re-shape, browser-based applications), communication,
interoperability (APIs), collaboration tools
Web 3.0: Web 2.0 + “collective intelligence” infrastructure and culture
Not one capability, but many separate, yet overlapping and synergistic trends
Federation of knowledge creation (knowledge mash-ups?)
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2. Web 3.0 Themes
Crowdsourcing and open sourcing becoming mainstream: large scale distributed
production of information, knowledge and culture
Contextual / implicit web: user gets action and information that they need when
they need it delivered to them
Semantic web: knowledge based use of information
Social graph: filter the web for end user, filter end users for marketers
Measurable web: measure effectiveness of ads, segment users, quantify
“sentiment”
“Internet of things”: sensors allow linking “physical world” and “cyber world”
Mobile web
Separate, yet overlapping and synergistic trends that enable:
Greater variety of models for monetizing the web
Better user experience on the web and enabled by the web
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Crowdsourcing and Open Sourcing Becoming Mainstream
Crowdsourcing: task outsourced to community via open call. Open sourcing:
activity initiated by community
UGC: user creates content and makes it publicly available
Variety of project types, business models, areas of application
Competition, competitive collaboration, “divide and conquer”, collaboration ,
collaborative filtering, prediction markets
Commercial use of crowdsourcing: P&G, IBM, Threadless, istock, current TV,
Marketocracy
R&D, product design, software design and development, stock photos, citizen
journalism,
journalism scientific research, investment
research investment…
Crowdsourcing & open sourcing are both enabled by and are an enabler of
“Web 3.0”
Creating semantic web content and metadata (tagging)
Helping machines use sensor readings (e.g., marking up or tagging visual data)
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3. Crowdsourcing and Open Sourcing - Development of Infrastructure
Emergence of “reputation economy”:
Reviews, recommendations, discussions of products drive purchasing
Credibility, reputation, position on “leader board”, connectedness is “coin of the
realm”
Companies specializing in crowdsourcing platforms
Examples: IBM IdeaJam, InnoCentive, TopCoder, Chaordix, Amazon Mechanical Turk
Academic research into crowdsourcing project life-cycle and management
“The Metropolis Model”
The Model
“Ultra Large Scale” systems
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Contextual / Implicit web
“Get rid of pages” – user gets action and information that they need when they
need it delivered to them
Based on user’s own behavior and behavior of their “social graph”
User gets value from having their behavior tracked and recorded
Extreme form of “personalization” and “recommendation”
Browser or application “guesses” the user’s intent and acts as a proactive
intelligent agent (e.g., finds, formats and displays relevant information)
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4. Semantic Web
“Semantic web” seems to refer to three inter-related, but different things:
1) Formal descriptions of concepts and relationships in a knowledge domain;
2) Tools and knowledge repositories to enable specification and use of metadata
about concepts and object: describing /tagging them, specifying relationships
among them and the types of inferences that can be made
3) Changes in online user experience enabled by combining this knowledge about
resources available to serve the user with insight into the user’s intent
Users interact not with “web pages” and bits of text but with “conceptual
objects”/concepts. Data becomes independent of application and can be
reused across the web
Can build applications to displace routine tasks done by humans: e.g.,
automate routine searches and scanning of documents to find the relevant
bits, compute answers by combining multiple bits of data “on the fly”
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Social Graph Vocabulary: Friend of A Friend (FOAF)
FOAF: machine-readable (and human readable) ontology describing people, the
links between them and the things they create and do ((e.g. photos, calendars,
weblogs)
The FOAF “grass roots” project is creating a Web of machine-readable page
Anyone can use FOAF to describe him or herself. Some social networking site
profiles can be used to generate a FOAF (e.g., Facebook has a tool for that)
FOAF allows groups of people to describe social networks (create their social
graphs) without the need for a centralized database. FOAF makes social graph
portable across applications / social networks
FOAF makes it possible to find people who “have something in common”
(common friend, common interest, etc.)
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5. Social Graph Vocabulary Use Case Example:
Aardvark (vark.com) – currently uses Facebook. Promises to expand to other
social networks
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Measurable web
Measurable web: derive (and monetize) knowledge from behavior of users and
from web content
Improve customer experience in a variety of verticals
Improve web itself (e.g., refine search algorithms, web site design)
To monetize web it is necessary to measure and analyze real-time user behavior
Measure ROI, adjust ad campaigns
Brand management: sentiment tracking
Ad targeting based on “faces, not places”
Identify, build relationships with “influencers”
Managing customer experience in real time is becoming the norm
Identifying and defusing complaints. Preemptive information dissemination
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6. Internet of Things
Objects an people cast “information shadow” in the cyberworld
Sensors linking “physical world” and “cyber world”
Location, orientation /movement of device, speech/sound recognition,
image/video, touchscreens
Examples: Gracenote: music recognition. Layar: augmented reality enhanced mobile
browser
Other sensors (e.g., energy consumption of device, health monitors)
One type of application: smart grid to reduce energy consumption
Combining sensor data with user intent, profile, behavior, context
Coordination of speech recognition and location in search
Voice search initiated based on gesture
Google iPhone search app
Image recognition is easier if know where you are and when
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Mobile Web
Smartphones, Netbooks adoption rapidly growing
Devices equipped with multiple sensors
iPhone applications downloads went from 1 billion to 1.5 billion in 3 months
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Adopters of smartphones and laptops equipped with cellular Internet different
from non-adopters on a wide range of technology and economic activity
behaviors (greater difference than due to demographics)
Game devices, book readers (Kindle) impacting usage patterns, business model
expectations
Switch from “mobile-specific web to “full web
mobile specific web” full web”
However, user interface does need to be mobile-specific
Users expect the same capabilities and content on mobile as they get on PC.
Users expect performance similar to what they get on fixed broadband.
Distinction between “on-deck” and “off deck” content losing importance
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7. Let’s Discuss!
Do any of the themes impact your current or future activities (work-related or
personal)?
What do you see as the key emerging themes in the online/communications
world?
Why do you consider them important?
How are they likely to impact you at work and/or in your personal life?
Is there any type of tool or service that you wish was around to help you make
better use of existing or emerging capabilities of the online/communications
world?
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