Download - WebTrends - Between Semantic, loss of Privacy and Personalization.

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© 2010 useKit • Informatics 2010, Freiburg, Germany, 26 - 28 July 2010

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Between Semantic loss of Privacy and Personalization.

WebTrends

Dr. Sven Rizzotti – ! University of Basel, Switzerland! useKit.com

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Predictions

Popular Mechanics, forecasting the relentless march of science, 1949

"Computers in the future may weighno more than 1.5 tons."



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Predictions

Thomas Watson, chairman of IBM1943

"I think there is a world market formaybe five computers."



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Predictions

Ken Olson, president, chairman and founder of Digital Equipment Corp.

1977

"There is no reason anyone wouldwant a computer in their home."



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Outline• Part I

– Location-Aware Web– Mobile Web– Web of Things

• Part II– Semantic Web– Personalization– Privacy

• Practical Example: useKit



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Mobile Web

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Image credit: RusselBeattie.com



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W3C – Mobile Web Initiative

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Tim Berners-Lee, W3C

“The Mobile Web Initiative’s goal is to make browsing the Web from mobile

devices a reality”



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Mobile Web

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• Explosive Mobile Web Growth Worldwide– Latin America (Chile: 3200% growth in 2009)– 40% of American adults use a mobile to access the Web (2010)

RedOrbit Staff & Wire Reports



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Mobile traffic expectation

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2010

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2012

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2015

0 125 250 375 500

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Coda Research Consultancy 2010

Peta Bytes / Month



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Location based Services - (lbs)

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Nokia



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Layar

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http://www.layar.com





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foursquare

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http://www.foursquare.com





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Location based Services - (lbs)• Mobile access demand for lbs

– Route planning– Where am I?– Where are my friends?– Point of interests

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http://www.where.com





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Location aware Web• Many applications use location and expose location in

the UI• Web not ready for locations yet

– Not supported in URI– Not supported in HTTP– Not supported in HTML– Mashability requires exposed application

models

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Web of Things

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Image credit: http://www.touchatag.com





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Web of Things

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Ian Rae (Twitter @irae), 6, 2008

• Frigdes, lights, toasters, washing machine• Network of internet enabled objects with web services

that interact with them• Technologies: RFID (radio frequency identification),

sensors, smartphones, gps



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Pachube

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http://www.pachube.com





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Web of Things

Dries Buytaert

“If the graph of people is cool, imagine a graph of everything”



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Semantic Web

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http://www.freewebs.com





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Web of Things

Rachel Lovinger

There’s no Semantic WebWithout Content and Data



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Semantics• Machines are good at complex things where people

arenʼt– Computing, recalling long numbers– Compare large data sets– Search through massive amount of data records

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Semantics• People are good at complex things where machines

arenʼt– Equivalence ! 14:00h / 4 pm– Inexactness! 14:00h / 4.23 pm– Splitting things! 14:01:20 / 140120

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Semantics Basics for machines• Identity

– Uniqueness

• Definition• Structure

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Semantics Basics for machines• Identity• Definition

– Ontology

• Structure

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Semantics Basics for machines• Identity• Definition• Structure

– Web Standards (Dublin, RDF, OWL, SKOS)– Non standards (Microformats, Folksonomy)

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Semantic Web – More relevant information

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• Documents with computer-processable meaning

• Mass of unstructured data ➜ semantic information?– RDF (Resource Description Framework)– OWL (Web Ontology Languages)– Microformats (hCard, hCalendar)



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Linked data

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Richard Cyganiak and Anja Jentzsch



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Linked data

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• One Page Concept– URL == ID– Contains related IDʼs– Search optimized


http://en.wikipedia.org/wiki/Linked_data





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Personalization• Semantic + Personalization = Relevant information

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Image credit: http://puttingdotsonpaper.com





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Personalization• Semantic + Personalization = Relevant information• Meeting the customer's needs more effectively and

efficiently ➜ increase satisfaction• Web Personalization: A site just for you• Real benefit: The Aha effect

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Personalization• Learns what you like from your actions

(you are what you click on and what you buy)• Personalization is about data

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Personalization• Collaborative filter• User profile• Data analysis tools

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How to gather user data?• Forms• Web usage mining (click analysis)• Cookies• Rating• Collaborative filters

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Collaborative filters• Choice of content based on like-minded people• Guessing based on previous actions• Recommendations based on profile

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Privacy is …Privacy is not simply an absence of information about us in the minds of others; rather it is the control we have over information about ourselves.

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Charles Fried

Image credit: http://www.arcotrasporti.it/





http://www.arcotrasporti.it

http://www.arcotrasporti.it



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Privacy is not dead• … but misunderstood• Privacy: Control of how information flows• Example: Google Buzz

– opting in vs. opting out

• Example: Chatrooms– The A/S/L question

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danah boyd, SXSW2010



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Social networks• Personal

– Private (ex. Address book)– Public (ex. Facebook)

• Behavioral– People involved in regular communication

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Private and public information• Publicly accessible ≠ Public content • Contextual integrity• Private and public is not as black and white

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Helen Nissenbaum, PRIVACY AS CONTEXTUAL INTEGRITY, 2004



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Privacy – Changing the rules• The default is “everybody”• Publicly available ≠ being publicized

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Privacy in details• PII / PEI

– Personally Identifiable Information– Personally Embarrassing Information

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Privacy and Publicity Mashup• ChatRoulette

– Entertain and be entertained– Sense of anonymity

• Blippy• Jigsaw

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Privacy• "Missed the bus again."• "Attempting to figure out why the cat is hiding."• "I'm signing off."• "On bus going in to the office."• "Scanning pictures of 12-year old girls in mini skirts..."• "Going to bed now."• "Thinking about eating."• "About to start a conference call."• "I'm watching my dog chase the reflection from his tags and wish I had a laser

pointer!"• "Feeling so bored at work I'm going to die. Wonder if it is my attitude or the work."• "Washing hair. Fetching groceries."

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The Twitter Curve

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http://headrush.typepad.com





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Subversives• Finding “Subversives” [Tom Owad]• Amazon: Wish lists of 100ʼ000 of people• Yahoo people search: name, city, state• Google maps:

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http://www.applefritter.com/bannedbooks





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Past Developments• Web 2.0• Blogging and micro-blogging• Social networks• Web services



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Web 2.0• Marketing term• 1999 [Darcy DiNucc], 2004 [John Battelle, Tim O'Reilly]



http://en.wikipedia.org/wiki/John_Battelle

http://en.wikipedia.org/wiki/John_Battelle

http://en.wikipedia.org/wiki/Tim_O%27Reilly

http://en.wikipedia.org/wiki/Tim_O%27Reilly

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Web 2.0

Tim Berners-Lee, W3C

“If Web 2.0 for you is blogs and wikis,then that is people to people.

But that was what the Webwas supposed to be all along.”



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WebTrendsWebTrends

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commons.wikimedia.org





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geek and poke





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Web Sites as Web Services• From unstructured to structured information

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ProgramableWeb



• Open data is a competitive advantage

Business Data

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Web Sites as Web Services

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Web Site

Customers



• Open data is a competitive advantage

Business Data

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Web Sites as Web Services

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Customers Additional Businesses

Mashups

Web Site

Business Data

Web Service API



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Showcase

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• Little efficient helpers ➜ Increase productivity➜ Focus

• Shareability➜ Collaboration➜ Visibility

• Contextualization➜ Semantic



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Where are we going?

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Web 1.0 Web 2.0 Web 3.0

Read-only45 Mio users (1996)Focus: CompaniesHome PagesOwning ContentHTML, PortalsWeb formsPage views

read-write1 Bio users (2006)Focus: CommunitiesBlogsSharing ContentXML, RSSWeb AppsCost per click

Personal mobile WebIndividualFocus: LifestreamConsol. dyn. contentSemantic WebUser behaviorPlatforms



Semantic Web Future Web

Web Social Software

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Where are we going?

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Info

rmat

ion

Con

nect

ivity

Social Connectivity



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Summary• Digital devices always connected and networked

– Car, Radio, TV, Fridge, …

• Mobile– Dominant platform to get to the net

• Privacy– Almost none, all in the same boat

• Semantic on the way– Presumably not in a top-down manner

• Information pollution and overload

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