• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Contextual Information
 

Contextual Information

on

  • 8,894 views

Presented for TTI Vanguard "Shift Happens" conference (http://bit.ly/TTIVshifthappens) visit to PARC, this represents a slice of some of our work in contextual intelligence.

Presented for TTI Vanguard "Shift Happens" conference (http://bit.ly/TTIVshifthappens) visit to PARC, this represents a slice of some of our work in contextual intelligence.

Statistics

Views

Total Views
8,894
Views on SlideShare
7,405
Embed Views
1,489

Actions

Likes
36
Downloads
0
Comments
0

23 Embeds 1,489

http://www.readwriteweb.com 984
http://readwrite.com 167
http://rfid.punt.nl 72
http://www.lifeinbeta.org 61
http://innovacion.ticbeat.com 60
http://www.sensoruniverse.com 28
https://192.168.1.102 26
http://innovacion.readwriteweb.es 24
http://swik.net 15
http://www.slideshare.net 10
https://www.readwriteweb.com 9
http://static.slidesharecdn.com 6
http://www.showbizdata.com 5
http://translate.googleusercontent.com 4
https://showbizdata.com 4
http://www.365online.nu 3
http://www.alltechienews.com 3
http://www3.showbizdata.com 2
http://www.hanrss.com 2
http://www.seofacts.biz 1
http://webcache.googleusercontent.com 1
http://www.netvibes.com 1
http://italy.gadgetmug.com 1
More...

Accessibility

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • collect data about people’s every day lives. several data types (e.g., time, location, motion, computer use, cellphone use, object use, people nearby) several data sources (phones, computers, fixed infrastructure (security cameras, polycoms, etc.)) process this data. draw “higher level conclusions” such as place visited, activity performed, project worked on. exploit the data. e.g: help people find stores and restaurants they will like. help advertisers target better. help users coordinate with each other more easily. help epidemiologists understand disease sources. help enterprise workers automatically organize notes. ------------- Behavior -/ activity -aware systems: Just being aware of the situation is not sufficient Applications must behave appropriately in the situation - Models human behavior and activity (whereby “behavior” refers to a person’s actions or reactions, usually in relation to the environment, and can be conscious or unconscious, overt or covert, and voluntary or involuntary; while “activity” is a conscious, voluntary pursuit) - Enable applications that use context-aware data collection, with their primary value centers on the ability to infer, and potentially respond to, present behavior, instead of intent. Examples: - Consumer observation - Health monitoring - Elder care - Interruptibility modeling - In-situ information delivery - Security monitoring - “Contextual” reminders
  • The fieldwork led to this general architectural vision. I’ll show you a more detailed View on the next slide, but I’d like you to understand the high-level picture first. As I mentioned at the start, our goal is to spontaneously provide appropriate recommendations. The system does this by determining both long-term preferences of genres of things that you like, and your immediate situation through Contextual data. From contextual data, the system estimates what activities You are likely to perform, filters and ranks the items in its database and returns To the client a useful list. When the user reviews the list, they may leave feedback, which is later used to Update their preferences. Now to explain how activity is represented, I have to go into a little more detail about The content recommender server
  • Next, we rank each piece of content in the repository according to the likely utility of the content based on a model of the user's personal preferences which is generated from multiple sources: The user's explicitly stated preferences, The ratings they've made of items they've seen and done, The topics of documents and web pages they've looked at, And what the user has done in the past. This generates a utility score for each item which determines the ranking of each item in the interface.

Contextual Information Contextual Information Presentation Transcript

  • Contextual Information Bo Begole, Ph.D. Ubiquitous Computing Computing Science Laboratory
  • confidential
    • Support revolutionary new context-enabled applications
    Information and people always linked to each other thru context Information self-organizes for faster retreival New context-enabled applications for vertical markets Better generic applications such as enterprise semantic search
    • Sensed
    • Location
    • Proximity to other devices
    • Inferred from activity
    • Author
    • Task, activity
    • Relation to other documents
    • Relation to people
    • Explicit
    • Social tagging
    • Comments
    • Bookmarks
    • Extracted from content
    • Topic
    • Author
    • Document type
    • People, places, and things
    • Key concepts
    • Project
    • Automatically build context around information & interactions
    Harness content and context relationships to make knowledge use much easier People Content Tasks Events Places Topics
  • Contextual Intelligence
    • Converting the ocean of unstructured , human readable information into a Personal Semantic Network would allow you to:
      • See more
        • Discover new information serendipitously
        • Deliver information proactively
      • See less
        • Better filter and sort search results
        • Auto-update business processes
      • See relationships
        • Summarize related items
        • Support multi-tasking
    email web plain text images audio office documents People Content Tasks Events Places Topics
  • Combine context data and cognitive models Activity model: User goals, beliefs and desires Physical context Location, Time, Social context Behavior model Past actions (individual, population) Preference model Tastes, Interests, Expertise Electronic context Calendar, Calls, Email, Documents Advertising Community monitoring Information retrieval Spatial/Time patterns Time tracking Power savings Support of science Group default behavior Group coordination Better usability Health monitoring
  • Recommendation service Consumer Local area Mobile device
    • Leisure information and suggestions based on
      • Situation
      • Past behavior
      • Personal preferences
    Trials in Japan, spring 2010 [ Nikkei Trendy Net ] Magitti: Activity-aware leisure guide Context: Time, location, recent messages, etc. Restaurants, stores, events, etc. Filter and rank information items Infer activity Feedback Model preferences
  • Magitti demo video http://www2. parc . com/csl/groups/ubicomp/videos/magitti_project_demonstration . wmv
  • Recommendable items Restaurant reviews Store descriptions Parks descriptions Movie listings Museum events Magazine articles … Filtering and ranking Activity Utility Information What you like
    • Personal Preferences
    • Explicit preferences
    • Rating of items inspected
    • Analysis of content read
    • Behavior; where/when/what
    • Context
    • Time
    • Location
    • Email analysis
    • Calendar analysis
    • History
    • Prior population patterns
    • User queries
    • User locations
    Eat Buy See Do Read What you are doing now EAT Straits Cafe 0.77 EAT Fuki Sushi 0.64 SEE J. Gallery 0.60 EAT Tamarine 0.57 DO Sam’s Salsa 0.39 EAT Bistro Elan 0.38 BUY Apple Store 0.33 EAT Spalti 0.31
  • Levels of Contextual Intelligence email web pages plain text images audio forms office documents Systems observe typical action-in-context patterns and deliver information that the user would not otherwise have known to look for. Systems extract and present relationships discovered in information to augment human sense-making . Users manually search, sort, sift and associate to find meaning and make sense Systems filter and sort information based on the user’s current context to increase efficiencies of search and discovery . Contextual Intelligence
  • Industry analysts takes on Contextual Intelligence
    • Gartner: Context-Enhanced Services
      • Major disruption over next 5-10 years
    • Forrester: Smart Computing, Business Intelligence
      • From ~$7B in 2008 to ~$14.5B by 2014
    • IDC: Context-based Information Retrieval
      • Real-time decision making
    • Applications range from consumer …
      • Leisure activity guide (Magitti)
      • Smart retail environments (Responsive mirror)
    • … to business
      • Personal information context (Ubidocs)
      • Group information context
      • Enterprise information repositories
      • Social cognition for knowledge creation (ASC)
    -Forrester Smart Computing -Gartner, What’s in the Lab -IDC Software Predictions 2010
  • To subscribe to the PARC Innovations Update e-newsletter or blog and other feeds, or to follow us on Twitter, go to www.parc.com/subscribe For more information, please contact: Bo Begole, Principal Scientist Bo. [email_address] .com Lawrence Lee, Business Development Lawrence. [email_address] .com