Dawn Nafus Tye Rattenbury Ken Anderson Add GPS and Stir? Some Context for Context Awareness
The shift to “context aware” systems has been a shift away from active user input toward leveraging passively collected datasets
Systems now add value by giving new life to data
Nike uses accelerometer data to tell customers about their run
Google takes search terms to ‘contextualize’ ad placement
Social networking sites present new contacts, ‘community’ visualizations based on system data
Though computer scientists would have a stricter, more additive, definition -
Accelerometer + Humidity= running context
Nike would have to add in humidity measurements in order to infer something about the run
Person + Location =Friend
Person + Location+ Same MP3s on iPhone=Potential New Friend
There’s excitement both in the marketplace AND in labs Like Bill Murray’s Groundhog Day, some tech scenarios just won’t go away.
On the market
Google’s astronomical rise through ‘contextual’ ads
Other opportunities persist as the perpetual ‘next big thing’
The ‘use location data to throw coupons at the passerby’ model won’t die
Many new friend finding startups, though not yet mass market
In the lab
Sensor technology rapidly developing
Inferencing and machine learning capabilities are on the increase
Compute resources growing (data aggregation and sharing, compute power)
… but there’s something fishy about the word ‘context’
Things that are machine readable
What’s relevant to the conversation
Blink vs wink
House vs home
Commute vs journey
Cooking for pleasure vs cooking for food intake
Things that are person readable
Source: P. Dourish, “What We Talk About When We Talk About Context.” Ubicomp 2004 ROI (getting people to value what you have) Culture (how people understand the world around them)
Which context you focus on leads you to different questions, which lead to different design choices and business models
Things that are machine readable
How much can I reliably detect? How many different data points can I throw together? How can I make machines mash it all up? What activity can I support? How do my end users interpret my data? People Context Things that are person readable
Sensor Context: Location +buddylist =ambient display of routine People Context: Relative to spouses and children Embedded in routines of ‘putting on the kettle’, practices of assurance-giving, as precise as conversations are , allows redefinition of home and away “ Whereabouts Clock”, Brown et al 2006 Location ≠ Context
Think very, very carefully about the following question.
The average office worker has 12 minutes to work before he/she gets interrupted.
You now have a device that tells you whether people of potential relevance to your work are in the office, based on location, other devices present, and historical interaction pattern.
Do you really want something to interrupt you to tell you that you are about to be interrupted?
Context-aware tourism applications Paris photo courtesy of Only_Point_Five
Easily machine sensed
People context present, ritualized
low demand for machine-inferred Eiffel Towers
Too many things to ‘sense’ (building? Shop? Construction materials?)
People context present, but unritualized (unpredictable)
High value– the ‘gem’ you discover on your trip!
Machine recognizable but (semi)long-tail. Who will connect the dots?
Value depends on people’s enthusiasm for a narrow art genre, not necessarily a particular place
Easy, low value Hard, high value Middling
Spurious connections between Sensor Context and People Context are fantastic for art, but cloud ‘prediction’ “ Home Health Horoscope” Gaver et al, 2007 Sensor Context: Condensation on windows + pattern of doors + weight of the coffeepot + … = home health People Context: Sensors mash up horoscopes and give them back to perplexed dwellers who infer their meaning (i.e., do the real ‘sensing’ work) Inferencing that involves more than 10 variables will deliver spurious connections. What will people make of these?
Current research: Mapping connections between People Context and Sensor Context
Data from 8 weeks of device activity used as a cultural probe
How the device ‘fits in’ to people’s daily patterns says a lot about both their lives and what they believe the device ‘does’
machine use is the aspect of life most likely to be interrupted, doesn’t do the interrupting
With a weak ecosystem of players focused on people context, we all suffer sustainability problems
Places for eyeballs to go become limited if no one interacts with the device
Creepyness factor becomes the foreground
People need a good reason to not notice they are creating a database for you
Bias towards literal accuracy can be more annoying rather than less
Non-traditional ads are by definition ‘out of place’—the bar is raised to get it right