1. Confab Tutorial Jason I. Hong Chris Beckmann Jeff Heer Alan Newberger G r o u p f o r User Interface Research University of California Berkeley

2. Feb 18 20032 Vision “A hospital Mirror World has a software version of every patient, doctor, bed, room – and every abstract entity that’s important: cash in the bank, drugs on order, and so on. Through permanent sensors and ordinary terminal- based record-keeping, the Mirror World reflects the real one.” – David Gelernter, Mirror Worlds

3. Feb 18 20033 Vision What if… – …we could create a world model that describes salient aspects of the world in real-time? – …everyone could contribute to this world model in the same way that everyone can contribute to the global World Wide Web? Such a World Model would greatly facilitate construction of context-aware apps – App developers wouldn’t have to hand craft each model – Streamline sharing of context data – Uniform façade around diverse sensors and software APIs

4. Feb 18 20034 Tutorial Outline Context Data Model Programming Model Liquid Distributed Querying The Messy Details

5. Feb 18 20035 Context Data Model High-Level Rationale

6. Feb 18 20036 Context Data Model High-Level Rationale Web (Global Scope) (Context for people) Web Service (Specialized API) (Global Scope) (Context for computers) Train Info Board (Local Scope) (Context for people) Unified API (Network effects) (Limited Scope) (Context for computers)

7. Feb 18 20037 Context Data Model High-Level Overview Beacon Web Scraping Sinks Data Stores Sources Context-Aware Applications Context Data Sensors, Beacons, Databases, Web pages Auto Diary HVAC Context Data Layer Sources Sinks Spaces Sensor Public Display Manual Input Context Browser

8. Feb 18 20038 Context Data Model Alice’s InfoSpace LocActivit y Health Room 525’s InfoSpace Person Device PDA- 1138’s InfoSpace Owner

9. Feb 18 20039 Context Data Model Division of Responsibilities InfoSpace Server InfoSpace Tuple Analogous to web servers Manages a collection of InfoSpaces Unit of administration Unit of deployment Analogous to a web site / homepage Represents context data about an entity Represents zone of protection Manages collection of context tuples Unit of ownership and addressing Analogous to individual web page Represents single piece of context data Contains privacy preferences and metadata Unit of storage

10. Feb 18 200310 Context Data Model InfoSpaces Distributed world model – Each with partial and incomplete knowledge of world – Each with a different perspective of the world Represents three different things – Context data about an entity “My name is John”, “I am hungry” – Context data perceived by that entity “I am with Alice”, “I am in room 525” – Context data queried by that entity “Carol tells me that the dog is in the kitchen” Managed by individual represented or by admin – Like a homepage

11. Feb 18 200311 Context Data Model InfoSpaces TupleSpace meets Web TupleSpace – A shared data space – add(), remove(), query(), subscribe(), unsubscribe() – Complexity shifted into data model and query language Web – Leverages existing technology (ex. firewalls) – Leverages well-understood models for administration, deployment, authoring, and programming – End-user mental model – Independent deployment & anarchic scalability [Fielding]

12. Feb 18 200312 Example Model InfoSpaces Alice’s InfoSpace Tuples Out-Log In-Log Out-Subscriptions In-Subscriptions Properties Policies

13. Feb 18 200313 Tuples Represent a discrete piece of context data Contains: – Context data – Metadata – History of that data – Privacy information

14. Feb 18 200314 Context Data Model Tuples StaticDynamic Intrinsic Name Height Extrinsic Room 525 part of Soda Room Temperature Hospital Bed Empty Alice is in Room 525 People in Room 606

15. Feb 18 200315 Querying XPath is a language for addressing parts of an XML document – Think of an XML document as a tree-structure http://12.233.57.65:8080/infospace/jasonh – ?q=//ContextTuple[@datatype='location.room'] – &sortby=//ContextTuple/@timestamp-created – &sortorder=descending – &num=2 Snapshot – Get current state

16. Feb 18 200316 Querying XPath Explorer

17. Feb 18 200317 Operators – Small components for transforming data – Extensibility without having to modify the main code In-operators – Check Privacy Tag Out-operators – Add Privacy Tag – Clear Sources – Sort On-operators – Garbage Collection – Periodic Report

18. Feb 18 200318 Operators Example Data-Flow Alice’s InfoSpace In OperatorsOut Operators Tuple HTTP Front-end

19. Feb 18 200319 Sink-Side Overview Confab Client Alice’s InfoSpac e Bob’s InfoSpac e Room 22 InfoSpac e InfoSpace Server HTTP Front-end Active Properties Listeners

20. Feb 18 200320 Sink-Side ConfabClient ConfabClient – Java client-side API for accessing InfoSpaces – add(), remove(), query(), subscribe(), unsubscribe() ActiveProperties “lederer.location” “lederer.activity” “lederer.temp” OnDemandQuery PeriodicQuery Subscription “606” “Napping” “98.6”

21. Feb 18 200321 Source-Side Simulator

22. Feb 18 200322 Putting it all together In / Out Board for Room 410 Approach 1 – Query each individual Approach 2 – Query the room “lederer.loc” “klemmer.loc” “mattkam.loc” PeriodicQuery …… “room410.occupants”PeriodicQuery

23. Feb 18 200323 The Messy Details Download these packages JDK 1.4 Tomcat 4.1.18 Web Server – http://jakarta.apache.org/builds/jakarta-tomcat- 4.0/release/v4.1.18/bin/ CVS – we like TortoiseCVS, http://www.tortoisecvs.org/ Ant 1.5.1 Build System – http://ant.apache.org/ Pageant and Putty Public-Key – http://www.chiark.greenend.org.uk/~sgtatham/putty/download.html

24. Feb 18 200324 The Messy Details SourceForge SourceForge Open Source Repository – Create account at http://sourceforge.net/ – Create and upload your public-key – Join Confab dev-team if you want to CVS commit http://sourceforge.net/projects/confab/ No team t-shirts yet – CVS checkout latest snapshot

25. Feb 18 200325

26. Feb 18 200326 Privacy Layer perspective – Each layer responsible for security and privacy between layers Dataflow perspective – Tuples contain data about usage – Digital rights management

27. Feb 18 200327 Related Work – Semantic Web / DAML Semantic Web has no story for – Individuals managing their data – Handling sensor data and dynamic updates – Where specific pieces of data live Confab is simpler – Complexity of Semantic Web is huge barrier to entry – Start simple

28. Feb 18 200328 Related Work – Context Toolkit Focus first on the data model rather than sensors Early mapping of sensor to ontology Per sensor managment

29. Feb 18 200329 Related Work – ParcTab System Confab is an evolution of ParcTab system

30. Feb 18 200330 Related Work – EventHeap / iRoom

31. Feb 18 200331 Related Work –

32. Feb 18 200332 Related Work –

33. Feb 18 200333 Motivation Modern computers divorced from our reality – Unaware of who, where, and what around them – Mismatch between our expectations and functionality – Also limits what we can do with computers Computers have extremely limited input – Aware of explicit input only – A lot of effort to do simple things (or to remember) Context-Aware Computing – One line of ubiquitous computing research – Making computers more aware of the physical and social situations they are embedded in

34. Feb 18 200334 Examples of Using Context Context TypesExisting ExamplesHuman Concern Room ActivityAuto Lights On / OffConvenience Personal Identity & Time File SystemsFinding Info TimeCalendar RemindersMemory Activity Finding Info Safety Time Location Activity Health Alert Tag Photos History Identity Proximity Efficiency Service Fleet Dispatching Context Types Potential ExamplesHuman Concern

35. Feb 18 200335 Technology Trends Sensors – GPS, Active Badges, Active Bats – Smart Dust – Cameras and microphones Recognition algorithms – MSR Radar location from 802.11 – Smart Floor footstep force Wireless technologies – Bluetooth, 802.11, cell phone

36. Feb 18 200336 A New Class of Context-Aware Apps Active Badge (Olivetti) ParcTabs (Xerox PARC) Cyberguide (Abowd et al)

37. Feb 18 200337 A Computational View of Context Context as a strategy for building apps Increasing the number of input channels into the computer – Pushing towards implicit acquisition of data Creating better models – Pushing towards the physical and social Using the input and models in useful ways – Proactively taking predictable and meaningful actions – Tagging other information for future lookup – Passing on more information to people

38. Feb 18 200338 A Computational View of Context Autonomy Sensing Lights FileSystem Calendar Tag Photos Health Realtime Dispatching Pervasiveness Inference Fusion Models

39. Feb 18 200339 Two Problems with Context- Awareness Scalability – Lots of people, places, things, and sensors – Over long periods of time – Over large geographic distances – Sharing resources (sensors and data) Privacy – Tremendous source of valid criticism – Need architecture and mechanisms to safeguard personal data and make it easy for people to manage

40. Feb 18 200340 Research Goals and Solution Overview Provide network-oriented set of abstractions, mechanisms, and programming model Scalability – Data-oriented P2P repositories called information spaces – Different infospaces federate when needed Privacy – Provide suite of mechanisms for app developers – Based on Fair Information Practices and Information Asymmetry

41. Feb 18 200341 Talk Overview  Motivation  Research Overview  Confab Architecture – Scalability  Confab Architecture – Privacy  Status  Context + Whisper thoughts  Context + SpeakEasy thoughts

42. Feb 18 200342 Architectural Abstractions Information Spaces – P2P TupleSpace repositories of context data and operators – Associated with entities (people, places, things) – Somewhat similar to web servers and home pages Context Data – Representation for context data Operators – Reusable and composable code operating on data Context Queries / Notifications – Simple API for accessing context

43. Feb 18 200343 Architectural Sketch Information Spaces Carol's InfoSpace (Desktop) Information Spaces Carol's InfoSpace (PDA) Soda 525 InfoSpace (Server)

44. Feb 18 200344 Architectural Sketch Context Data Loc Act Loc Context Data Information Spaces

45. Feb 18 200345 Architectural Sketch Operators Context Data Information Spaces TransFilter Log Operators

46. Feb 18 200346 Architectural Sketch Context Queries Loc Context Data Information Spaces TransFilter Operators Query Loc Trans

47. Feb 18 200347 Architectural Sketch Context Notifications Context Data Information Spaces Operators Notification (Standing Query)

48. Feb 18 200348 Architectural Sketch Peering of Information Spaces Carol's Context when Mobile Carol's Context in Room 525 Context = Set of Available Info Spaces

49. Feb 18 200349 Emergency Response Scenario Part of a suite of context-aware apps under development for fire or earthquake situations Keep track of people in a building – Allow building managers to check if a building is clear in the event of an evacuation – Allow firefighters to check where people were Provide reasonable privacy protection – People don't like to be tracked – Emergency situations relatively rare

50. Feb 18 200350 Emergency Response Scenario Registering with the Building's InfoSpace Building InfoSpace Carol's InfoSpace Smart Dust User="Carol" Location="525 Soda Hall" Time="Apr 12 1:05PM" Access Control User="Carol" Location="5th floor" Age="37 seconds" Send location info Logging User="Carol" Location="in" Age="37 seconds" Blurring

51. Feb 18 200351 Emergency Response Scenario Querying during an Emergency Building InfoSpace Carol's InfoSpace Smart Dust User="Carol" Location="5th floor" Age="37 seconds" User="Carol" Location="525 Soda Hall" Time="Apr 12 1:05PM" NotificationLogging User="Carol" Location="525 Soda" Age="7 seconds"

52. Feb 18 200352 Layers of InfoSpaces and Context Data Physical Logical View My Location on PDA My Location on PC My Location to Strangers My Location to Friends My Location to Family

53. Feb 18 200353 Scalability Recap Architecture analogous to web – Information spaces are like web servers – Information spaces contain context data – Context data is eventually consistent (helps availability) Differences from web architecture – Each device contains an information space (so devices can access context even w/o net access) – Information spaces contain reusable operators for manipulating and protecting context data

54. Feb 18 200354 Talk Overview  Motivation  Research Overview  Confab Architecture – Scalability  Confab Architecture – Privacy  Status  Context + Whisper thoughts  Context + SpeakEasy thoughts

55. Feb 18 200355 Privacy Philosophy Fair Information Practices Notice Choice Onward Transfer Access Security Data Integrity Enforcement

56. Feb 18 200356 Privacy Philosophy Information Asymmetry “In all of human history, no government has ever known more about its people than our government knows about us. And in all of human history, no people have ever been anywhere near as free.” (Brin)

57. Feb 18 200357 Some Desired Privacy Features Intentional ambiguity – "Where is Victoria?" "Chez Panisse" -> "Berkeley" -> "CA" – Give different answers depending on requestor Plausible deniability – "Is Adam busy?" "Yes" or "Unknown" according to prefs Risk Avoidance – "Mark does not trust this person / infospace" Tracking – Who has my data? What are they doing with it? – (Also a reverse-privacy issue?)

58. Feb 18 200358 A Privacy Design Space Legal Social Economic Technology Detection Avoidance Prevention CollectionSecond UseAccess Themes for Minimizing Asymmetry Data Lifecycle Anonymization Pseudonymization P3P RBAC Location Support Privacy Mirrors Wearables User Interfaces for Feedback, Notification, and Consent Goal: Provide reusable mechanisms that can populate this design space

59. Feb 18 200359 Privacy Trust Model Optimistic – I trust you and your current infospace – Make it easy for others to do "the right thing" [tm] Pessimistic – I don't trust you or your current infospace – Modify the data assuming you will do "the wrong thing" (more blurring or watermarking) – Or don't send the data to you at all Make it easy to support spectrum of trust models between full optimistic and full pessimistic

60. Feb 18 200360 Two Privacy Mechanisms Operators Privacy Tags – Preferences for how personal data should be used – "Don't forward to anyone else" – "Don't fuse with other pieces of data" Garbage collectionRemove or aggregate old data BlurringIncrease ambiguity Access ControlCheck authorization LoggingDetection FiltersRemove certain data

61. Feb 18 200361 Talk Overview  Motivation  Research Overview  Confab Architecture – Scalability  Confab Architecture – Privacy  Status  Context + Whisper thoughts  Context + SpeakEasy thoughts

62. Feb 18 200362 Status Still in early-to-mid phases – Currently developing initial implementation – JDK, JXTA (Java P2P), XML – Possibly also WSDL, SOAP Target applications – SpeakEasy (PARC) – Suite of Emergency Response apps – Possible Educational Technology apps "Metrics" – Types of and effectiveness of apps that can be built – Ease of adoption – Robustness

63. Feb 18 200363 The Ultimate Metric

64. Feb 18 200364 Some Context + Whisper Thoughts Use location + activity to help determine level of security – Within "safe" boundaries use low security – Within "unsafe" boundaries switch to high security, provide more feedback, and avoid risky situations (talking to strange computers) Boundaries can be based on: – People nearby (Social) – Activity – Location (Physical) Use contextual information from sensors and other sources to help determine these boundaries

65. Feb 18 200365 Some Context + SpeakEasy Thoughts Useful context for components – History of usage / Inferred patterns of usage – Location of component Useful context for people – Location of person – Personal history of usage / Inferred patterns – Shared history of usage (how others have used) – Activity ie "It looks like you're doing a presentation" Make it easy, or automate some things How well can you guess activity from simple data? How well can you do it over time?

66. Feb 18 200366 Q & A Focus, Jason, focus! Jen Mankoff Asst Prof Berkeley Privacy is good here, but be careful not to fall into the systems tarpit. Bill Schilit Intel Labs Seattle Co-director

67. Feb 18 200367 Q & A Agree with Bill do I, beware the dark side of systems you must! Yoda Jedi Master Kickass Dude

68. Feb 18 200368 Q & A Good work, Jason, I think you deserve a raise! James Landay Assoc Prof Berkeley My Advisor This party's started! Mace Windu Jedi Master Also a Kickass Dude

69. Jason I. Hong http://guir.berkeley.edu/cfabric G r o u p f o r User Interface Research University of California Berkeley Thanks to: DARPA Expeditions PARC Intel Fellowship NSF ITR Yoda Context the circumstances in which an event occurs; a setting; to join; to weave

70. Feb 18 200370 Q & A Maybe privacy won't be a large issue in the future. Very difficult to say because of the tradeoffs in value, safety, convenience. One way of evaluating is to describe the design space, and show how your work makes it easy to build in that space.

71. Feb 18 200371 Q & A But do we really need ubicomp at all? And if so, how do we build and evaluate it so that it's socially relevant and meaningful? Maybe context itself isn't really the issue, because activity orders and delineates what is and isn't relevant at any point.

72. Feb 18 200372 Functional Requirements Context Acquisition – Getting the data from a variety of sources Context Modeling – Representing the data Context Storage and Dissemination – Storing the data – Making the data available when it is needed Context Usage – Using the data in a program

73. Feb 18 200373 Context Data Problem: how to represent context data? Entities – Like nouns, people, places, and things Attributes – Like adjectives or properties, key-value pairs Relationships – How one entity relates to another entity Aggregates – Actions, Groups of people

74. Feb 18 200374 Context Data Person="jasonh@cs.berkeley.edu" Name="Location" Value="Room 525" Schema="Building:Room" Metadata= Time="1023498143" Time-to-Live="60sec" Source="SmartDust" Name="Device" Value=http://zzz.comhttp://zzz.com Schema="Device" Entity Attribute Relationship

75. Feb 18 200375 Key Architectural Abstractions Information Spaces – Repositories of context data and operators Context Data – Representation for context data Operators – Composable code operating on context data Context Queries / Notifications – Simple query language (like SQL for DB) – Push / Pull semantics

76. Feb 18 200376 Information Spaces Problem: where to store context data? Information Spaces analogous to web servers – Have a unique name – Have an owner – Contain multiple (and not necessarily related) pieces of data – Can get / put pieces of data (given security and privacy prefs)

77. Feb 18 200377 Operators Problem: how to manipulate context data in a reusable manner? Chainable Operators Data-type ConversionEx. Celsius -> Farenheit FusionRefine same data type CompositionMerge different data types Garbage collectionRemove or aggregate old data BlurringIncrease ambiguity Access ControlCheck authorization LoggingDetection FiltersRemove certain data

78. Feb 18 200378 Context Queries Problem: how to use context data?

79. Feb 18 200379 Related Work Context Toolkit EventHeap ParcTab infrastructure

80. Feb 18 200380 Existing Examples of Using Context Context TypesExisting ExamplesHuman Concern Room ActivitySmoke AlarmSafetyRoom ActivityAuto Lights On / OffConvenienceObject IdentityBarcode ScannersEfficiency Personal Identity & Time File SystemsFinding InfoTimeCalendar RemindersMemory

81. Feb 18 200381 Potential Examples of Using Context Existing ExamplesContext Types Potential ExamplesHuman Concern ActivityConvenience ActivityFinding Info IdentityMemory Identity & TimeSafety TimeEfficiency Identity Time Location Proximity Activity History … Health Alert Auto Cell Phone Off In Meetings Service Fleet Dispatching Tag Photos Proximal Reminders

82. Feb 18 200382 Defining Context Abowd & Dey / Moran & Dourish "Any information that can be used to characterize the situation of an entity, where an entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and the application themselves. Context is typically the location, identity, and state of people, groups, and computational and physical objects." (Abowd and Dey) "Context refers to the physical and social situation in which computational devices are embedded" (Moran and Dourish)

83. Feb 18 200383 Defining Context Distributed Cognition Distributed cognition – Need to go beyond physical attributes (ex. temp) – Look at “state of digital resources, people’s concepts, task state, social relations, local work culture” (Kirsh) – Model key attributes and deep structure of whole system (individuals, offices, social structs, work practices) Problems – What are the key attributes? – How to represent?

84. Feb 18 200384 Defining Context Situated Action Situated action – Actions are fluid, moment-by-moment, improvised, often unplanned, and highly context- dependent – “[T]he context in which actions take place is what allows people to find it meaningful” (Dourish) Problems – Very high-level form of context – Can low-level computer-based context be useful? – Also, how does this really help us build systems?

85. Feb 18 200385 Defining Context Phenomenology Phenomenology – Reality consists of objects and events as they are perceived in human consciousness and not of anything independent of human consciousness. – Meaning (and hence context) arises from the ways in which we engage with and act within the world Problems – Need this level of sophistication to make progress? – How does this help us build systems? – Very wide chasm between philosophy and practice

86. Feb 18 200386 Defining Context My Perspective Point #1 – Not clear if we need a solid definition – Operating systems and Artificial Intelligence Point #2 – Let's treat it like "information" – Shannon treated it from a mechanical perspective (i.e. transmission) made great inroads – We are still debating the meaning of "information" – But now we can do it electronically Let's treat context from computer perspective – Let designers define context app-by-app – Provide generic reusable mechanisms (like DB)

87. Feb 18 200387 Privacy Privacy is a relatively new concept in society, and is “ultimately a psychological construct, with malleable ties to specific objective conditions” (Grudin) – Convenience, Safety, Efficiency – Ex. Credit cards and cell phones Open access to online calendars for efficiency and awareness (Palen)

88. Feb 18 200388 Designing Context-Aware Systems Minimize automatic actions – Probably cost-to-benefit via decision theory (value, error, correctness) Provide feedback – What is being captured? – Why did the system do that? Feed-forward – If you do that, then the system will do this Confirmation – The system just did the following action Endpoint – Context for people or context for computers?

89. Feb 18 200389 Vision Context-Aware Computing Today

90. Feb 18 200390 Vision Context-Aware Computing in the Future January

91. Feb 18 200391 Example Model Organizing End-User Devices, Services And Applications Sensors and Beacons (Mobile and Infrastructure) Data Sources (Personal, Group, Public) ??? January

92. Feb 18 200392 Example Model Organizing End-User Devices, Services And Applications Sensors and Beacons (Mobile and Infrastructure) Data Sources (Personal, Group, Public) ???

93. Feb 18 200393 Example Model Organizing Context Data Model Layer Sources Sinks

94. Feb 18 200394 Example Model Organizing Confab Client Alice’s InfoSpac e Bob’s InfoSpac e Room 22 InfoSpac e InfoSpace Server HTTP Front-end Active Properties

95. Feb 18 200395 Example Model Active Properties “scott.location” “scott.activity” “current-device.room”

96. Feb 18 200396 Example Model Division of Responsibilities InfoSpace Server InfoSpace Tuple Analogous to web servers Manages a collection of InfoSpaces Unit of administration and deployment Unit of deployment Analogous to a web site / homepage Represents context data about an entity Represents zone of protection Manages collection of context tuples Unit of ownership and addressing Analogous to individual web page Represents single piece of context data Contains privacy preferences and metadata Unit of storage

97. Feb 18 200397 Example Model Evolution of Context-Aware Systems

98. Feb 18 200398 Example Model A Predicted Evolution of Context-Aware Systems Web (Global Scope) (Context for people) Web Service (Global Scope) (Context for computers) Train Info Board (Local Scope) (Context for people) Unified API (Global Scope) (Context for computers) (Network effects) Restricted Scope (Global Scope) (Context for computers) (Network effects)

99. Feb 18 200399 Example Model A Predicted Evolution of Context-Aware Systems Train Info BoardSensor InputManual Input

100. Feb 18 2003100 Example Model Physical, Logical, and View Sinks Data Stores Sources Context-Aware Applications Context Data Sensors, Beacons, Databases, Web pages

101. Feb 18 2003101 Example Model Intrinsic and Extrinsic Context Loc

102. Feb 18 2003102 Berkeley CS InfoSpace Server Example Model Single InfoSpace Server Alice’s InfoSpace Room 525’s InfoSpace HTTP Front-end In-Operators Out-Operators On-Operators Tuple InfoSpaceAccess

103. Feb 18 2003103 Example Model Context Data Model Alice’s InfoSpace Room 525’s InfoSpace LocActivit y PDA- 1138’s InfoSpace Person Device Owner Health

104. Feb 18 2003104 Example Model InfoSpaces Alice’s InfoSpace MotionGPS Health Monitor Service Heartbeat Personal Loc Triggers Auto Diary Calorie Tracker

105. Feb 18 2003105 Example Model InfoSpaces Activit y Alice’s InfoSpace Room 525’s InfoSpace LocActivit y PDA- 1138’s InfoSpace Person Device Owner Health InfoSpac e Context Data Op

106. Feb 18 2003106 Example Model Adding Data Loc Fusion Active Badge Wireless Triangulation Alice’s Laptop’s InfoSpace Alice’s Laptop’s InfoSpace Loc.Tri Alice’s InfoSpace Alice’s InfoSpace Loc. Active- Badge Loc.Tri SUBSCRIBE Loc.* SUBSCRIBE Loc.* SUBSCRIBE Loc

107. Feb 18 2003107 Example Model Adding Data Loc Fusion Active Badge Wireless Triangulation Alice’s Laptop’s InfoSpace Alice’s Laptop’s InfoSpace Loc. Active- Badge POST Loc.Tri POST Loc.Tri Alice’s InfoSpace Alice’s InfoSpace Loc. Active- Badge Loc.Tri POST

108. Feb 18 2003108 Example Model Transforming Data Acquaintance’ s InfoSpace Acquaintance’ s InfoSpace Type=“Location” User=“Alice” Location=“Berkeley, CA” Time=“Oct 06 1:05 PM” Notify=“alice@me.com” Pref=“Do not forward” Time-to-Live=“1 week” Type=“Location” User=“Alice” Loc=“525 Wozniak Hall” Time=“Oct 06 1:05 PM” Time-to-live=“Forever” Building’s InfoSpace Alice’s InfoSpace Type=“Location” User=“xyzzy” Loc=“5th floor Wozniak Hall” Time=“Oct 06 1:05 PM” Notify=“someone@me.com” Pref=“Do not forward” Pref=“Emergency use only” Time-to-Live=“1 hour”

109. Feb 18 2003109 Address Name Occupied Hospital Phone# Name Patient Doctor Temperature Room Number Room

110. Feb 18 2003110 Example Model Physical, Logical, and View Hospital's InfoSpace Room 525's InfoSpace Occupied Temperature Room Number Room 527's InfoSpace Occupied Temperature Room Number Dr. X's InfoSpace Room Phone# Name Room Doctor Patient Y's InfoSpace Heart Rate Room Address Name Patients

111. Feb 18 2003111 <ContextTuple dataformat="http://badge.hospital.org/map.jsp" datatype="location.room" description="Represents location of an entity" entity-name="Doctor X" timestamp-created="2002.Dec.02 14:28:39 PST"> <Source datatype="location.room" link="http://badge.hospital.org/loc.jsp" source="Active Badge" timestamp="2002.Dec.02 14:28:39 PST" out-tid="3F8B4528" in-tid="22A4610F" value="525" />

112. Feb 18 2003112 Example Model InfoSpaces and Tuples Room 525's InfoSpace Occupied Temperature Room Number 525 527 535 537 Dr. X's InfoSpace Activity Room Phone# Name Power Monitor Personal Diary Context Sources Context Sinks Context Data Model Dr. X

113. Feb 18 2003113 DBCA

114. Feb 18 2003114 ACF AccessSecond Use Alice's Location Alice's Location

115. Feb 18 2003115 B A