Confab Tutorial

Group for User Interface Research Confab Tutorial University of California Berkeley Jason I. Hong Chris Beckmann Jeff Heer Alan Newberger 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 terminalbased record-keeping, the Mirror World reflects the real one.” – David Gelernter, Mirror Worlds Feb 18 2003 2 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 Feb 18 2003 3 Tutorial Outline • • • • Context Data Model Programming Model Liquid Distributed Querying The Messy Details Feb 18 2003 4 Context Data Model High-Level Rationale Feb 18 2003 5 Context Data Model High-Level Rationale Train Info Board (Local Scope) (Context for people) Web (Global Scope) (Context for people) Web Service (Specialized API) (Global Scope) (Context for computers) Unified API (Network effects) (Limited Scope) (Context for computers) Feb 18 2003 6 Context Data Model High-Level Overview Sinks Public Display Auto Diary Context Browser HVAC Spaces Context Data Layer Sources Sensor Beacon Web Manual Scraping Input 7 Feb 18 2003 Context Data Model Health Alice’s InfoSpace Loc Activit y Person Room 525’s InfoSpace Person Feb 18 2003 Device PDA1138’s InfoSpace Owner 8 Context Data Model Division of Responsibilities Analogous to web servers InfoSpace Manages a collection of InfoSpaces Server Unit of administration Unit of deployment Analogous to a web site / homepage Represents context data about an entity InfoSpace Represents zone of protection Manages collection of context tuples Unit of ownership and addressing Tuple Feb 18 2003 Analogous to individual web page Represents single piece of context data Contains privacy preferences and metadata Unit of storage 9 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 Feb 18 2003 10 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 11 Feb 18 2003 [Fielding] Example Model InfoSpaces Alice’s InfoSpace Properties Policies In-Log Out-Log In-Subscriptions Out-Subscriptions Tuples Feb 18 2003 12 Tuples • Represent a discrete piece of context data • Contains: – – – – Context data Metadata History of that data Privacy information Feb 18 2003 13 Context Data Model Tuples Static Intrinsic Dynamic Name Height Room Temperature Hospital Bed Empty Extrinsic Alice is in Room 525 Room 525 part of Soda People in Room 606 Feb 18 2003 14 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 Feb 18 2003 15 Querying XPath Explorer Feb 18 2003 16 Operators • 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 Feb 18 2003 17 Operators Example Data-Flow HTTP Front-end In Operators Alice’s InfoSpace Tuple Tuple Tuple Feb 18 2003 18 Out Operators Sink-Side Overview Active Properties Confab Client HTTP Front-end Listeners Alice’s InfoSpac e InfoSpace Server Bob’s InfoSpac e Room 22 InfoSpac e Feb 18 2003 19 Sink-Side ConfabClient • ConfabClient – Java client-side API for accessing InfoSpaces – add(), remove(), query(), subscribe(), unsubscribe() • ActiveProperties “lederer.location” OnDemandQuery “lederer.activity” PeriodicQuery “lederer.temp” Subscription “606” “Napping” “98.6” Feb 18 2003 20 Source-Side Simulator Feb 18 2003 21 Putting it all together In / Out Board for Room 410 • Approach 1 – Query each individual “lederer.loc” “klemmer.loc” “mattkam.loc” … PeriodicQuery PeriodicQuery PeriodicQuery … • Approach 2 – Query the room “room410.occupants”PeriodicQuery Feb 18 2003 22 The Messy Details Download these packages • JDK 1.4 • Tomcat 4.1.18 Web Server – http://jakarta.apache.org/builds/jakarta-tomcat4.0/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 Feb 18 2003 23 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 Feb 18 2003 24 Feb 18 2003 25 Privacy • Layer perspective – Each layer responsible for security and privacy between layers • Dataflow perspective – Tuples contain data about usage – Digital rights management Feb 18 2003 26 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 Feb 18 2003 27 Related Work – Context Toolkit • Focus first on the data model rather than sensors • Early mapping of sensor to ontology • Per sensor managment Feb 18 2003 28 Related Work – ParcTab System • Confab is an evolution of ParcTab system Feb 18 2003 29 Related Work – EventHeap / iRoom Feb 18 2003 30 Related Work – Feb 18 2003 31 Related Work – Feb 18 2003 32 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 Feb 18 2003 33 Examples of Using Context Existing Examples Context Types Human Concern Auto Lights On / Off File Systems Calendar Reminders Potential Examples Tag Photos Health Alert Service Fleet Dispatching Feb 18 2003 Room Activity Personal Identity & Time Time Context Types Time Convenience Finding Info Memory Human Concern Finding Info Safety Efficiency Activity History Location Activity Identity Proximity 34 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 Feb 18 2003 35 A New Class of Context-Aware Apps Active Badge (Olivetti) (Xerox PARC) ParcTabs Cyberguide (Abowd et al) Feb 18 2003 36 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 Feb 18 2003 37 A Computational View of Context Autonomy • Lights • Pervasiveness •Realtime Dispatching • Inference •Health • Fusion • Models • Calendar • FileSystem • Tag Photos Sensing Feb 18 2003 38 Two Problems with ContextAwareness • 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 Feb 18 2003 39 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 Feb 18 2003 40 Talk Overview Motivation Research Overview Confab Architecture – Scalability Confab Architecture – Privacy Status Context + Whisper thoughts Context + SpeakEasy thoughts Feb 18 2003 41 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 Feb 18 2003 – Simple API for accessing context 42 Architectural Sketch Information Spaces Information Spaces Carol's InfoSpace (Desktop) Carol's InfoSpace (PDA) Soda 525 InfoSpace (Server) Feb 18 2003 43 Architectural Sketch Context Data Context Data Information Spaces Loc Act Loc Act Feb 18 2003 44 Architectural Sketch Operators Context Data Information Spaces Trans Filter Log Operators Feb 18 2003 45 Architectural Sketch Context Queries Context Data Information Spaces Query Loc Trans Filter Trans Loc Operators Feb 18 2003 46 Architectural Sketch Context Notifications Context Data Information Spaces Notification (Standing Query) Operators Feb 18 2003 47 Architectural Sketch Peering of Information Spaces Carol's Context when Mobile Context = Set of Available Info Spaces Carol's Context in Room 525 Feb 18 2003 48 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 Feb 18 2003 49 Emergency Response Scenario Registering with the Building's InfoSpace Smart Dust Send location info User="Carol" Location="525 Soda Hall" Time="Apr 12 1:05PM" Carol's InfoSpace Building InfoSpace User="Carol" Location="5th floor" Age="37 seconds" User="Carol" Location="in" Age="37 seconds" 50 Access Control Logging Blurring Feb 18 2003 Emergency Response Scenario Querying during an Emergency Smart Dust Building InfoSpace User="Carol" Location="525 Soda Hall" Time="Apr 12 1:05PM" Carol's InfoSpace User="Carol" Location="525 Soda" Location="5th floor" Age="7 seconds" Age="37 seconds" Notification Feb 18 2003 Logging 51 Layers of InfoSpaces and Context Data View My Location to Strangers My Location to Friends My Location My Location to Family Logical Physical My Location on PDA My Location on PC 52 Feb 18 2003 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 Feb 18 2003 53 Talk Overview Motivation Research Overview Confab Architecture – Scalability Confab Architecture – Privacy Status Context + Whisper thoughts Context + SpeakEasy thoughts Feb 18 2003 54 Privacy Philosophy Fair Information Practices • • • • • • • Notice Choice Onward Transfer Access Security Data Integrity Enforcement Feb 18 2003 55 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) Feb 18 2003 56 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?) Feb 18 2003 57 A Privacy Design Space • • • • Legal Social Economic Technology RBAC Prevention Location Support Wearables Anonymization Pseudonymization Themes for Minimizing Asymmetry Goal: Provide reusable mechanisms that can User Interfaces for Feedback, Notification, and space populate this design Consent Privacy Mirrors Detection Avoidance P3P Collection Feb 18 2003 Access Data Lifecycle Second Use 58 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 Feb 18 2003 59 Two Privacy Mechanisms • Operators Garbage collection Blurring Access Control Logging Filters Remove or aggregate old data Increase ambiguity Check authorization Detection Remove certain data • Privacy Tags – Preferences for how personal data should be used – "Don't forward to anyone else" – "Don't fuse with other pieces of data" Feb 18 2003 60 Talk Overview Motivation Research Overview Confab Architecture – Scalability Confab Architecture – Privacy Status Context + Whisper thoughts Context + SpeakEasy thoughts Feb 18 2003 61 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 Feb 18 2003 62 The Ultimate Metric Feb 18 2003 63 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 Feb 18 2003 64 boundaries 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 • • • • Feb 18 2003 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? 65 Q&A Privacy is good here, but be careful not to fall into the systems tarpit. Focus, Jason, focus! Jen Mankoff Asst Prof Berkeley Feb 18 2003 Bill Schilit Intel Labs Seattle Co-director 66 Q&A Agree with Bill do I, beware the dark side of systems you must! Yoda Jedi Master Kickass Dude Feb 18 2003 67 Good work, Jason, Q&A I think you deserve a raise! This party's started! James Landay Assoc Prof Berkeley My Advisor Feb 18 2003 Mace Windu Jedi Master Also a Kickass Dude 68 Group for User Interface Research Thanks to: DARPA Expeditions PARC Intel Fellowship NSF ITR Yoda University of California Berkeley Context the circumstances in which an event occurs; a setting; to join; to weave Jason I. Hong http://guir.berkeley.edu/cfabric 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. Feb 18 2003 70 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. Feb 18 2003 71 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 Feb 18 2003 72 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 Feb 18 2003 73 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.com Schema="Device" Feb 18 2003 Attribute Relationship Entity 74 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 Feb 18 2003 75 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) Feb 18 2003 76 Operators • Problem: how to manipulate context data in a reusable manner? Data-type Conversion • Chainable Operators Ex. Celsius -> Farenheit Fusion Composition Garbage collection Blurring Access Control Logging Filters Feb 18 2003 Refine same data type Merge different data types Remove or aggregate old data Increase ambiguity Check authorization Detection Remove certain data 77 Context Queries • Problem: how to use context data? Feb 18 2003 78 Related Work • Context Toolkit • EventHeap • ParcTab infrastructure Feb 18 2003 79 Existing Examples of Using Context Existing Examples Auto Lights On / Off File Systems Calendar Reminders Smoke Alarm Barcode Scanners Feb 18 2003 Context Types Room Activity Personal Identity & Time Time Room Activity Object Identity Human Concern Convenience Finding Info Memory Safety Efficiency 80 Potential Examples of Using Context PotentialExamples Existing Examples Context Types Activity Identity Time Activity Location Human Concern Convenience Finding Info Memory Safety Efficiency 81 Auto Cell Phone Off In Meetings Tag Photos Proximal Reminders Health Alert Proximity Identity Activity Identity & Time History … Time Service Fleet Dispatching Feb 18 2003 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) Feb 18 2003 82 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? Feb 18 2003 83 Defining Context Situated Action • Situated action – Actions are fluid, moment-by-moment, improvised, often unplanned, and highly contextdependent – “[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? Feb 18 2003 84 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? Feb 18 2003 85 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 Feb 18 2003 – Let designers define context app-by-app – Provide generic reusable mechanisms (like DB) 86 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) Feb 18 2003 87 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? Feb 18 2003 88 Vision Feb 18 2003 Context-Aware Computing Today 89 Vision January Feb 18 2003 Context-Aware Computing in the Future90 Example Model Organizing January (Perso Feb 18 2003 91 Example Model Organizing End-User Devices, Services And Applications ??? Data Sources Sensors and Beacons (Personal, Group, Public) (Mobile and Infrastructure) Feb 18 2003 92 Example Model Organizing Sinks Context Data Model Layer Sources Feb 18 2003 93 Example Model Organizing Active Properties Confab Client HTTP Front-end InfoSpace Server Alice’s InfoSpac Feb 18 2003 e Bob’s InfoSpac e Room 22 InfoSpac e 94 Example Model Active Properties “scott.location” “scott.activity” “current-device.room” Feb 18 2003 95 Example Model Division of Responsibilities Analogous to web servers Manages a collection of InfoSpaces InfoSpace Server 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 InfoSpace Tuple Feb 18 2003 Analogous to individual web page Represents single piece of context data Contains privacy preferences and metadata Unit of storage 96 Example Model Evolution of Context-Aware Systems Feb 18 2003 97 Example Model A Predicted Evolution of Context-Aware Systems Train Info Board (Local Scope) (Context for people) Web (Global Scope) (Context for people) Web Service (Global Scope) (Context for computers) Feb 18 2003 (Global Scope) (Global Scope) (Context for computers) (Context for computers) (Network effects) (Network effects) Unified API Restricted Scope 98 Example Model A Predicted Evolution of Context-Aware Systems Manual Input Sensor Input Train Info Board Feb 18 2003 99 Example Model Physical, Logical, and View Sinks Data Stores Sources Context-Aware Applications Context Data Sensors, Beacons, Databases, Web pages Feb 18 2003 100 Example Model Intrinsic and Extrinsic Context Loc Feb 18 2003 101 Example Model Single InfoSpace Server HTTP Front-end In-Operators InfoSpaceAccess On-Operators Alice’s InfoSpace Tuple Tuple Tuple Room 525’s InfoSpace Tuple Tuple Out-Operators Berkeley CS InfoSpace Server Feb 18 2003 102 Example Model Context Data Model Health Alice’s InfoSpace Loc Activit y Person Room 525’s InfoSpace Person Feb 18 2003 Device PDA1138’s InfoSpace Owner 103 Example Model InfoSpaces Calorie Tracker Health Monitor Service Alice’s InfoSpace Personal Loc Triggers Auto Diary GPS Feb 18 2003 Motion Heartbeat 104 Example Model InfoSpaces Health Alice’s InfoSpace Loc Activit y Person Room 525’s InfoSpace Person Feb 18 2003 Device PDA1138’s InfoSpace Owner 105 Example Model Adding Data SUBSCRIBE Loc.* Loc. ActiveBadge Loc.Tri Active Badge Alice’s InfoSpace SUBSCRIBE Loc Wireless Triangulation Feb 18 2003 SUBSCRIBE Loc.* Alice’s Loc.Tri Laptop’s InfoSpace 106 Example Model Adding Data POST Loc. ActiveBadge Loc. ActiveBadge Loc.Tri Active Badge Alice’s InfoSpace POST Loc.Tri Wireless Triangulation Feb 18 2003 POST Loc.Tri Alice’s Loc.Tri Laptop’s InfoSpace 107 Example Model Transforming Data Alice’s InfoSpace Type=“Location” User=“Alice” Loc=“525 Wozniak Hall” Time=“Oct 06 1:05 PM” Time-to-live=“Forever” Building’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” Acquaintance’ Type=“Location” s InfoSpace User=“Alice” Feb 18 2003 Location=“Berkeley, CA” Time=“Oct 06 1:05 PM” Notify=“alice@me.com” Pref=“Do not forward” Time-to-Live=“1 week” 108 Hospital Room Number Temperature Doctor Room Patient Name Phone# Name Address Occupied Feb 18 2003 109 Example Model Physical, Logical, and View Hospital's InfoSpace Room Room Room Number Temperature Patients Doctor Room 525's InfoSpace Dr. X's InfoSpace Room Name Phone# Occupied Room 527's InfoSpace Feb 18 2003 Room Number Temperature Patient Y's Name InfoSpace Address Heart Rate Room 110 Occupied  Feb 18 2003 111 Context Sinks Example Model Personal InfoSpaces and Tuples Diary Power Monitor Context Data Model Name Dr. X's InfoSpace Phone# Room Activity Room 525's InfoSpace Room Number Temperature Occupied Context Sources 525 535 527 537 Dr. X Feb 18 2003 112 A B C D Feb 18 2003 113 Access Second Use A Alice's Location F Alice's Location C Feb 18 2003 114 A B Feb 18 2003 115