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A Context Broker for Building Smart Meeting Rooms

A Context Broker for Building Smart Meeting Rooms. Harry Chen, Tim Finin, Anupam Joshi Univ. of Maryland, Baltimore County AAAI Spring Symposium 2004. Outline. Introduction Issues in building context-aware systems How Semantic Web languages can help Background

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A Context Broker for Building Smart Meeting Rooms

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  1. A Context Broker for Building Smart Meeting Rooms Harry Chen, Tim Finin, Anupam Joshi Univ. of Maryland, Baltimore County AAAI Spring Symposium 2004

  2. Outline • Introduction • Issues in building context-aware systems • How Semantic Web languages can help • Background • The Semantic Web vision and ontologies • Context Broker Architecture (CoBrA) • Approach, design, and prototypes • Ongoing work & concluding remarks

  3. Computing Evolution …

  4. Pervasive Computing Thank God! PerCom is here…

  5. Configuration? Too much work… Sync. Download. Done. Intelligence is the Key

  6. Context-Aware Systems • Context-awareness is a key aspect of the intelligent pervasive computing systems • Systems that can anticipate users’ needs and act in advance by “understanding” their context • A system that knows I am the speaker • A system that knows you are the audiences • A system that knows we are in a conference • …

  7. What’s Context? • The situational conditions that are associated with a user • Location, room temperature, lighting conditions, noise level, social activities, user intentions, user beliefs, user roles, personal information, etc.

  8. Related Work • Since the early 90’s, people have been interested in building context-aware systems • Olivetti: Call forwarding & teleporting systems … • Xerox PARC: Active map, PARC Tab … • Georgia Tech.: Context toolkit, cyberguide … • MIT: Office assistant, location-aware information delivery, intelligent room … • UC Berkley: Context Fabric • UIUC: Gaia • HP Labs: Cooltown, CoolAgents … • …

  9. The Shortcomings of the Previous Systems • Lacking an adequate representation for context modeling and reasoning • Individual agents are responsible for managing their own context knowledge • Users often have no control over the information that is acquired by the sensors

  10. Research Issues • Context Modeling & Reasoning • How to represent context, so that it can be processed and reasoned by the computers • Knowledge Maintenance & Sharing • How to maintain consistent context knowledge and share that information with other systems • User Privacy Protection • How to let users to control the sharing and the use of their contextual information that is acquired by the hidden sensors

  11. Our Research Contributions • CoBrA: a broker-centric agent architecture for supporting pervasive context-aware systems • Using SW languages to define ontologies for context modeling and reasoning • Using logic inference to interpret context and to detect and resolve inconsistent knowledge • Allowing users to defined policies to control the use of their contextual information

  12. Other Contributions • EasyMeeting: a smart meeting room prototype that exploits CoBrA • Providing relevant services and information to meeting participants based on their situational needs • Allowing users to control the use and the sharing their location and social context

  13. Semantic Web & Ontologies

  14. About the Semantic Web • An extension to the present World Wide Web. • The focus is on enabling computing machines to be able to reason about web information in addition to display web information. • NOTE: displaying information does not necessarily require “deep” understanding of the information. • NOTE: in order to reason about information often requires “deep” understanding of the information.

  15. The Current Web (adopted from Eric Miller’s presentation http://www.w3.org/2004/Talks/0120-semweb-umich/) • Resource: • Identified by URI’s • Untyped • Links: • “href”, “src” … • non-descriptive • Users: • Exciting world - semanticsof resource, however, gleanedfrom content • Machine: • Very little information available - significance of the links only evident fromthe context around the anchor

  16. The Semantic Web (adopted from Eric Miller’s presentation http://www.w3.org/2004/Talks/0120-semweb-umich/) • Resource: • Globally identified by URI’sor locally scoped (blank) • Extensible • Relational • Links: • Identified by URI’s • Extensible • Relational • Users: • Even more exciting world, richeruser experience • Machine: • More processable informationis available (Data Web)

  17. The Semantic Web Layer Cake “The Semantic Web will globalize KR, just as the WWW globalize hypertext”-- Tim Berners-Lee we arehere

  18. Semantic Web Ontologies • Formally, an ontology is an explicit specification of a conceptualization. • For the developers, building ontologies is about defining shared vocabularies and associated semantic relations • SonyEricsson T68i is a type of cellphone • All SonyEricsson T68i supports Bluetooth • Harry has a SonyEricsson T68i device • => Harry’s cellphone supports Bluetooth.

  19. Semantic Web Languages http://www.w3.org/2001/sw/ • KR languages for defining ontologies • W3C Recommendations • RDF/RDFS -- represents information as N-Triples (subject, predicate, object); supports basic class-subclass & properties. • OWL (Web Ontology Language) -- adds more vocab. for describing classes and properties, cardinality, equality, XML datatypes, enumerations etc.

  20. Pervasive computing is great! OWL? Ontologies? But where?

  21. How does OWL Help? ontology language service description lang. context model { PerCom } meta lang (policy) Interop language XSLT/XML friendly OWL provided a uniformed language which met many needs in developing a complex pervasive computing system.

  22. Context Broker Architecture(CoBrA)

  23. Context Broker Architecture Pervasive Computing Semantic Web CoBrA Software Agents CoBrAnot CORBA!

  24. A Bird’s Eye View of CoBrA

  25. Key Features of CoBrA • Using OWL to define ontologies for context modeling and reasoning • COBRA-ONT, SOUPA -- Standard Ontology for Ubiquitous & Pervasive Applications • Taking a rule based approach to interpret and reason about context • Jena + Jess, Theorist (assumption-based reasoning) • Using a policy language and engine to control the sharing of user context • Rei -- a policy language that exploits denotic concepts & speech acts (UMBC)

  26. The broker detects Alice’s presence Alice “beams” her policy to the broker Alice enters a conference room » B B » » Policy says, “inform my personal agent of my location” The broker builds the context model Policy says, “can share with any agents in the room” B A .. isLocatedIn .. Web B A An EasyMeeting Scenario

  27. The broker tells her location to her agent The broker informs the subscribed agents The projector agent asks slide show info. A B B The projector agent wants to help Alice Her agent informs the broker of her role and intentions The projector agent sets up the slides + An EasyMeeting Scenario

  28. Research Work in CoBrA Context Reasoner PrivacyProtection CoBrA Ontologies EasyMeeting Prototype

  29. The CoBrA Ontology (v0.4) http://daml.umbc.edu/ontologies/cobra/0.4/

  30. Example 1: Location Inference • Goal: reason about a person’s location using the available sensing information. => Step 1: define a domain spatial ontology

  31. A Simple UMBC Ontology

  32. Location Inference Assume the broker is told that Harry is located in RM-201A

  33. Location Inference A: the used spatial relations are “rdfs:subProeprtyOf” the “inRegion” property B: “inRegion” is of type “Transitive Property” Based on A & B => …

  34. Location Inference

  35. Example 2: Spotting a Sensor Error Premise (static knowledge): R210 rdf:type AtomicPlace. ParkingLot-B rdf:type AtomicPlace. Premise (dynamic knowledge): Harry isLocatedIn R210. Harry isLocatedIn ParkingLot-B. Premise (domain knowledge): No person can be located in two different AtomicPlace at the same time. Conclusion: There is an error in the knowledge base.

  36. Context Reasoner Jena OWL/RDFS Reasoner Sensing Information KB (MySQL) Context Knowledge JESS Rule Engine Context Broker

  37. BT Sensor JADE BrokerJADE EasyMeeting Prototype #1 Room ECS201 MySQL CWM Tomcat Server N-Triple + Jena + RDQL N-Triple + Jena + RDQL Context information (FIPA + OWL-XML) HTTP Server Harry’s Policy The URL of Harry’s Policy (FIPA+N3)

  38. EasyMeeting Prototype #2

  39. Work in Progress

  40. Things that I’m working on… • Enhancing the broker’s reasoning • Implementing a policy-based privacy protection mechanism • Building an Eclipse Plug-in for monitoring the “brain” of the broker • Working with other researchers to define a shared ontology for supporting PerCom applications.

  41. Enhancing the Reasoner; Adding Privacy Protection • Using an assumption-based reasoner (called Theorist) to support default and abductive reasoning • Tries to explain the observed sensing information by making hypotheses (abduction), and then predicts users’ future actions (defaults) • Using the Rei policy language & engine to support privacy protection

  42. Privacy Policy Use Case (1) • The speaker doesn’t want others to know the specific room that he is in, but does want others to know that he is present on the school campus • He defines the following policies: • Can share my location with a granularity > ~1 km radius • The broker: • isLocated(US) => Yes! • isLocated(Maryland) => Yes! • isLocated(BaltimoreCounty) => Yes! • isLocated(UMBC) => Yes! • isLocated(ITE-RM-201A) => I don’t know…

  43. Privacy Policy Use Case (2) • The problem of inference! • Knowing your phone + white pages => I know where you live • Knowing your email address (.mil, .gov) => I know you works for the government • The broker models the inference capability of other agents • mayKnow(X, homeAdd(Y)) :- know(X,phoneNum(Y))

  44. CoBrA Eclipse Viewer (CEV) Inspired by the Java Spider application http://www.javaspider.org For exploring the knowledge and user policies that are stored in the Context Broker; for monitoring the broker’s reasoning process.

  45. Building a Standard Ontology for Supporting PerCom Apps. • Standard Ontology for Ubiquitous and Pervasive Application (SOUPA) • Semantic Web in UbiComp SIG • http://pervasive.semanticweb.org/ • The bigger goal of SW-UbiComp SIG • Bring together SW+PerCom researchers • Exploring the use of ontologies in PerCom

  46. Conclusions

  47. Semantic Web for PerCom • Semantic Web languages & ontologies can facilitate knowledge sharing, context reasoning, and user privacy protection in a PerCom environment • CoBrA is a new pervasive context-aware architecture that exploits the Semantic Web technologies

  48. Questions? • CoBrA (ontologies, CEV, source code) • http://cobra.umbc.edu • SW-UbiComp SIG • http://pervasive.semanticweb.org/ • PerCom news & development • http://www.ebiquity.org/ • Harry Chen • Google “Harry Chen”

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