semantic web in context broker architecture presented by harry chen, tim finin, anupan joshi at...

Semantic Web inSemantic Web inContext Broker ArchitectureContext Broker Architecture

Presented by Harry Chen, Tim Finin, Anupan JoshiAt PerCom ‘04

Summarized by Sungchan Park2008-11-17

Copyright 2008 by CEBT

Problems in Context-Aware Problems in Context-Aware ComputingComputing Limited Resources in Mobile Devices

Battery Power Constraints Information Storage Constraint Computing Power Constraint Communication Constraint

– Agents may be lack of sufficient knowledge to communicate with context sources in the environment e.g. knowing which sensor can provide what information and how to

communicate– Context sources may dynamically join and leave the

environment without notifying the agents

IDS Lab. Seminar - 2Center for E-Business Technology


Problems in Context-Aware Computing Problems in Context-Aware Computing (cont’d)(cont’d) Privacy Issues in Accessing User Information

People are worried about how computer systems use and share their personal information

Agents often collect and share user information Agents also share user information

Lack of Reusable Context-Aware Mechanisms Many of existing context-aware systems are difficult and costly to

build Because existing systems are tightly coupled with agent

implementations, modifying the system often requires modifying the implementation of the agents.– Growing the number of agents could cause difficulties



BackgroundBackground Aspects of Context-Aware Computing

Enhancing User Interfaces Guiding the Adaptation of System Behavior Enabling Smart Space Applications

How Do Applications Acquire Contexts? Contexts are acquired by directly accessing low-level

context sensors Contexts are acquired from some kind of middle-ware

infrastructures that in turn interact with low-level context sensors

Contexts are acquired from servers that maintain situational knowledge about the environment



Proposed Solution: Context Broker Proposed Solution: Context Broker ArchitectureArchitecture The author proposes to develop a broker-centric agent architecture (i.e.

COBRA) to provide runtime supports for context-aware systems in an Intelligent Meeting Rom environment

The core of the COBRA architecture: Domain Context Broker is responsible for Maintaining the context model of the domain, which includes domain

contexts from the past and at the present Resolving inconsistencies and ambiguities of the domain contexts through

information fusion Establishing privacy policies with users before sharing their personal

information Providing knowledge sharing service for context-aware agents through

agent communications



Context-Aware Computing Meets Semantic Context-Aware Computing Meets Semantic WebWeb Semantic Web & Context-Aware System

Semantic Web standards will enhance the communication and information sharing

Semantic Web will become a major source of information for determining the context of users and thie associated daily activities– E.g. When a person enters a room, his location information (his

presence in the room) is automatically updated in his personal web site. Immediately, context-aware agents in both remote and local environment can acquire the presence information about the person through the Web without needing any sensing or reasoning.



COBRA ArchitectureCOBRA Architecture Objectives

Acquiring context from heterogeneous sources– From sensors, Web, user

profiles, behavior pattern Maintaining consistent

contextual knowledge Enabling knowledge sharing

among agents Protecting the privacy of

users Domain Context Broker

Context sharing Protecting privacy



The Design of Domain Context BrokerThe Design of Domain Context Broker



Knowledge BaseKnowledge Base Domain Ontology

Context Model : fundamental concepts for representing contexts Information Source Model : sensors, user devices, web services, … Agent Profile Model : types or services that an agent offers, the

reason for acquiring certain contexts etc. Information Privacy Model

Domain Heuristics Help the broker to resolve knowledge inconsistency caused by

imperfect sensing Help the broker to build and maintain a shared model of context

User Privacy Policies What information can or can’t be shared, etc



Inference EngineInference Engine Ontology Reasoning Module

deduce facts that can be concluded from the knowledge in the Knowledge Base in conjunction with the models that are defined in the Domain Ontology

Context Reasoning Module Reasoning about situational conditions of an entities

Knowledge Maintenance Module Check logical implication

– “If A is true, then B must be true”– “B is not true”– “A is true”– “B is true”– Inconsistent!

Apply domain heuristics– “No one person can be in two different rooms at the same time”– “Bob is currently attending a meeting in RM 201”– “Bob is currently typing a paper on his computer in RM 102”– Inconsistent!



Broker BehaviorBroker Behavior Privacy Policy Negotiation



Use Case: An EasyMeeting ScenarioUse Case: An EasyMeeting Scenario

Alice enters a conference room

The broker detects Alice’s presence

B

Policy says, “can share with any agents in the room”

A

B

The broker buildsthe context model

Web

Alice “beams” her policy to the broker

B

Policy says, “inform my personal agent of my location”

AB .. isLocatedIn ..


Use Case: An EasyMeeting Scenario Use Case: An EasyMeeting Scenario (cont’d)(cont’d)

Her agent informs the broker of her

role and intentions+

The broker tells herlocation to her agent

A

The projector agent wants to help Alice

The projector agentasks slide show info.

B

The projector agent sets up the slides

The broker informsthe subscribed agents

B


A Bird’s Eye View of CoBrAA Bird’s Eye View of CoBrA

semantic web in context broker architecture presented by harry chen, tim finin, anupan joshi at...

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