1 Context Tailor: Towards a Programming Model for Context Aware Computing John S. Davis, Daby M. Sow, Marion Blount and Maria Ebling IBM T.J. Watson Research Center Hawthorne, NY 10532

2 Context Aware Computing The process of using pervasive context to assist a computing service on behalf of a particular user Focus on Customized Context Aware (CCA) Applications Applications that customize their execution to the expected needs of users

3 More on CCA Applications Application examples: Weiser’s waking state coffee machine Smart HVAC system Context Sensitive Scheduler Context Aware Content Distribution (CACD) System The Customization Challenge Need effective mechanism to incorporate user preferences

4 Naïve Approach to Customization Explicit customization Requires expert rules Problems: Domain specific expertise Error prone Expensive

5 Context Tailor Approach Automatic generation of context patterns Middleware to learn context patterns Application Model API enabling application developers to use the learning capabilities of the middleware

6 More on the Approach Model: Decouple CCA applications Triggering (the brain) The process that uses context to initiate an application’s execution Effecting (the muscles) The process that uses context to impact an application during run-time Framework Move the triggering process into the middleware and out of the developer’s view Allow the developer to focus on the effecting process

7 Decoupling Example Web Logs Miner Access Patterns Decision Module User Policies CACD Directives Apache Proxy Prefetching Module Directive Server TriggeringEffecting Other Context

8 Existing Context Service Dispatcher Location Context Driver Desktop Context Driver Context Driver Interface Context Service Instant Messaging Context Driver Internal Utilities CS API Notes Calendar Context Driver Context Push Interface Desktop client SameTime

9 Context Tailor Architecture Context Service Source 2Source 3Source m Learning Engine Pattern Activator Context Patterns Context Logs Service 1 Service 2Service n Arbitrator Source 1 Framework Layer Application Layer CS APIContextTailor API (trigger, feedback) CS API Pattern: Condition  Trigger

10 Research Challenges Effecting A Programming Model for Deep Context-Aware Computing Applications Driven by the need for a machine learning-independent interface that is reusable across applications Triggering Machine Learning (ML) applied to a Pervasive Computing Environment Driven by characteristics of a pervasive computing environment Security and Privacy

11 Relating Applications No single Machine Learning (ML) algorithm is useful for all applications Our framework must map each application to an appropriate ML algorithm The triggering specification interface must support this without requiring ML expertise

12 Application Classification Different ML techniques apply to different portions of the classification space The API will enable the developer to determine where in the classification an application falls Z InstantaneousDuration Recurrent Events Rare Events Timed Events Ordered Events Grouped Events Applications of initial interest to the Context Tailor Project are shown in the grey subspace. Y X

13 Research Challenges Effecting A Programming Model for Deep Context-Aware Computing Applications Driven by the need for a machine learning-independent interface that is reusable across applications Triggering Machine Learning (ML) applied to a Pervasive Computing Environment Driven by characteristics of a pervasive computing environment Security and Privacy

14 Impact of PvC on ML Pervasive Computing Distributed infrastructure Highly dynamic data How/when is context accessed Heterogeneous data formats Varied communications protocols Temporarily disconnected networks Specification of context compositions Privacy & security issues Varying reliability Broad user characteristics Impact on ML Incremental Learning Universality Need for Semantic Closeness Handling of Out of Order Events

15 Future Work: Security and Privacy Management of privacy policies Need to generate privacy policies on generated rules Need to keep the user in the loop in a non intrusive manner Administration across domains Context may be owned by several distinct organization Context Tailor can help publishing summaries of user context without exposing detailed user context

16 Back-up Slides…

17 The Context Tailor Slogan To shield application developers from the complexity of customization. “ Write Once, Run For Everyone ”

18 Existing Context Service Dispatcher Location Context Driver Desktop Context Driver Context Driver Interface Context Service Instant Messaging Context Driver Event Engine Privacy Engine Connection Mgr Context Cache InternalUtilities CS API Notes PBX Calendar Context Driver Context Push Interface SameTime RIM Work Pacer PBX Context Driver RIM Context Driver Desktop client

19 Challenges of Pervasive Computing (PvC) Environments Distributed Infrastructure Highly dynamic data How/when is context accessed Heterogeneous data formats Varied communications protocols Temporarily disconnected networks Specification of context compositions Privacy & Security Issues Varying reliability Abundance of context data available to application developers Need ways to extract meaningful information from the noise Meaningful information is customized

20 Information Flow Context Patterns Context Service Vocabulary Pattern Activator Arbitrator Service n Service m 1. New Context 2. Pattern Query 3. Patterns Registered Services 4. Service query 5. Services 6. Triggers 7. Triggers8. Triggers

21 Related Fields Computability Theory (Turing, 1936) Algorithmic Complexity Theory (1960’s) Kolmogorov (randomness) Chaitin (incompleteness) Solomonoff (prediction) LZ algorithm (Lempel & Ziv, 1976) Probability Theory Pascal, Fermat,1654 Kolmogorov, 1933 Information Theory (Shannon, 1948) Statistical Signal Processing 1960’s

22 Web User Learnability Distribution log n U c log c + c L(U) = 1 - c = number of nodes in tree n u = number of url’s in system number of users = 623 mean = 0.74 variance =

23 Context Source Failure Problem: In pervasive environments context sources will go down Initial approach: Ontology and closeness of data sources If source A fails, use source B for learning/prediction because d(A,B) is small according to ontology

24 Out-of-Order Events Problem: Receiving delayed events and the effect on on-line learning techniques Initial Approach Dynamic batching schemes Storing the N-last patterns generated Adjusting N dynamically Generic event insertion