A Context Modeling Survey T. Strang, C. Linnhoff-Popien German Aerospace Center (DLR), Ludwig-Maximilians-University Munich (LMU) Workshop on Advanced Context Modeling, Reasoning and Management, UbiComp, Presentation by KwangHyun Nam, IDS Lab.

Copyright  2008 by CEBT Contents  Introduction  Fundamentals  Modeling Approaches Key-Value Models Markup Scheme Models Graphical Models Object Oriented Models Logic Based Models Ontology Based Models  Evaluation  Summary, conclusion and outlook 2

Copyright  2008 by CEBT Introduction  Past research Published with respect to location, identity, time  Current research To develop uniform context model, representation and query languages as well as reasoning algorithms – To facilitate context sharing and interoperability of applications  Aim of this paper Survey of the most relevant current approaches to modeling context for ubiquitous computing 3

Copyright  2008 by CEBT Fundamentals  Evolution Chain Context dependency is a major issue in recent work in the area of ubiquitous computing systems Ubiquitous computing is a specialization of distributed computing and mobile computing 4

Copyright  2008 by CEBT Requirement for ubiquitous computing  Distributed composition (dc) UbiComp is a derivative of a distributed computing system Lacks of a central instance being responsible for the creation, deployment and maintenance of data and services, in particular context descriptions Composition and administration of model varies with high dynamics in terms of time, network, topology and source  Partial validation (pv) Desirable to enable to partially validate contextual knowledge on structure & instance level This is particularly important – Due to the complexity of contextual interrelationships, which make any modeling intention error-prone 5

Copyright  2008 by CEBT Requirement for ubiquitous computing  Richness and quality of information (qua) The quality of a information and the richness of that may differ Model should support quality and richness indication  Incompleteness and ambiguity (inc) The set of contextual information at any point in time is usually incomplete and/or ambiguous This should be covered by the model – Example By interpolation of incomplete data on the instance level 6

Copyright  2008 by CEBT Requirement for ubiquitous computing  Level of formality (for) A challenge to describe contextual facts & interrelationships in a precise and traceable manner “Print document on printer near to me” – What ‘near’ means to ‘me’? -> need a precise definition of terms Each participating party in an ubiquitous computing interaction shares the same interpretation of the data exchanged – Shared understanding  Applicability to existing environments (app) A context model must be applicable within existing the infrastructure of ubiquitous computing environment Example – A service framework 7

Copyright  2008 by CEBT Modeling approaches  Key-Value Models Most simple data structure of models Frequently used in distributed service frameworks Described with a list of simple attributes in a key-value manner Easy to manage Not very efficient for more sophisticated structuring 8 Environment Variables: Key-Value Models

Copyright  2008 by CEBT Modeling approaches (cont’d)  Markup Scheme Models Hierarchical data structure consisting of markup tags Typical representatives: profiles – Based upon a serialization of a derivative of SGML Examples – Defined as extension to Composite Capabilities/Preference Profile (CC/PP) User Agent Profile (UAProf) – Comprehensive Structured Context Profiles (CSCP) Unlike CC/PP, not define any fixed hierarchy – Pervasive Profile Description Language (PPDL) Allow to account for contextual information and dependencies when defining interaction pqtterns on a limited scale – Centaurus Capability Markup Language (CCML) 9

Copyright  2008 by CEBT Modeling approaches (cont’d)  Graphical Models Particularly useful for structuring, but usually not used on instance level Examples – Well known: UML A strong graphical component (UML diagram) Due to its generic structure, UML is appropriate to model the context – Contextual Extended ORM Basic modeling concept in ORM is the fact The modeling of a domain involves indentifying proper fact types & roles Extended ORM is allowed to categorize fact types either as static or as dynamic 10

Copyright  2008 by CEBT Modeling approaches (cont’d)  Object Oriented Models Intention behind object orientation is (as always) encapsulation and reusability Examples – Representative: Cues (TEA project) Provide an abstraction from physical and logical sensors Regarded as a function  Taking the value of a single physical/logical sensor up to a certain time as input  Providing a symbolic/sub-symbolic output – Active Object Model (GUIDE project) All the details of data collection and fusing are encapsulated within the active objects  Hidden to other components of the system. 11

Copyright  2008 by CEBT Modeling approaches (cont’d)  Logic Based Models Logic defines conditions on which a concluding expression or fact may be derived from a set of other expressions or facts (reasoning) Context is defined as facts, expressions and rules High degree of formality Examples – McCarthy’s Formalizing Context To give a formalization recipe which allows for simple axioms for common sense phenomena – Akman & Surav’s Extended Situation Theory Extend the Situation Theory by Barwise & Perry To model the context with situation which are ordinary situations 12

Copyright  2008 by CEBT Modeling approaches (cont’d)  Ontology Based Models Ontology is used as explicit specification of a shared conceptualization Strong in the field of normalization and formality Context is modeled as concepts and facts Examples – ASC model of Context Ontology Language (CoOL) Used to support context-awareness in distributed service frameworks for various applications – CONON ontology An upper ontology which captures general features of basic contextual entities and a collection of domain specific ontologies and features. – CoBrA system Provide a set of ontological concepts to characterize entities 13

Copyright  2008 by CEBT Evaluation  Key-Value Models Weak on the requirements 1 to 5 (-) The simplicity of key-value pair is a drawback if quality meta- information or ambiguity shall be considered (-) Solely the applicability is a strength (+)  Markup Scheme Models Strong concerning the partial validation requirement (++) Standard CC/PP & UAProf have only restricted overriding and merging mechanisms (-) Applicability to existing markup-centric infrastructures is a strength (++) 14

Copyright  2008 by CEBT Evaluation (cont’d)  Graphical Models The strengths are definitely on the structure level – Mainly used to describe the structure of contextual knowledge and drive code or an ER-model from model Distributed composition requirement has some constraints on the structure level (-)  Object Oriented Models Strong regarding the distributed composition requirement (++) A higher level of formality is reached through the use of well-defined interfaces (+) – The invisibility as consequence of encapsulation is a little drawback 15

Copyright  2008 by CEBT Evaluation (cont’d)  Logic Based Models Be composed distributed (++) Formality is extremely high (++) However, this model is weak with respect to other requirements(-)  Ontology Based Models Strong in the distributed composition requirement (++) Inherit the strengths in the field of normalization and formality from ontologies (++) All requirements for UbiComp enable to be covered by this model. 16

Copyright  2008 by CEBT Summary, Conclusion and Outlook 17 Miss Ubiquitous Contest I’m so nervousMe, too. Winner : Ontology Thank you. I give all these glory to you!! But, all others are also valuable.

Copyright  2008 by CEBT Summary, Conclusion and Outlook  The most promising assets for context modeling for ubiquitous computing environments Ontology category  But, the other approaches aren’t unsuitable for UbiComp  This list of context modeling approaches is comprehensive, but - as in all surveys - incomplete 18

Copyright  2008 by CEBT Discussion  Pros Indicate definite criterion for comparison of models for ubiquitous computing May help to identify appropriate approach for ubiquitous computing applications  Cons Lack reasons of analysis decision with respect to criterion of some items Typographical error – Specialisation -> Specialization ( 1 Page, right-side 6 th line) – Categorised -> Categorized (3 Page, in content of Graphical Models) 19