Contributions
- Not only system designers but also end users to specify rules on how to compose context-aware applications
- It supports both rule-based and learning-based context-aware service composition
- It utilizes semantic similarities among components to improve its adaptability in a dynamic environment
- supports seamless service migration which autonomously composes a new application and migrates onto it when user context changes
Componetn Service Model with Semantics (CoSMoS)
There is a UML meta model for CoSMoS wichi helps with Defining
- Functional Informaiton
- Semantic Information
- Contextual Information
- User specific rules
Component Runtime Environment
CoRE consists of the following pieces
- Dscovery Manager
- Execution Manager
- User Manager
- metadata of the components: context information is embedded in the components metadata
- context-aware discovery or user submodule: acquiring context information through existing context aware technologies
- inference: infer context based on a set of facts
two approaches are used
- Rule-based
- Learning-based
The learning algorithm for selecting a component
Pi = max (SSi,j x (CMDj + const)) 1 <= j <= n SS i,j = semantic similarity between two components CMDj = context matching degree. (how well the component matches the context it is used in based on the previous experiments in using this component in this context). To decide about context matching conditioins a C4.5 DT algorithm is used collecting information about a composed workflow. context aware dynamic service composition systems have problems for the following reasons:
- predefined rules usually cannot be modified once they are deployed
- it is difficult to define a generic rule that is applicable to every user
- some users preferences may be too complex to define as a set of rules
