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This paper aims at demonstrating that the Model-Driven Architecture (MDA) approach to transformation is suitable to cope with the abstraction gap between the specification of application rules for context-aware applications and the implementation of these rules using rule engines. This paper reports on the transformation of Event-Condition-Action (ECA) rules onto rules that can be executed by the Jess rules engine. We have applied the MDA approach to define this transformation, by specifying it in terms of mappings between elements of the ECA Domain-specific Language (ECA-DL), which is the language we developed to represent ECA rules, and the Jess metamodel. The transformation was fully specified, implemented using the Atlas Transformation Language (ATL) and tested with a simple example.

Context awareness has emerged as an important element in distributed computing. It offers mechanisms allowing applications to be aware of their environment and enabling them to adjust their behavior to the current context. In order to keep track of the relevant context information, a flexible service mechanism should be available for the client applications. In this paper we present a service architecture to provide context- awareness capabilities to users and client applications. Moreover, the service is able to react depending on the user's preferences and context. The conditions for the reaction and the reaction itself are defined in rules the users submit to the service by means of a convenient rule language.

Context-aware applications use and manipulate context information to detect high-level situations, which are used to adapt application behavior. This paper discusses the specification of situations in context-aware applications and introduces a rule-based approach to detect situations. Situations are specified using a combination of UML class diagrams and OCL constraints. We support a wide range of situations, which can be composed of more elementary kinds of context. We discuss how to cope with distribution and to exploit it beneficially for context manipulation and situation detection. We employ a generic rule-based platform (DJess [2]) to support the derivation of situations in a distributed fashion.

Service discovery is a process of locating, or discovering, one or more documents, that describe a particular service. Most of the current service discovery approaches perform syntactic matching, that is, they retrieve services descriptions that contain particular keywords from the user’s query. This often leads to poor discovery results, because the keywords in the query can be semantically similar but syntactically different, or syntactically similar but semantically different from the terms in a service description. Another drawback of the existing service discovery mechanisms is that the query-service matching score is calculated taking into account only the keywords from the user’s query and the terms in the service descriptions. Thus, regardless of the context of the service user and the context of the services providers, the same list of results is returned in response to a particular query. This paper presents a novel approach for service discovery that uses ontologies to capture the semantics of the user’s query, of the services and of the contextual information that is considered relevant in the matching process.