Research in pervasive computing and ambience intelligence aims to enable users to interact with the environment in a context-aware way. To achieve this, a complex set of features describing different aspects of the environment has to be captured and processed; in other words situation-awareness is needed. This article notes uniquely three points when modelling situations. Firstly, unlike most existing approaches, context information history should be considered when modelling the situations. We argue here that the current state cannot be understood in isolation from the previous states. Secondly, in order to track user's behaviour there is a need to consider the context information available in the different domains the user visits. Thirdly, to identify situations it can be problematic to define situation patterns and looking for an exact match as most of the approaches does. We found that the combination of the flexibility of the user behaviour and automated capture of context events provide a very effective solution for contextual situation recognition. In this article we first provide a formalization of the situation recognition problem and then we focus on the potential use of process mining techniques for measuring situation alignment, i.e., comparing the real situations of users with the expected situations. To this end, we propose two ways to create and/or maintain the fit between them: linear temporal logic (LTL) analysis and conformance testing.We evaluate the effectiveness of the framework using a third party published smart home dataset. Our experiments prove the effectiveness of applying the proposed approach to recognizing situations in the flow of context information.

Abstract-Context-awareness and adaptability are important and desirable properties of service-based processes designed to provide personalized services. Most of the existing approaches focus on the adaptation at the process instance level [1] which involves extending the standard Business Process Execution Language (BPEL) and its engine or creating their own process languages (e.g. [2]). However, the approach proposed here aims to apply an adaptation to processes modeled or developed without any adaptation possibility in mind and independently of specific usage contexts. In addition, most of the existing approaches tackle the adaptation on the process instance or definition levels by explicitly specifying some form of variation points. This, however, leads to a contradiction between how the architect logically views and interprets differences in the process family and the actual modeling constructs through which the logical differences must be expressed. We introduce the notion of an evolution fragment and evolution primitive to capture the variability in a more logical and independent way. Finally, the proposed approach intends to support the viewpoint of context-aware adaptation as a crosscutting concern with respect to the core "business logic" of the process. In this way, the design of the process core can be decoupled from the design of the adaptation logic. To this end, we leverage ideas from the domain of model-driven development (MDD) and generative programming.

Abstract-In pervasive environment, it is essential for computing applications to be context-aware. However, one of the major challenges is the establishment of a generic and dynamic context model. Many different approaches to modeling the context exist, but an application-and domainagnostic context model, that captures various types of context information and dependency between them, that could be reused and shared by different applications, and that can be dynamically changed when a shift in focus occurs, is missing. Therefore, we are interested in defining a structure for the dynamic management of context information. This paper describes our notion of context and proposes distributed context management architecture that supports the development of context-aware applications. It presents CANDEL, a generic context information representation framework that considers the context as a dynamic product line composed of context primitives (CPs). Frame based software product line techniques are used together with OWL ontology to define CPs and to dynamically generate the current context model. Further, using Petri-Nets, we also show how this framework will be used to support the context-aware adaptive pervasive applications.Keywords-ontology-based context model; pervasive applications; software product line; feature model.

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The evolving concepts of mobile computing, context-awareness, and ambient intelligence are increasingly influencing user's experience of services. Therefore, the goal of this paper is to provide an overview of recent developments and implementations of middleware-based pervasive systems, and to explore major challenges of implementing such systems. This paper also provides a comprehensive access to the literature of the emerging approaches and design strategies of middleware for providing users with personalized services taking into consideration their preferences and the overall operating context. Middleware systems were categorized according to their internal coordination model.