Zakwan Jaroucheh

Recognize contextual situation in pervasive environments using process mining techniques

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.

Apto: A MDD-Based Generic Framework for Context-Aware Deeply Adaptive Service-Based Processes

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.

An approach to domain-based scalable context management architecture in pervasive environments

In pervasive environments, context management systems are expected to administrate large volume of contextual information that is captured from spatial to nonspatial elements. Research in context-aware computing produced a number of middleware systems for context management to intermediate the communications between applications and context providers. In particular, in pervasive environments, the design of distributed storage, retrieval and propagation mechanisms of context information across domains is vital. In this paper, we propose a domain-based approach to address the requirements of scalable distributed context management, cross-domain efficient context information dissemination and domain-based privacy policy enforcement. We propose infinitum, a middleware architecture that incorporates the management and communication benefits of the Google Wave Federation Protocol, while also taking advantage of the semantic and inference benefits of ontology-based context models. This architecture establishes a robust cross-domain scalable context management and collaboration framework, which has been implemented and evaluated in a real-life application of “SMART University” to support virtual team collaboration.

CANDEL: Product Line Based Dynamic Context Management for Pervasive Applications

In a 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 domain-agnostic context model, that captures various types of context information and the dependencies 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 a 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 context-aware adaptive pervasive applications.

A Perspective on Middleware-Oriented Context-Aware Pervasive Systems

The evolving concepts of mobile computing, context-awareness, and ambient intelligence are increasingly influencing users 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.

Mapping Features to Context Information: Supporting Context Variability for Context-Aware Pervasive Applications

Context-aware computing is widely accepted as a promising paradigm to enable seamless computing. Several middlewares and ontology-based models for describing context information have been developed in order to support context-aware applications. However, the context variability, which refers to the possibility to infer or interpret different context information from different perspectives, has been neglected in the existing context modeling approaches. This paper presents an approach for context-aware software development based on a flexible product line based context model which significantly enhances reusability of context information by providing context variability constructs to satisfy different application needs.

Jabber-based Cross-Domain Efficient and Privacy- Ensuring Context Management Framework

in pervasive environments, context-aware applications require a global knowledge of the context information distributed in different spatial domains in order to establish context-based interactions. Therefore, the design of distributed storage, retrieval, and dissemination mechanisms of context information across domains becomes vital. In such environments, we envision the necessity of collaboration between different context servers distributed in different domains; thus, the need for generic APIs and protocol allowing context information exchange between different entities: context servers, context providers, and context consumers. As a solution this paper proposes ubique, a distributed middleware for context-aware computing that allows applications to maintain domain-based context interests to access context information about users, places, events, and things - all made available by or brokered through the home domain server. This paper proposes also a new cross-domain protocol for context management which ensures the privacy and the efficiency of context information dissemination. It has been robustly built upon the Jabber protocol which is a widely adopted open protocol for instant messaging and is designed for near real-time communication. Simulation and experimentation results show that ubique framework well supports robust cross-domain context management and collaboration.