Recognize contextual situation in pervasive environments using process mining techniques

Abstract

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.

Factors influencing quality of experience of commonly used mobile applications

“…With respect to mobile user experience, according to Jaroucheh et al [10], while modeling usage of pervasive technology and the resulting user's QoE, one should consider the historical as well as current user context and the flexibility of user behavior depending on this context. Similarly, according to Hassenzahl and Tractinsky [9] user experience is influenced by user's internal state, the characteristics of the designed system, the context within which the interaction occurs, and the meaningfulness of the activity.…”

State-of-the-Practice in Evaluation of Quality of Experience in Real-Life Environments

“…This conclusion was drawn based on the results of Jelassi et al in [48] who tested the QoE of a VoIP service. It should also be stressed that even if the tests were conducted in natural environment, where users normally consume this kind of video content, the impact of the social context and the user habits were not analysed (the importance of these factors was highlighted in [49,50]). …”

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

“…That is, in order to track user's behaviour there is a need to consider the context information available in the different domains the user visits [6]. Hence, understanding the user's current situation may require considering the different states the user experienced in these domains.…”

“…tracking and understanding user's tendency) and to identify the contextual situations which span different domains (see [6] for example). Moreover, this enforces the idea that each domain should have its own inference mechanism and in the home domain a cross-domain inference mechanism becomes possible.…”