The latest developments of mobile devices include a variety of hardware features that allow for more rich data collection and services. Numerous sensors, Internet connectivity, low energy Bluetooth connectivity to other devices (e.g., smart watches, activity tracker, health data monitoring devices) are just some examples of hardware that helps to provide additional information that can be beneficially used for many application domains. Among others, they could be utilized in mobile learning scenarios (for data collection in science education, field trips), in mobile health scenarios (for health data collection and monitoring the health state of patients, changes in health conditions and/or detection of emergency situations), and in personalized recommender systems. This information captures the current context situation of the user that could help to make mobile applications more personalized and deliver a better user experience. Moreover, the context related information collected by the mobile device and the different applications can be enriched by using additional external information sources (e.g., Web Service APIs), which help to describe the user’s context situation in more details.

The main challenge in context modeling is the lack of generalization at the core of the model, as most of the existing context models depend on particular application domains or scenarios. We tackle this challenge by conceptualizing and designing a rich generic context model. In this thesis, we present the state of the art of recent approaches used for context modeling and introduce a rich context model as an approach for modeling context in a domain-independent way. Additionally, we investigate whether context information can enhance existing mobile applications by making them sensible to the user’s current situation. We demonstrate the reusability and flexibility of the rich context model in a several case studies. The main contributions of this thesis are: (1) an overview of recent, existing research in context modeling for different application domains; (2) a theoretical foundation of the proposed approach for modeling context in a domain-independent way; (3) several case studies in different mobile application domains.

Nowadays, people use a wide range of devices (e.g., mobile phones, smart watches, tablets, activity bands, laptops) to access different digital applications and services. The ubiquitous distribution of these devices allows them to be used across different settings, in different situations, and in a large number of different domains. These devices contain a variety of hardware features (e.g., sensors, Internet connectivity, camera, low energy Bluetooth connectivity) that allow for gathering diverse data types that can be used in many application domains. Among other areas, they could be utilized in mobile learning situations (e.g., for data collection in science education, field trips), to support mobile health (e.g., for health data collection, monitoring the health states of patients, monitoring for changes in health conditions and/or detection of emergency situations), and to provide personalised recommendations (e.g., for recommending services based on the user’s location and time). These devices help to capture the current contextual situation of the user, which could make applications more personalised in order to generate novel services and to deliver a better user experience. However, most applications lack capturing the user’s context situation or have been often limited to the user’s current location and time. Therefore, new ways of conceptualising and processing contextual information are necessary in order to support the development of personalised and contextualised applications and services. Substantial research in the field of contextualisation has explored aspects related to computational modelling of context focusing on just one specific application domain. Most of the existing context models do not address the issue of generalization as being a core feature of the model. Thus, the model is to a particular application domain or scenario. The main goal of this thesis is to conceptualise, design and validate an approach for a unified context model and to investigate its applicability in different application domains. This thesis presents the state of the art of recent approaches used for context modelling and it introduces a rich context model as an approach for modelling context in a domain-independent way. Reusability and flexibility of the proposed rich context model are illustrated by showing several applications domains (e.g., mobile learning, recommender systems, data analytics, eHealth) in which the model has been tested. This work explains the promising potential of using rich context models to support the personalization of services that are tied to the user’s current context. The results and outcomes of this work pave the way for new opportunities and further research related to the integration and combination of the proposed rich context model with machine learning techniques.