Towards an Extensible Context Ontology for Ambient Intelligence

Lecture Notes in Computer Science, 2011

This paper presents an approach for contextualizing an event-based decision support system for scheduling patient assessments in a hospital. To cope with unexpected delays, patient coordinators often pursue a worst case scenario when scheduling patient assessments, leading to an underutilization of human resources and equipment when the procedure went without complications. We present a context-based decision support system for patient planning that helps the patient coordinator with taking well-informed rescheduling decisions and anticipating changes in other patients' schedules. The system uses information and events produced by medical equipment. As these events can be nondeterministic, we demonstrate how our domain specific context model can be used to contextualize events to enhance their quality and ascertain their meaning.

International Journal of Ambient Computing and Intelligence, 2010

Since the appearance of the Ambient Intelligence paradigm, as an evolution of the Ubiquitous Computing, great deal of the research efforts in this field have been mainly aimed at anticipating user actions and needs, out of a prefixed set. However, Ambient Intelligence is not just constrained to user behaviour pattern matching, but to wisely supervise the whole environment, satisfying those unforeseen requirements or needs, by means of rational decisions. This work points at the lack of commonsense reasoning, as the main reason underlying the existance of these idiots savant systems, capable of accomplishing very specific and complex tasks, but incapable of making decisions out of the prefixed behavioral patterns. This work advocates for the integration of the commonsense reasoning and understanding capabilities as the key elements in bridging the gap between idiot savant systems and real Ambient Intelligence systems.

Lecture Notes in Computer Science, 2006

A major challenge in pervasive computing environments is to provide users with complex, context-sensitive applications, dynamically composed from networked services. In this paper, we present an approach to the dynamic, context-aware composition of services to perform user tasks, i.e., software applications abstractly described on the user's handheld device. Both networked services and user tasks are modeled as semantic Web services in OWLS extended with context information. The distinctive feature of our solution is the ability to compose Web services that expose complex behaviors (conversations) to realize a user task that itself has a complex behavior. Furthermore, the context-related requirements of the task are met by aggregating the context-sensitive behaviors of the individual services.

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct - UbiComp '16, 2016

This paper tackles an important issue of how to use semantic web technologies for Quantified Self (QS). Ontologies offer a great opportunity for data integration and reasoning over data in a QS environment.

Lecture Notes in Computer Science, 2015

Context modelling is one of the stages conducted during the context life cycle. It has the aim of giving meaning and structure to the collected context's raw data. Although there are different context models proposed in the literature, we have identified some gaps that are not fully covered, particularly related to the reusability of the models themselves and the lack of consolidated and standardized ontological resources. To tackle this problem, we adopt a three-layered context ontology perspective and we focus on this paper in the middle layer, which is defined following a prescriptive process and structured in a modular way for supporting reuse.

Computational Intelligence and Its Applications, 2018

In smarts cities environments, a recommender system (RS) has for goal to recommend relevant services to the user who is sometimes mobile. Thus, to be able to provide accurate personalized recommendations, the RS should be aware to the user's context (preferences, location, activities, environment, ...), thereby, it should be Context-Aware Recommender System (CARS, for short). Therefore, the context modeling becomes crucial for developing CARSs. Although there is a lack of context models in the RS literature, several ones have been proposed in pervasive computing field. Nevertheless, most of them are dedicated for closed spaces and should be reviewed to be more suitable for open intelligent environments such as smart cities. This paper aims to propose an extensible ontology-based context model for representing contextual information within a smart city. The proposed context model would subsequently allow to design and develop CARSs.

Proceedings of the IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, 2020

A distributed system's functionality must continuously evolve, especially when environmental context changes. Such required evolution imposes unbearable complexity on system development. An alternative is to make systems able to self-adapt by opportunistically composing at runtime to generate systems of systems (SoSs) that offer value-added functionality. The success of such an approach calls for abstracting the heterogeneity of systems and enabling the programmatic construction of SoSs with minimal developer intervention. We propose a general ontology-based approach to describe distributed systems, seeking to achieve abstraction and enable runtime reasoning between systems. We also propose an architecture for systems that utilize such ontologies to enable systems to discover and 'understand' each other, and potentially compose, all at runtime. We detail features of the ontology and the architecture through two contrasting case studies: one on controlling multiple systems in smart home environment, and another on the management of dynamic computing clusters. We also quantitatively evaluate the scalability and validity of our approach through experiments and simulations. Our approach enables system developers to focus on high-level SoS composition without being constrained by deployment-specific implementation details. CCS CONCEPTS • Computer systems organization → Architectures; • Computer systems organization Self-organizing autonomic computing; KEYWORDS

Journal of Ambient Intelligence and Humanized Computing, 2015

The progressive installation of renewable energy sources requires the coordination of energy consuming devices. At consumer level, this coordination can be done by a home energy management system (HEMS). Interoperability issues need to be solved among smart appliances as well as between smart and non-smart, i.e., legacy devices. We expect current standardization efforts to soon provide technologies to design smart appliances in order to cope with the current interoperability issues. Nevertheless, common electrical devices affect energy consumption significantly and therefore deserve consideration within energy management applications. This paper discusses the integration of smart and legacy devices into a generic system architecture and, subsequently, elaborates the requirements and components which are necessary to realize such an architecture including an application of load detection for the identification of running loads and their integration into existing HEM systems. We assess the feasibility of such an approach with a case study based on a measurement campaign on real households. We show how the information of detected appliances can be extracted in order to create device profiles allowing for their integration and management within a HEMS.

Advances in Systems Analysis, Software Engineering, and High Performance Computing

User Interface (UI) personalization aims at providing the right information, at the right time, and on the right support (tablets, smart-phone, etc.). Personalization can be performed on the interface elements’ presentation (e.g. layout, screen size, and resolution) and on the content provided (e.g., data, information, document). While many existing approaches deal with the first type of personalization, this chapter explores content personalization. To that end, the authors define a context-aware Model Driven Architecture (MDA) approach where the UI model is enriched by data from a domain model and its mapping to a context model. They conclude that this approach is better used only for domains where one envisions several developments of software applications and/or user interfaces.

Datalog Reloaded, 2011

Many interpretations of the notion of context have emerged in various fields and context-aware systems are pervading everyday life, becoming an expanding research field. Context has often a significant impact on the way humans (or machines) act, and on how they interpret things; furthermore, a change in context causes a transformation in the experience that is going to be lived. Accordingly, while the computer science community has initially perceived the context simply as a matter of user time and location, in the last few years this notion has been considered not simply as a state, but as part of a process in which users are involved; thus, sophisticated and general context models and systems have been proposed to support context-aware applications. In this paper we propose a foundational framework for the life-cycle of context-aware system, in which the system design and management activities consider context as an orthogonal, first-class citizen. In doing so, we present a Datalog-based formulation for the definition of context-aware databases. This research has been partially funded by the European Commission, Programme IDEAS-ERC, Project 227977-SMScom.

Sensors, 2012

In the Future Internet, applications based on Wireless Sensor Networks will have to support reconfiguration with minimum human intervention, depending on dynamic context changes in their environment. These situations create a need for building these applications as adaptive software and including techniques that allow the context acquisition and decisions about adaptation. However, contexts use to be made up of complex information acquired from heterogeneous devices and user characteristics, making them difficult to manage. So, instead of building context-aware applications from scratch, we propose to use FamiWare, a family of middleware for Ambient Intelligence specifically designed to be aware of contexts in sensor and smartphone devices. It provides both, several monitoring services to acquire contexts from devices and users, and a context-awareness service to analyze and detect context changes. However, the current version of FamiWare does not allow the automatic incorporation related to the management of new contexts into the FamiWare family. To overcome this shortcoming, in this work, we first present how to model the context using a metamodel to define the contexts that must to be taken into account in an instantiation of FamiWare for a certain Ambient Intelligence system. Then, to configure a new context-aware version of FamiWare and to generate code ready-to-install within heterogeneous devices, we define a mapping that automatically transforms metamodel elements defining contexts into elements of the FamiWare family, and we also use the FamiWare configuration process to customize the new context-aware variant. Finally, we evaluate the benefits of our process, and we analyze both that the new version of the middleware works as expected and that it manages the contexts in an efficient way.