The current evolution of Service-Oriented Computing in ubiquitous systems is leading to the development of context-aware services. Context-aware services are services of which the description is enriched with context information related to non-functional requirements, describing the service execution environment or its adaptation capabilities. This information is often used for discovery and adaptation purposes. However, in real-life systems, context information is naturally dynamic, uncertain, and incomplete, which represents an important issue when comparing the service description with user requirements. Uncertainty of context information may lead to an inexact match between provided and required service capabilities, and consequently to the non-selection of services. In this chapter, we focus on how to handle uncertain and incomplete context information for service selection. We consider this issue by presenting a service ranking and selection algorithm, inspired by graph-based matching algorithms. This graph-based service selection algorithm compares contextual service descriptions using similarity measures that allow inexact matching. The service description and non-functional requirements are compared using two kinds of similarity measures: local measures, which compare individually required and provided properties, and global measures, which take into account the context description as a whole.

No abstract

Background Medical smartphone apps and mobile health devices are rapidly entering mainstream use because of the rising number of smartphone users. Consequently, a large amount of consumer-generated data is being collected. Technological advances in innovative sensory systems have enabled data connectivity and aggregation to become cornerstones in developing workable solutions for remote monitoring systems in clinical practice. However, few systems are currently available to handle such data, especially for clinical use. Objective The aim of this study was to develop and implement the digital health research platform for mobile health (DHARMA) that combines data saved in different formats from a variety of sources into a single integrated digital platform suitable for mobile remote monitoring studies. Methods DHARMA comprises a smartphone app, a Web-based platform, and custom middleware and has been developed to collect, store, process, and visualize data from different vendor-specific sensors. The middleware is a component-based system with independent building blocks for user authentication, study and patient administration, data handling, questionnaire management, patient files, and reporting. Results A prototype version of the research platform has been tested and deployed in multiple clinical studies. In this study, we used the platform for the follow-up of pregnant women at risk of developing pre-eclampsia. The patients’ blood pressure, weight, and activity were semi-automatically captured at home using different devices. DHARMA automatically collected and stored data from each source and enabled data processing for the end users in terms of study-specific parameters, thresholds, and visualization. Conclusions The increasing use of mobile health apps and connected medical devices is leading to a large amount of data for collection. There has been limited investment in handling and aggregating data from different sources for use in academic and clinical research focusing on remote monitoring studies. In this study, we created a modular mobile health research platform to collect and integrate data from a variety of third-party devices in several patient populations. The functionality of the platform was demonstrated in a real-life setting among women with high-risk pregnancies.

No abstract

Publish/Subscribe is an interesting communication paradigm because it fosters a high degree of decoupling between the communicating parties and provides the ability to communicate in an asynchronous way. Publish/Subscribe systems have been extensively studied for wired networks but designing a Publish/Subscribe system for mobile ad hoc networks is still a challenge. In this paper we propose a lightweight Publish/Subscribe system for mobile ad hoc networks which uses a limited gossip mechanism to match the published messages with the subscriptions. The goal of this work is to reduce the number of exchanged messages used for communication and maintenance while keeping an acceptable delivery ratio. Experimental results show that Fadip achieves an acceptable delivery ratio even in high mobility rates.

Nowadays novel embedded computing devices enable vehicles to form large scale mobile peer-to-peer networks in which they can assist each other to improve their driving experience. Therefore context-aware communication is considered to be vital for inducing inter-vehicular intelligence between groups of vehicles with similar interests. However, traditional network addressing schemes are not well suited for group-based communication in large scale vehicular networks. The classical network paradigms of multicasting and broadcasting to define groups are too limited. First, there is no way to optimize network traffic based on the contextual characteristics of the nodes. Second, the groups of nodes are highly dynamic with vehicles randomly joining and leaving multiple groups. We propose an information dissemination approach based on context grouping in which only relevant information is shared among nodes. We evaluate our approach in a large scale vehicular network where groups are formed based on the location and shared interests of the nodes. The experiments show that by inducing our context-based grouping mechanism we can significantly eliminate irrelevant information and reduce overall network traffic in a scalable way.

Abstract. Many current context-aware systems only react to the current situation and context changes as the occur. In order to anticipate to future situations and exhibit proactive behavior, these systems should also be aware of their future context. Since predicted context is uncertain and can be wrong, applications need to be able to assess the quality of the predicted context information. This allows applications to make a wellinformed decision whether to act on the prediction or not. In this paper, we present prediction quality metrics to evaluate the probability of future situations. These metrics are integrated in a structured prediction component development methodology, which is illustrated by a health care application scenario. The metrics and the methodology address the needs of the developer aiming to build context-aware applications that realize proactive behavior with regard to past, present and future context.

Abstract. In Business Intelligence systems, users interact with data warehouses by formulating OLAP queries aimed at exploring multidimensional data cubes. Being able to predict the most likely next queries would provide a way to recommend interesting queries to users on the one hand, and could improve the efficiency of OLAP sessions on the other. In particular, query recommendation would proactively guide users in data exploration and improve the quality of their interactive experience. In this paper, we propose a framework to predict the most likely next query and recommend this to the user. Our framework relies on a probabilistic user behavior model built by analyzing previous OLAP sessions and exploiting a query similarity metric. To gain insight in the recommendation precision and on what parameters it depends, we evaluate our approach using different quality assessments.