A Methodological Approach to Quality of Future Context for Proactive Smart Systems

Abstract

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.

A user-centric vision of service-oriented Pervasive Information Systems

“…An anticipatory behavior has been proposed by some works on context prediction [22][37] [40] and on context-aware service recommendation [1] [43]. Context prediction works try to anticipate user's next context [22] [40] or fulfill missing context information [37]; while recommendation works try to proactively propose services to the user [1] [43].…”

“…Context prediction works try to anticipate user's next context [22] [40] or fulfill missing context information [37]; while recommendation works try to proactively propose services to the user [1] [43]. Both are based on the analysis of user's history in order to identify common patterns enabling the anticipation of user's next situation.…”

Intention Prediction Mechanism in an Intentional Pervasive Information System

“…Concerning the first aspect, we have major contributions towards generic context prediction, such as Mayrhofer, Harald et al(2003), Sigg et al (2010) and Petzold et al (2005). According to Vanrompay (2011), Mayrhofer et al (2003, 2004 propose an architecture and a framework for high-level context prediction. It is based on an unsupervised classification, which tries to find previously unknown classes from input data.…”

“…By avoiding focusing on the prediction of future user"s situation, current systems lack an important element inthe search for transparency and homogeneity. In order to help end users obtain their desired services, some research on Ubiquitous Computing (Abbar et al, 2009;Boytsov&Zaslavsky, 2011;Sigg et al, 2010;Vanrompay, 2011) proposes mechanisms to automatically predict or recommend services using user"s context. These researches focus especially on context prediction and on context based recommendation systems.…”

“…All these works try to anticipate user"s needs in order to offer him more transparency. Context prediction approaches (Boytsov&Zaslavsky, 2011;Meiners et al 2010;Sigg, 2010;Vanrompay, 2011) try to predict user"s next context based on the user"s current context and history. However, none of these works consider the services a user invokes on a given context.…”