Context Inference Engine (CiE): Inferring Context

Context Inference Engine (CiE): Inferring Context

Umar Mahmud (Department of Computer Software Engineering, Military College of Signals, National University of Sciences & Technology(NUST), Islamabad, Pakistan) and Mohammed Younus Javed (Dean of Faculty of Engineering, College of Electrical & Mechanical Engineering, National University of Sciences & Technology(NUST), Islamabad, Pakistan)
DOI: 10.4018/japuc.2012070102

Abstract

Context Awareness is the task of inferring contextual data acquired through sensors present in the environment. ‘Context’ encompasses all knowledge bounded by a scope and includes attributes of machines and users. A general context aware system is composed of context gathering and context inference modules. This paper proposes a Context Inference Engine (CiE) that classifies the current context as one of several recorded context activities. The engine follows a distance measure based classification approach with standard deviation based ranks to identify likely activities. The paper presents the algorithm and some results of the context classification process.
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2. Context Inference Approaches In Literature

The first generation context aware systems were designed as applications that considered spatial and temporal factors as the context of the system (Schilit, Hilbert, & Trevor, 2002; Want, Hopper, Falcao, & Gibbons, 1992; Cheverst, Mitchell, & Davies, 1999; Abowd, Atkenson, Hong, Long, Kooper, & Pinkerton, 1997; Román, Hess, Cerqueira, Campbell, & Nahrstedt, 2002; Hofer, Pichler, Leonhartsberger, Altmann, & Werner, 2002; Wang, 2004; de Deugd, Carroll, Kelly, Millett, & Ricker, 2006; Dey & Abowd, 1999; Riaz, Kiani, Lee, Han, & Lee, 2005). These first generation systems lacked the inference support based on classification techniques and were primarily RBA. Many researchers have identified RBA as a promising technique due to its efficiency in computation (Chen, Finin, & Joshi, 2003; Mahmud, Iltaf, Rehman, & Kamran, 2007; Gu, Pung, & Zhang, 2004; Fahy & Clarke, 2004; Guo, Gao, Ma, Li, & Huang, 2008). Use of context matching (Samulowitz, Michahelles, & Linnhoff-Popien, 2001) has provided an improvement on RBA. The design of a context aware system following a rule based technique though considered efficient lacks flexibility in terms of learning new rules and situations. Moreover, the strain on the developer to introduce new rules and modify them in different software engineering phases is high. Context aware systems are thus restricted to the quantity of the rules developed by the engineer.

To provide learning capability in the context aware system, some researchers have proposed context recognition processing as the alternative to the inference task (Korpipaa, Mantyjarvi, Kela, Keranen, & Malm, 2003; Mayrhofer, 2004; Blum, 2005; Brdiczka, Crowley, & Reignier, 2007; Yuan & Wu, 2008). The context aware system recognizes the current situation and proposes appropriate actions as suggested by the developer. Use of supervised learning has provided encouraging results in context recognition (Korpipaa, Mantyjarvi, Kela, Keranen, & Malm, 2003; Brdiczka, Crowley, & Reignier, 2007). The researchers following the context recognition generally agree on the distance being the criterion for similarity among different contextual situations (Mayrhofer, 2004; Brdiczka, Crowley, & Reignier, 2007; Yuan & Wu, 2008). The similarities in context highlight the importance of contextual conflicts that may arise in dynamic situations. Priorities have been proposed as a general remedy to resolve conflicts (Mayrhofer, 2004; Shin & Woo, 2005).

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