Context Inference Engine (CiE): Inferring Context

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).