# Modeling and Reasoning of Contexts in Smart Spaces Based on Stochastic Analysis of Sensor Data +
## 2 Related Work
### Applicable and Algorithmic Approaches  
Even though the syntactic analysis of contexts in the first category is an advantage in the described contexts, it does provide entities which are needed to reason a context in a given situation. In the second category, modeling contexts is primarily dependent on the reasoning context. In other words, entities in a context model are determined through the reasoning context process. Thus, it is critical to appropriately build rules to catch contextual space states and determine contexts. These approaches are mostly found in the research for context-awareness computing or activity recognition learning. The context-driven simulation approach [13] is an example. Contexts defined in this approach were defined by related context entities, such as sensors and their statuses. Once context models are manually defined, the causality in between contexts also needs to be configured so that they can describe an entire daily living scenario. Another example, the context-aware simulation system for smart home (CASS) [22] adapts the mechanism of a rule-based system, in which the system detects the conflicts of certain rules to control a preconfigured character to move it. The rules need to be defined to describe certain conditions of entities, causing burdens in modeling contexts.  
To reduce the burden and increase automaticity, research into activity recognition has derived high-level information, which is meaningful for activity, from low-level information, which generally consists of sensory data. The research assumed that the collected sensor datasets triggered by human activities could be divided into multiple clusters, each of which contains highly relevant sensors. For instance, in cluster-based classification for activity recognition systems (CBARS) [26], supervised learning models were first built by clustering training datasets, and then unsupervised learning was applied with new testing data to recognize an activity. The challenge was that a supervised learning model was needed for the recognition process. This was addressed in activity recognition using active learning in the overlapped activities (AALO) [27]. It proposed an active and dynamic recognition system, which enables an accurate classification of specific activities according to locations and times, without training data. With cluster-based classifier ensemble (CBCE) [28], an approach was proposed that combines multiple classifiers including Naive Bayesian (NB) models, hidden Markov models (HMMs), and conditional random fields (CRFs). Even though this ensemble of classifiers enabled an unsupervised method to recognize an \"activity\", it is hard to define abstract information for a \"context\", which represents other state spaces in a given cluster, due to lack of contextual entities.  
As context-aware computing is increasingly integrated into end-user service applications, the development of more applicable and practical methods has been the subject of relevant research. In mobile cloud services [34] and recommender systems [35; 36], for instance, historic computational methods such as cosine similarity were applied for the context recognition process. However, it is not their main purpose to find a present context. The primary goal of the approaches is to suggest proper service(s) for the present context found by the recognition process and, thus, an extra process for finding proper services should be developed. As a result, the computational complexity of their proposed methods will greatly increase.