Situation Identification using Context Space Theory and Decision Tree

Context Space Theory (CST) is a geometrical approach used to represent contexts and situations in situation-aware computing applications. In this theory, situations are represented in a multidimensional space, where each dimension corresponds to an interesting feature of the context. The primary advantage of CST lies in its capacity to effortlessly integrate multiple factors, creating a meaningful representation of situations that can be observed and manipulated by experts. Moreover, it empowers experts to customize the situation space to align with their knowledge and understanding of the situation. However, when applied to real-world scenarios, modeling complex situation spaces can be time-consuming and labor-intensive. This is due to the manual effort required in defining contribution functions for each context feature, as well as determining weights and thresholds to identify the situation space. To address this challenge, the paper proposes a hybrid approach that combines decision trees with the CST, thereby expediting the definition of situation spaces. Decision trees are employed to automatically identify an initial definition of the contribution functions and weights, reducing the workload on human experts. To demonstrate the efficacy of this approach, the paper showcases a case study focused on the management of the Covid-19 pandemic situation in Italy.