CSM-H-R: A Context Modeling Framework in Supporting Reasoning Automation for Interoperable Intelligent Systems and Privacy Protection

Abstract

The automation of High-Level Context (HLC) reasoning across intelligent systems at scale is imperative because of the unceasing accumulation of contextual data, the trend of the fusion of data from multiple sources (e.g., sensors, intelligent systems), and the intrinsic complexity and dynamism of context-based decision-making processes. To mitigate the challenges posed by these issues, we propose a novel Hierarchical Ontology-State Modeling (HOSM) framework CSM-H-R, which programmatically combines ontologies and states at the modeling phase and runtime phase for attaining the ability to recognize meaningful HLC. It builds on the model of our prior work on the Context State Machine (CSM) engine by incorporating the hierarchies, relationships, and state transitions to take care of the dynamic aspects of context. The design of the framework supports the sharing and interoperation of context among intelligent systems and the components for handling CSMs and the management of hierarchy, relationship, and transition. Case studies are developed for IntellElevator and IntellRestaurant, two intelligent applications in a smart campus setting. The prototype implementation of the framework experiments on translating the HLC reasoning into vector and matrix computing and presents the potential of using advanced probabilistic models to reach the next level of automation in integrating intelligent systems. The implementation of the framework is available at https://github.com/songhui01/CSM-H-R. Additionally, a built-in feature of privacy protection support is discussed in the application domain by anonymization through indexing and reducing information correlation.

Keywords

• Automation
• context dynamism
• context modeling
• context reasoning
• intelligent system
• interoperability