Incremental application of knowledge to continuously arriving time-oriented data

In our previous work, we introduced a computational architecture that effectively supports the tasks of continuous monitoring and of aggregation querying of complex domain meaningful time-oriented concepts and patterns (temporal abstractions), in environments featuring large volumes of continuously arriving and accumulating time-oriented raw data. Examples include provision of decision support in clinical medicine, making financial decisions, detecting anomalies and potential threats in communication networks, integrating intelligence information from multiple sources, etc. In this paper, we describe the general, domain-independent but task-specific problem-solving method underling our computational architecture, which we refer to as incremental knowledge-based temporal abstraction (IKBTA). The IKBTA method incrementally computes temporal abstractions by maintaining persistence and validity of continuously computed temporal abstractions from arriving time-stamped data. We focus on the computational framework underlying our reasoning method, provide well-defined semantic and knowledge requirements for incremental inference, which utilizes a logical model of time, data, and high-level abstract concepts, and provide a detailed analysis of the computational complexity of our approach.