Knowledge-Based Spatiotemporal Abstraction

We present a case study of reusing the same problemsolving method for the related tasks of temporal abstraction and linear-space abstraction. The method, known as knowledge-based temporal abstraction, abstracts high-level concepts from time-stamped raw data, and was evaluated primarily in clinical domains. We applied this method to the domain of traffic control, in which the monitoring task requires linear reasoning along both space and time. First, we reused the method for creation of spatial abstractions over highways, given sensor measurements along each highway. Second, we reused the method to create temporal abstractions of traffic behavior at the same space segmentsions of traffic behavior at the same space segments over time. Thus, we demonstrated the reusabilty of the knowledge-based temporal abstraction method. Our results also show that the knowledge-based temporal-abstraction method can be generalized into a knowledge-based linear abstraction method. This method solves tasks requiringion method. This method solves tasks requiring abstraction of data along a linear distance measure. Bothion of data along a linear distance measure. Both the spatialand the temporal-abstraction methods are instances of that generalized method mapped to different dimensions. We show how a spatiotemporal-abstraction method can be assembled from these methods. The spatiotemporal-abstraction method can be applied to tasks requiring abstraction of time-oriented data over a linear, decomposable space, such as traffic over a set of highways.