Probabilistic spatio-temporal knowledge bases: Capacity constraints, count queries, and consistency checking

Abstract

The problem of managing spatio-temporal data arises in many applications, such as location-based services, environment monitoring, geographic information system, and many others. In real life, this kind of data is often uncertain. The SPOT framework has been proposed for the representation and processing of probabilistic spatio-temporal data where probability is represented as an interval because the exact value is unknown.In this paper, we enhance the SPOT framework with capacity constraints, which allow users to better model many real-world scenarios. The resulting formalization is called a PST knowledge base. We study the computational complexity of consistency checking, a central problem in this setting. Specifically, we show that the problem is NP-complete and also identify tractable cases.We then consider a relevant kind of queries to reason on PST knowledge bases, namely count queries, which ask for how many objects are in a region at a certain time. We investigate semantics for answering count queries and study their computational complexity, when the knowledge base has capacity constraints and when there are no capacity constraints. We also show cases for which consistency checking can be exploited for query answering.