Continuous Query Processing in Spatio-Temporal Databases

In this paper, we aim to develop a framework for continuous query processing in spatio-temporal databases. The proposed framework distinguishes itself from other query processors by employing two main paradigms: (1) Scalability in terms of the number of concurrent continuous spatio-temporal queries. (2) Incremental evaluation of continuous spatio-temporal queries. Scalability is achieved thorough employing a

shared execution

paradigm. Incremental evaluation is achieved through computing only the updates to the previously reported answer. We distinguish between two types of updates;

positive

updates and

negative

updates. Positive or negative updates indicate that a certain object should be added to or removed from the previously reported answer, respectively. The proposed framework is applicable to a wide variety of continuous spatio-temporal queries where we do not have any constraints about the mutability of objects and queries (i.e., both objects and queries can be either stationary or moving) or the movement representation (i.e., movement can be represented either by sampling or trajectory).