Concurrent Execution of Data Mining Queries for Spatial Collocation Pattern Discovery

In spatial databases, Collocation Pattern Discovery is a very important data mining technique. It consists in searching for types of spatial objects that are frequently located together. Due to high requirements for CPU, memory or storage space, such data mining queries are often executed at times of low user activity. Multiple users or even the same user experimenting with different parameters can define many queries during the working hours that are executed, e.g., at off-peak night-time hours. Given a set of multiple spatial data mining queries, a data mining system may take advantage of potential overlapping of the queried datasets. In this paper we present a new method for concurrent processing of multiple spatial collocation pattern discovery queries. The aim of our new algorithm is to improve processing times by reducing the number of searches for neighboring objects, which is a crucial step for the identification of collocation patterns.