Use of a weighted matching algorithm to sequence clusters in spatial join processing

One of the most expensive operations in a spatial database is spatial join processing. This study focuses on how to improve the performance of such processing. The main objective is to reduce the Input/Output (I/O) cost of the spatial join process by using a technique called cluster-scheduling. Generally, the spatial join is processed in two steps, namely filtering and refinement. The cluster-scheduling technique is performed after the filtering step and before the refinement step and is part of the housekeeping phase. The key point of this technique is to realise order wherein two consecutive clusters in the sequence have maximal overlapping objects. However, finding the maximal overlapping order has been shown to be Nondeterministic Polynomial-time (NP)-complete. This study proposes an algorithm to provide approximate maximal overlapping (AMO) order in a Cluster Overlapping (CO) graph. The study proposes the use of an efficient maximum weighted matching algorithm to solve the problem of finding AMO order. As a result, the I/O cost in spatial join processing can be minimised

Singularities Make Spatial Join Scheduling Hard

. The efficiency of database join operations depends crucially on how page fetches are scheduled. In general, finding an optimum schedule is NP-hard. We show that for a class of popular spatial clustering techniques used for spatial data structures, an optimum page fetch schedule that holds two pages in main memory can be computed in time linear in the length of the schedule. In full generality, we prove spatial join scheduling to be NP-hard. 1 Introduction and Overview In databases in general and spatial databases in particular, join processing is one of the most expensive operations. One of the reasons is that for large databases, main memory capacity is a bottleneck: Pages may need to be fetched from disk more than once in order to compute a join. Since disk access time usually is the dominant part of the join computation time, it pays to schedule disk accesses carefully. This is not always easy: For two relations on disk, where each page contains a set of tuples, and an equijoin o..