Multiple range query optimization in spatial databases

Abstract

Abstract. In order to answer efficiently range queries in 2-d R-trees, first we sort queries by means of a space filling curve, then we group them together, and finally pass them for processing. Initially, we consider grouping of pairs of requests only, and give two algorithms with exponential and linear complexity. Then, we generalize the linear method, grouping more than two requests per group. We evaluate these methods under different LRU buffer sizes, measuring the cache misses per query. We present experimental results based on real and synthetic data. The results show that careful query scheduling can improve substantially the overall performance of multiple range query processing.

Joint Top-K Spatial Keyword Query Processing

“…Papadopoulos et al [17] use a space filling curve to group queries so as to improve the overall performance of processing all queries. Zhang et al [28] study the processing of multiple nearest neighbor queries; they propose R-tree-based solutions and heuristics for the grouping of queries.…”

“…Specifically, the key of e is defined as its minimum distance to its relevant subqueries of Q. The loop has two phases: (I) checking whether the dequeued entry e can contribute a closer and relevant result for some subquery (lines [8][9][10][11][12][13][14][15][16][17], and (II) processing the child node of e (lines [18][19][20][21][22][23][24][25][26][27][28][29][30]. Algorithm 2 GROUP (Joint query Q, Tree root root, Integer k)…”

“…If |D| < B/2 then D is returned to the parent algorithm call (lines 3-4) for further processing. If |D| > B (line 5) then we partition D (lines [6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

“…The remaining objects from these recursive calls (i.e., the sets T + and T − ) are then merged into the set T (line 15). If T has enough objects, it is added as a node to L (lines [16][17]. Otherwise, set T is returned to the parent algorithm call (lines [18][19].…”

Performance of nearest neighbor queries in R-trees

“…Research interests focused mainly on the design of robust and efficient spatial data structures [Gutt84,Henr89,Guen89,Beck90,Kame94], the invention of new spatial data models [Laur92], the construction of effective query languages [Egen94] and the query processing and optimization of spatial queries [Oren86,Aref93,Papa95].…”