Complex Spatial Query Processing

Abstract

The user of a Geographical Information System is not limited to conventional spatial selections and joins, but may also pose more complicated and descriptive queries. In this paper, we focus on the ef®cient processing and optimization of complex spatial queries that involve combinations of spatial selections and joins. Our contribution is manifold; we ®rst provide formulae that accurately estimate the selectivity of such queries. These formulae, paired with cost models for selections and joins can be used to combine spatial operators in an optimal way. Second, we propose algorithms that process spatial joins and selections simultaneously and are typically more ef®cient than combinations of simple operators. Finally we study the problem of optimizing complex spatial queries using these operators, by providing (i) cost models, and (ii) rules that reduce the optimization space signi®cantly. The accuracy of the selectivity models and the ef®ciency of the proposed algorithms are evaluated through experimentation.

Query optimizer for spatial join operations

“…Some recent papers optimize complex spatial queries, involving a combination of selections and spatial joins [17], or multi-way spatial joins [18,8]. All the proposed solutions are based on the STT algorithm.…”

“…Index Nested [16] Sort Sweep-Based Spatial Join [12] Priority Queue-Driven Process [2] Build & Match [4] Seeded-Tree [16] Slot Index Spatial Join [17] Partition Based Spatial Merge Join [20] Spatial Hash Join [16] Size Separation Spatial Join [15] Sequential files Priority Queue-Driven Process 2 [2] Hashed files Histogram-based Hash Stripped Join [11] Figure 1 shows a complete set of algorithms that perform the filtering step of a spatial join operation. The algorithms are classified according to the file organization used to maintain object descriptors.…”

Efficient processing of complex similarity queries in RDBMS through query rewriting

“…Complex similarity queries consisting of more than one similarity predicate have been studied considering complex similarity queries over a single feature [10] and over multiple features [7,3]. Algorithms to perform a spatial selection and a spatial join simultaneously are presented in [14]. Indexing structures are used in [10,3] to enhance complex similarity queries in relevance feedback environment.…”