원문정보
초록
영어
Spatial Partitioning Fragmentation (SPF) is a popular method to partition data in Distributed Spatial Databases (DSDBs). The issue of cross-border queries is an inherent problem however with distributed spatial data queries based on partitioning fragmentation given a continuity and strong correlation of geospatial data. In the case of partitioning fragmentation, a global spatial join can be translated into multiple sub-joins, and then divided into 2 groups: Cross-Border Joins (CBJs) and Non-Cross-Border Joins (NCBJs). The CBJ approach is essential for process efficiency in a distributed spatial query. A compound join based on a topological relationship inquiry and a buffering analysis is a crucial class of spatial queries. This article studies compound join optimization for spatial queries in a DSDB, and proposes a set of theorems and rules for the optimization of CBJs, contributing a removal rule and a filtering rule. This article supplies a Partition Fragmentation Join Strategy (PFJS) to resolve the compound join problem based on these rules. Experimental results show that the PFJS can improve the efficiency of CBJs, when compared with the Naive Join Strategy (NJS) or the Spatial Semi-Join Strategy (SSJS). The PFJS contributes to the optimization of spatial compound joins.
목차
1. Introduction
2. Related Work
3. The Join Optimization Principle for Compound Queries
3.1 The Classification of Spatial Topological Relationship Predicates and Spatial Joins
3.2 The Cross-border Spatial Query Principle Based on Partitioning Fragmentation
3.3 The Buffer Zone Boundary-restricting Theorem of a Spatial Fragment
3.4 The Removal Rule for Fragment Joins of a Compound Query
3.5 The Filtering Rule for Fragment Joins of a Compound Query
3.6 The Join Optimization Principle for CBJs and its Formalization
4. Comparison and Analysis of Three Strategies of Compound Queries
4.1 Naive Join Strategy (NJS)
4.2 Spatial Semi-Join Strategy (SSJS)
4.3 The Partition Fragments’ Join Strategy (PFJS)
4.4 Complexity Analysis of PFJS
5. Experiments and Analysis
5.1 Experimental Environment and Dataset
5.2 Methodology
5.3 Comparison of Performance in Processing a Compound Query with Partitioning Fragmentation
5.4 Comparison of Performance in Processing a Compound Query with Mixed Fragmentation
6. Summary and Future Prospects
Acknowledgements
References