Incorporating updates in domain indexes: experiences with Oracle spatial R-trees

Kothuri, Ravi; Ravada, Siva; An, Ning

doi:10.1109/icde.2004.1320042

Cited by 1 publication

(1 citation statement)

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“…Spatial indexing methods like the R-tree and its variants [18,32,10] use the MBRs of complex geometry shapes for indexing purposes to speedup query evaluation using the two-step filtering approach [11]. Oracle Spatial and Graph uses a variant of the R-tree index for indexing of spatial data [24,26]. For very large datasets, consisting of billions of rows, the index creation process may take up to a few hours.…”

Section: Introductionmentioning

confidence: 99%

Statistics collection in oracle spatial and graph

Bamba

Ravada

et al. 2013

Proc. VLDB Endow.

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Oracle Spatial and Graph is a geographic information system (GIS) which provides users the ability to store spatial data alongside conventional data in Oracle. As a result of the coexistence of spatial and other data, we observe a trend towards users performing increasingly complex queries which involve spatial as well as non-spatial predicates. Accurate selectivity values, especially for queries with multiple predicates requiring joins among numerous tables, are essential for the database optimizer to determine a good execution plan. For queries involving spatial predicates, this requires that reasonably accurate statistics collection has been performed on the spatial data. For extensible data cartridges such as Oracle Spatial and Graph, the optimizer expects to receive accurate predicate selectivity and cost values from functions implemented within the data cartridge. Although statistics collection for spatial data has been researched in academia for a few years; to the best of our knowledge, this is the first work to present spatial statistics collection implementation details for a commercial GIS database. In this paper, we describe our experiences with implementation of statistics collection methods for complex geometry objects within Oracle Spatial and Graph. Firstly, we exemplify issues with previous partitioning-based algorithms in presence of complex geometry objects and suggest enhancements which resolve the issues. Secondly, we propose a main memory implementation which not only speeds up the disk-based partitioning algorithms but also utilizes existing R-tree indexes to provide surprisingly accurate selectivity estimates. Last but not the least, we provide extensive experimental results and an example study which displays the efficacy of our approach on Oracle query performance.

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Section: Introductionmentioning

confidence: 99%