Configuring Spatial Grids for Efficient Main Memory Joins

Tauheed, Farhan; Heinis, Thomas; Ailamaki, Anastasia

doi:10.1007/978-3-319-20424-6_19

Cited by 3 publications

(3 citation statements)

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“…It partitions space in a uniform grid and assigns the elements, belonging to the pivots, to the cells they overlap with. Finally, it probes the grid with the elements from the candidate set to find pairs of intersecting elements [11]. When TRANSFORMERS uses the node level as data layout it additionally filters elements before the in-memory join.…”

Section: Transformers Indexingmentioning

confidence: 99%

“…Like the approaches we compare it with and driven by our motivating application, TRANSFORMERS is designed to join two static spatial datasets and we do not compare it to self-joins or trajectory joins. PBSM and TRANSFORMERS use the grid hash join [11] as the in-memory join algorithm, while R-TREE uses the plane sweep. R-TREE is based on R-Trees bulkloaded using the STR approach [10].…”

Section: A Experimental Setupmentioning

confidence: 99%

“…By default, its strategy causes elements replication that leads to additional I/Os, comparisons and deduplication. Although the grid hash join [11] provides better performance for PBSM than the plane sweep join, it additionally increases the replication rate. The R-TREE join suffers from overlap at tree level and therefore performs on average 21 times more comparisons.…”

Section: Dataset Distributionsmentioning

confidence: 99%

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TRANSFORMERS: Robust spatial joins on non-uniform data distributions

Pavlović

Heinis

Tauheed³

et al. 2016

2016 IEEE 32nd International Conference on Data Engineering (ICDE)

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Abstract-Spatial joins are becoming increasingly ubiquitous in many applications, particularly in the scientific domain. While several approaches have been proposed for joining spatial datasets, each of them has a strength for a particular type of density ratio among the joined datasets. More generally, no single proposed method can efficiently join two spatial datasets in a robust manner with respect to their data distributions. Some approaches do well for datasets with contrasting densities while others do better with similar densities. None of them does well when the datasets have locally divergent data distributions.In this paper we develop TRANSFORMERS, an efficient and robust spatial join approach that is indifferent to such variations of distribution among the joined data. TRANSFORM-ERS achieves this feat by departing from the state-of-the-art through adapting the join strategy and data layout to local density variations among the joined data. It employs a join method based on data-oriented partitioning when joining areas of substantially different local densities, whereas it uses big partitions (as in space-oriented partitioning) when the densities are similar, while seamlessly switching among these two strategies at runtime. We experimentally demonstrate that TRANSFORMERS outperforms state-of-the-art approaches by a factor of between 2 and 8.

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Section: Transformers Indexingmentioning

confidence: 99%

Section: A Experimental Setupmentioning

confidence: 99%

Section: Dataset Distributionsmentioning

confidence: 99%

See 1 more Smart Citation

TRANSFORMERS: Robust spatial joins on non-uniform data distributions

Pavlović

Heinis

Tauheed³

et al. 2016

2016 IEEE 32nd International Conference on Data Engineering (ICDE)

Self Cite

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Two-Layer Space-Oriented Partitioning for Non-Point Data

Tsitsigkos

Bouros

Lampropoulos

et al. 2024

IEEE Trans. Knowl. Data Eng.

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Spatial joins

Bouros

Mamoulis

2019

SIGSPATIAL Special

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The spatial join is a popular operation in spatial database systems and its evaluation is a well-studied problem. This paper reviews research and recent trends on spatial join evaluation. The complexity of different data types, the consideration of different join predicates, the use of modern commodity hardware, and support for parallel processing open the road to a number of interesting directions for future research, some of which we outline in the paper.

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Configuring Spatial Grids for Efficient Main Memory Joins

Cited by 3 publications

References 4 publications

TRANSFORMERS: Robust spatial joins on non-uniform data distributions

TRANSFORMERS: Robust spatial joins on non-uniform data distributions

Two-Layer Space-Oriented Partitioning for Non-Point Data

Spatial joins

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