Skew-resistant parallel in-memory spatial join
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Computer-science paper on a skew-resistant parallel spatial join algorithm; the object is a database algorithm.
It develops a spatial-join algorithm for computing applications, not a study of research.
Parallel database algorithm for spatial joins; computer systems performance, not scholarly communication.
Abstract
Spatial join is a crucial operation in many spatial analysis applications in scientific and geographical information systems. Due to the compute-intensive nature of spatial predicate evaluation, spatial join queries can be slow even with a moderate sized dataset. Efficient parallelization of spatial join is therefore essential to achieve acceptable performance for many spatial applications. Technological trends, including the rising core count and increasingly large main memory, hold great promise in this regard. Previous parallel spatial join approaches tried to partition the dataset so that the number of spatial objects in each partition was as equal as possible. They also focused only on the filter step. However, when the more compute-intensive refinement step is included, significant processing skew may arise due to the uneven size of the objects. This processing skew significantly limits the achievable parallel performance of the spatial join queries, as the longest-running spatial partition determines the overall query execution time.
Stored with the screening record, where it is evidence for the labels above.
The record
- Venue
- Topic
- Data Management and Algorithms
- Field
- Computer Science
- Canadian institutions
- University of Toronto
- Funders
- —
- Keywords
- SkewComputer sciencePartition (number theory)Join (topology)Spatial analysisParallel computingSpatial databaseTheoretical computer scienceMathematics
- Has abstract in OpenAlex
- yes