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• W2034856714 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." @default.
• W2034856714 created "2016-06-24" @default.
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• W2034856714 date "2014-06-30" @default.
• W2034856714 modified "2023-09-27" @default.
• W2034856714 title "Skew-resistant parallel in-memory spatial join" @default.
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• W2034856714 doi "https://doi.org/10.1145/2618243.2618262" @default.
• W2034856714 hasPublicationYear "2014" @default.
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