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Lightweight Distributed Execution Engine for Large-Scale ...
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由 J Zhang 著作2015被引用 12 次 — In this study, we report our work in developing a lightweight distributed execution engine for spatial join query processing on large-scale geospatial data. By ...
Lightweight Distributed Execution Engine for Large-Scale Spatial ...
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In this study, we report our work in developing a lightweight distributed execution engine for spatial join query processing on large-scale geospatial data.
III: Medium: Collaborative Research: Spatial Data and ...
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2022年11月24日 — ... Lightweight Distributed Execution Engine for Large-Scale Spatial Join Query Processing" The 4th IEEE International Congress on Big Data , 2015.
Proceedings of the 2015 IEEE International Congress on Big ...
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2015年6月27日 — Lightweight Distributed Execution Engine for Large-Scale Spatial Join Query Processing ... Existing Big Data systems are mostly designed for ...
Large-scale spatial join query processing in Cloud
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We develop SQUID as a distributed subtrajectory query processing engine on Spark, with threefold technical contributions. First, we propose compact index ...
Simin You
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2024年4月25日 — Lightweight Distributed Execution Engine for Large-Scale Spatial Join Query Processing. ... Large-scale spatial join query processing in Cloud.
[PDF] Spatial Join Query Processing in Cloud: Analyzing Design ...
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2015 IEEE International Congress on Big Data. 2015. TLDR. A lightweight distributed execution engine for spatial join query processing on large-scale ...
Advanced join strategies for large-scale distributed computation
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Efficient large-scale distance-based join queries in spatialhadoop ... Lightweight Distributed Execution Engine for Large-Scale Spatial Join Query Processing.
2015 IEEE International Congress on Big Data (BigData ...
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Lightweight Distributed Execution Engine for Large-Scale Spatial Join Query Processing pp. 150-157. Efficient and Self-Balanced ROLLUP Aggregates for Large ...
LocationSpark: In-memory Distributed Spatial Query ...
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由 M Tang 著作2020被引用 27 次 — LocationSpark produces cost-optimized query execution plans over in-memory distributed spatial data. We introduce a new lightweight yet efficient spatial bitmap ...