Geo is a framework for optimizing graph pattern matching queries via rewrite rules and equality saturation that discovers equivalences and reduces costs by up to 99%.
In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (Portland, OR, USA) (SIGMOD ’20)
3 Pith papers cite this work, alongside 56 external citations. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
PystachIO is a PyTorch-based distributed OLAP engine that delivers up to 3x end-to-end speedups for storage-resident queries by combining fast RDMA networks, NVMe storage, and I/O-computation overlap optimizations.
An experimental evaluation of learned spatial indexes derives a decision tree for index selection under varying data skew, query selectivity, and storage conditions, validated on real point sets.
citing papers explorer
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Geo: A Query Rewrite Framework for Graph Pattern Mining
Geo is a framework for optimizing graph pattern matching queries via rewrite rules and equality saturation that discovers equivalences and reduces costs by up to 99%.
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PystachIO: Efficient Distributed GPU Query Processing with PyTorch over Fast Networks & Fast Storage
PystachIO is a PyTorch-based distributed OLAP engine that delivers up to 3x end-to-end speedups for storage-resident queries by combining fast RDMA networks, NVMe storage, and I/O-computation overlap optimizations.
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Evaluating Learned Spatial Indexes
An experimental evaluation of learned spatial indexes derives a decision tree for index selection under varying data skew, query selectivity, and storage conditions, validated on real point sets.