ArticleProceedings of the ACM on management of data2023
NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks.
Article in Proceedings of the ACM on management of data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Theoretical Analysis of Learned Database Operations under Distribution Shift through Distribution Learnability.Proceedings of machine learning research · 2024Article
- A Neural Database for Answering Aggregate Queries on Incomplete Relational Data (Extended Abstract).Proceedings. International Conference on Data Engineering · 2024Article
- NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks.Proceedings of the ACM on management of data · 2023Article
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Authors and funding
3 authors.
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Abstract
Range aggregate queries (RAQs) are an integral part of many real-world applications, where, often, fast and approximate answers for the queries are desired. Recent work has studied answering RAQs using machine learning (ML) models, where a model of the data is learned to answer the queries. However, there is no theoretical understanding of why and when the ML based approaches perform well. Furthermore, since the ML approaches model the data, they fail to capitalize on any query specific information to improve performance in practice. In this paper, we focus on modeling "queries" rather than data and train neural networks to learn the query answers. This change of focus allows us to theoretically study our ML approach to provide a distribution and query dependent error bound for neural networks when answering RAQs. We confirm our theoretical results by developing NeuroSketch, a neural network framework to answer RAQs in practice. Extensive experimental study on real-world, TPC-benchmark and synthetic datasets show that NeuroSketch answers RAQs multiple orders of magnitude faster than state-of-the-art and with better accuracy.
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Registered trials
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