Aggregate Queries on Sparse Databases
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Abstract (arXiv)
We propose an algebraic framework for studying efficient algorithms for query evaluation, aggregation, enumeration, and maintenance under updates, on sparse databases. Our framework allows to treat those problems in a unified way, by considering various semirings, depending on the considered problem. As a concrete application, we propose a powerful query language extending first-order logic by aggregation in multiple semirings. We obtain an optimal algorithm for computing the answers of such queries on sparse databases. More precisely, given a database from a fixed class with bounded expansion, the algorithm computes in linear time a data structure which allows to enumerate the set of answers to the query, with constant delay between two outputs.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Dec 2019.
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Citing papers per year: 2024: 1; 2025: 0; 2026: 2.
3 papers.
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- 2024Query languages for neural networks19 Aug 2024