Maintaining Queries under Updates Using Heavy-Light Partitioning of the Input Relations
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866909028340203520 |
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| author | Abo-Khamis, Mahmoud Chmielewski, Eden Draghici, Andrei Kara, Ahmet Olteanu, Dan |
| author_facet | Abo-Khamis, Mahmoud Chmielewski, Eden Draghici, Andrei Kara, Ahmet Olteanu, Dan |
| contents | We study the classical incremental view maintenance problem: Given a query and a database, maintain the query output under single-tuple updates (inserts or deletes) to the database such that the tuples in the query output can be enumerated with constant delay after any update.
We introduce a maintenance approach whose update time matches or improves the best update time reported in prior work. Whereas prior approaches are manually tailored to each of a handful of queries, our approach generalizes to arbitrary join queries. It combines three techniques: delta queries, trees of materialized views, and heavy-light data partitioning. The overall update time incurred by our approach for a given join query is characterized by the maintenance width, a new measure that is parameterized by the heavy-light threshold for data partitioning. We show how to find the threshold that minimizes the maintenance width. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08397 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Maintaining Queries under Updates Using Heavy-Light Partitioning of the Input Relations Abo-Khamis, Mahmoud Chmielewski, Eden Draghici, Andrei Kara, Ahmet Olteanu, Dan Databases We study the classical incremental view maintenance problem: Given a query and a database, maintain the query output under single-tuple updates (inserts or deletes) to the database such that the tuples in the query output can be enumerated with constant delay after any update. We introduce a maintenance approach whose update time matches or improves the best update time reported in prior work. Whereas prior approaches are manually tailored to each of a handful of queries, our approach generalizes to arbitrary join queries. It combines three techniques: delta queries, trees of materialized views, and heavy-light data partitioning. The overall update time incurred by our approach for a given join query is characterized by the maintenance width, a new measure that is parameterized by the heavy-light threshold for data partitioning. We show how to find the threshold that minimizes the maintenance width. |
| title | Maintaining Queries under Updates Using Heavy-Light Partitioning of the Input Relations |
| topic | Databases |
| url | https://arxiv.org/abs/2605.08397 |