Structural Indexing of Relational Databases for the Evaluation of Free-Connex Acyclic Conjunctive Queries
Cristian Riveros, Benjamin Scheidt, Nicole Schweikardt

TL;DR
This paper introduces a novel index structure based on structural symmetries in relational databases to efficiently evaluate free-connex acyclic conjunctive queries, outperforming traditional value-based indexes.
Contribution
The authors develop a new index leveraging structural symmetries, enabling constant-delay enumeration and counting of query answers after linear preprocessing, a significant improvement over prior methods.
Findings
Index size relates to structural symmetries, e.g., logarithmic for trees.
Index allows constant-delay enumeration of query answers.
Performance can be better than scanning the entire database.
Abstract
We present an index structure to boost the evaluation of free-connex acyclic conjunctive queries (fc-ACQs) over relational databases. The main ingredient of the index associated with a given database is an auxiliary database . Our main result states that for any fc-ACQ over , we can count the number of answers of or enumerate them with constant delay after a preprocessing phase that takes time linear in the size of . Unlike previous indexing methods based on values or order (e.g., B+ trees), our index is based on structural symmetries among tuples in a database, and the size of is related to the number of colors assigned to by Scheidt and Schweikardt's "relational color refinement" (2025). In the particular case of graphs, this coincides with the minimal size of an equitable partition of the graph. For example, the size of is…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsData Management and Algorithms · Advanced Database Systems and Queries · Algorithms and Data Compression
