Trie-Compressed Adaptive Set Intersection

Authors Diego Arroyuelo , Juan Pablo Castillo

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Author Details

Diego Arroyuelo
  • Departamento de Informática, Universidad Técnica Federico Santa María, Santiago, Chile
  • Millennium Institute for Foundational Research on Data, Santiago, Chile
Juan Pablo Castillo
  • Departamento de Informática, Universidad Técnica Federico Santa María, Santiago, Chile
  • Millennium Institute for Foundational Research on Data, Santiago, Chile


We thank Gonzalo Navarro, Cristian Riveros, Adrián Gómez-Brandón, and Francesco Tosoni for enlightening comments, suggestions, and discussions about this work. We also thank the anonymous reviewers whose thorough reviews helped us to improve this paper.

Cite AsGet BibTex

Diego Arroyuelo and Juan Pablo Castillo. Trie-Compressed Adaptive Set Intersection. In 34th Annual Symposium on Combinatorial Pattern Matching (CPM 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 259, pp. 1:1-1:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


We introduce space- and time-efficient algorithms and data structures for the offline set intersection problem. We show that a sorted integer set S ⊆ [0..u) of n elements can be represented using compressed space while supporting k-way intersections in adaptive O(kδlg(u/δ)) time, δ being the alternation measure introduced by Barbay and Kenyon. Our experimental results suggest that our approaches are competitive in practice, outperforming the most efficient alternatives (Partitioned Elias-Fano indexes, Roaring Bitmaps, and Recursive Universe Partitioning (RUP)) in several scenarios, offering in general relevant space-time trade-offs.

Subject Classification

ACM Subject Classification
  • Theory of computation → Data compression
  • Theory of computation → Design and analysis of algorithms
  • Theory of computation → Data structures and algorithms for data management
  • Information systems → Information retrieval query processing
  • Set intersection problem
  • Adaptive Algorithms
  • Compressed and compact data structures


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