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Pontificia Universidad Catolica de Chile

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Selected work

Representative Papers

PathFinder: A unified approach for handling paths in graph query languages

Jun 03, 2023

Path matching in graph query languages (e.g., Cypher, SQL/PGQ, GQL) lacks a unified and efficient processing mechanism—particularly when supporting complex path semantics (e.g., shortest paths, simple paths) and regular-expression constraints on edge labels—posing dual challenges in expressive power and performance. This paper introduces the first cross-language, general-purpose path-solving framework. It features a compact symbolic path representation and integrates dynamic-programming-based enumeration, incremental pipelined execution, and regex compilation optimizations to enable unified modeling and efficient evaluation of diverse path semantics and edge-label constraints. Experimental evaluation on real-world datasets and complex queries demonstrates an order-of-magnitude speedup over state-of-the-art graph engines, while maintaining high expressiveness, strong scalability, and behavioral stability.

2 citationsRead paper

Structural Indexing of Relational Databases for the Evaluation of Free-Connex Acyclic Conjunctive Queries

Jan 08, 2026arXiv.org

This work proposes a novel indexing approach for the efficient evaluation of free-connex acyclic conjunctive queries (fc-ACQs) over relational databases, leveraging structural symmetries inherent in tuple data. By introducing an auxiliary database $D_{col}$ and employing a relation coloring refinement technique, the method constructs a compact structural index that enables linear-time preprocessing and constant-delay enumeration or counting. This is the first approach to exploit internal structural symmetries in relational data, departing from conventional value- or order-based indexing paradigms. The resulting index achieves significant compression on canonical structures such as binary trees and regular graphs—while maintaining worst-case linear size—and supports efficient evaluation of all fc-ACQs in time complexity strictly better than the size of the underlying database.

1 citationsRead paper
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Latest Papers

Graph Neural Assisted Actor-Critic for Latency-Efficient Edge Vision System

Aug 17, 2026

This study addresses the high latency in UAV video transmission that hinders ground-assisted efficiency by proposing an edge visual optimization framework integrating Graph Convolutional Networks (GCN) with A2C reinforcement learning. The proposed method leverages GCN to exploit pixel-level feature correlations, enabling selective sub-region transmission as a superior alternative to conventional full-frame approaches, while employing Lagrangian duality for policy optimization under latency constraints. Experimental results demonstrate that this model significantly reduces both video transmission latency and false detection rates, outperforming existing deep reinforcement learning methods and state-of-the-art models. Consequently, the framework effectively achieves low-latency, high-efficiency edge intelligent perception for UAV applications.

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