Approximate Distance Oracle for Fault-Tolerant Geometric Spanners

Authors: Kyungjin Cho, Jihun Shin, Eunjin Oh

AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical In this paper, we present approximate distance and shortest-path oracles for fault-tolerant Euclidean spanners motivated by the routing problem in real-world road networks. The paper focuses on theoretical contributions, presenting algorithm design, lemmas, and complexity analysis (Table 1 shows Space, Construction time, and Query time), rather than empirical evaluation or experimental results. The authors state, "we choose a different approach to bridge the gap between theory and practice from a more theoretical point of view in a more classical method: first define a theoretical model for real-world road networks, and then design an oracle on this theoretical model."
Researcher Affiliation Academia Kyungjin Cho, Jihun Shin, Eunjin Oh Pohang University of Science and Technology, South Korea. {kyungjincho, tmtingsamuel, eunjin.oh}@postech.ac.kr
Pseudocode No The paper describes the construction and operation of its data structures (e.g., FT-Data Structure, Kernel Oracle) in detail within the text, but it does not include formally labeled pseudocode blocks or algorithms.
Open Source Code No The paper does not provide any explicit statement about open-sourcing code or a link to a code repository.
Open Datasets No As a theoretical paper, it defines a theoretical model (fault-tolerant Euclidean t-spanners) and analyzes algorithm complexity, rather than conducting empirical studies requiring public datasets for training.
Dataset Splits No This paper is theoretical and does not involve empirical experiments with data splits for training, validation, or testing.
Hardware Specification No This is a theoretical paper that does not report on empirical experiments, and therefore no hardware specifications are mentioned.
Software Dependencies No This is a theoretical paper and does not specify any software dependencies with version numbers needed for empirical implementation or replication.
Experiment Setup No This is a theoretical paper and does not include details on experimental setup such as hyperparameters or system-level training settings.