Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Distance-Bounded Consistent Query Answering
Authors: Andreas Pfandler, Emanuel Sallinger
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work we present a new approach where this distance is bounded and analyze its computational complexity. Our results show that in many (but not all) cases the complexity drops. |
| Researcher Affiliation | Academia | 1Vienna University of Technology, Austria 2University of Siegen, Germany |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical, analyzing computational complexity, and does not involve empirical training on datasets. Therefore, no information about publicly available training datasets is provided. |
| Dataset Splits | No | The paper is theoretical, focusing on computational complexity analysis rather than empirical evaluation with datasets. Thus, no dataset split information (training, validation, test) is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any empirical experiments that would require specific software dependencies with version numbers for replication. |
| Experiment Setup | No | The paper is theoretical and focuses on computational complexity analysis. It does not describe any empirical experimental setup, hyperparameters, or training configurations. |