Distance-Bounded Consistent Query Answering
Authors: Andreas Pfandler, Emanuel Sallinger
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | 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. |