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 [1].
Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases
Authors: John Grant, Maria Vanina Martinez, Cristian Molinaro, Francesco Parisi
JAIR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we define and investigate new measures that are particularly suitable for dealing with inconsistent spatio-temporal information... We then analyze their behavior w.r.t. common postulates defined for classical propositional knowledge bases... Finally, we study the complexity of the proposed inconsistency measures. |
| Researcher Affiliation | Academia | John Grant EMAIL Department of Computer Science and UMIACS University of Maryland, College Park, USA Maria Vanina Martinez EMAIL Institute for Research in Computer Science (UBA-CONICET) Department of Computer Science, FCEy N University of Buenos Aires, Argentina Cristian Molinaro EMAIL Francesco Parisi EMAIL Department of Informatics, Modeling, Electronics and System Engineering University of Calabria, Italy |
| Pseudocode | Yes | Algorithm 1 Compute the IOT measure Input: An ST database S. Output: IOT (S). |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code for the methodology described. It mentions prior work using the SPOT framework on real US Navy databases but not the code for the current paper's contributions. |
| Open Datasets | No | The SPOT framework has been implemented and tested on real US Navy databases containing ship location data by Parker et al. (2009) and Parisi et al. (2010), where probability intervals were added to the original data in order to model spatio-temporal uncertainty by means of consistent SPOT databases, but the original data were in the form of an (inconsistent) ST database. (This refers to prior work's data, not data directly made available or used for experiments in this paper). Example 1. Consider a farm where the tasks of irrigation and fertilization are performed automatically by means of drones. (This is a conceptual example, not a dataset). |
| Dataset Splits | No | The paper does not describe any empirical experiments involving dataset splits. Its focus is on theoretical definitions, postulate analysis, and complexity characterization of inconsistency measures. |
| Hardware Specification | No | The paper does not provide specific hardware details for running experiments. The research is primarily theoretical, focusing on logical frameworks and complexity analysis. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. The work is theoretical, describing algorithms and measures without detailing an implementation environment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details, hyperparameters, or training configurations. The research is theoretical, focusing on mathematical definitions and complexity analysis. |