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..
Hypothetical Answers to Continuous Queries over Data Streams
Authors: Luís Cruz-Filipe, Isabel Nunes, Graça Gaspar2798-2805
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a declarative semantics for queries in Temporal Datalog, where we define the notions of hypothetical and supported answers. We define an operational semantics based on SLD-resolution, and show that there is a natural connection between the answers computed by this semantics and hypothetical and supported answers. By refining SLD-resolution, we obtain an online algorithm for maintaining and updating the set of answers that are consistent with the currently available information. Finally, we show that our results extend to a language with negation. |
| Researcher Affiliation | Academia | Lu ıs Cruz-Filipe Dept. of Mathematics and Computer Science, University of Southern Denmark Grac a Gaspar, Isabel Nunes University of Lisbon, Faculty of Sciences, Bio ISI Biosystems & Integrative Sciences Institute, Portugal |
| Pseudocode | No | The paper describes algorithmic steps in numbered text paragraphs (e.g., 'The following algorithm computes Sτ+1 from PQ and Sτ in time polynomial...'), but does not present them in a formal pseudocode block or algorithm environment. |
| Open Source Code | No | The paper does not contain any statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper uses illustrative examples with abstract data facts (e.g., Temp(wt25, high, 0)), but does not refer to any specific publicly available dataset with concrete access information or citations. |
| Dataset Splits | No | The paper is theoretical and uses illustrative examples rather than empirical experiments, so there is no mention of training/test/validation dataset splits. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for computations or experiments. |
| Software Dependencies | No | The paper refers to logical frameworks like 'Temporal Datalog' and 'SLD-resolution' but does not specify any software dependencies with version numbers used for implementation or testing. |
| Experiment Setup | No | The paper is theoretical and does not present empirical experiments, therefore no experimental setup details or hyperparameters are provided. |