Most Probable Explanations for Probabilistic Database Queries

Authors: İsmail İlkan Ceylan, Stefan Borgwardt, Thomas Lukasiewicz

IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical The focus of this paper was to determine the precise complexity of these problems, and we provided a detailed analysis for these problems relative to a variety of query languages, ranging from conjunctive queries to ontology-mediated queries. We study the computational complexity of the corresponding decision problems, denoted by MPD and MPH, respectively, for a variety of query languages. Our results provide detailed insights about the nature of these problems. We show that the data complexity of both problems is lower for existential queries than for universal queries. As expected, MPH usually has a higher complexity than MPD.
Researcher Affiliation Academia Ismail Ilkan Ceylan and Stefan Borgwardt Faculty of Computer Science Technische Universit at Dresden, Germany ... Thomas Lukasiewicz Department of Computer Science University of Oxford, UK
Pseudocode No The paper does not contain any pseudocode or clearly labeled algorithm blocks.
Open Source Code No The paper does not provide concrete access to source code for the methodology described. It only mentions 'Detailed proofs of all results can be found at https://lat.inf.tu-dresden.de/research/papers.html', which refers to proofs, not code.
Open Datasets No The paper is theoretical and does not conduct experiments involving real datasets. It uses example probabilistic databases for illustrative purposes (e.g., 'Consider the PDB Pv given in Figure 1'), but does not use them as training data or provide access information for public datasets.
Dataset Splits No The paper is theoretical and does not describe experiments involving data splits (training, validation, or testing).
Hardware Specification No The paper is theoretical and does not describe any experimental setup or the specific hardware used to run experiments.
Software Dependencies No The paper is theoretical and does not describe any experimental setup or specific software dependencies with version numbers.
Experiment Setup No The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations.