# Tackling Large Qualitative Spatial Networks of Scale-Free-Like Structure

@inproceedings{Sioutis2014TacklingLQ, title={Tackling Large Qualitative Spatial Networks of Scale-Free-Like Structure}, author={Michael Sioutis and Jean-François Condotta}, booktitle={SETN}, year={2014} }

We improve the state-of-the-art method for checking the consistency of large qualitative spatial networks that appear in the Web of Data by exploiting the scale-free-like structure observed in their underlying graphs. We propose an implementation scheme that triangulates the underlying graphs of the input networks and uses a hash table based adjacency list to efficiently represent and reason with them. We generate random scale-free-like qualitative spatial networks using the Barabasi-Albert (BA… Expand

#### 17 Citations

Triangulation Versus Graph Partitioning for Tackling Large Real World Qualitative Spatial Networks

- Computer Science
- 2014 IEEE 26th International Conference on Tools with Artificial Intelligence
- 2014

A clear view is established on the state-of-the-art solutions for reasoning with large real world qualitative spatial networks efficiently by properly re-evaluating them with the benchmark dataset of that paper. Expand

Studying the use and effect of graph decomposition in qualitative spatial and temporal reasoning

- Computer Science
- The Knowledge Engineering Review
- 2017

It is proved that a recently proposed decomposition-based approach for checking the satisfiability of qualitative spatial constraint networks lacks soundness, and a particular tree decomposition is presented that allows for cost-free utilization of parallelism for a qualitative constraint language that has patchwork for satisfiable atomic networks. Expand

A Simple Decomposition Scheme for Large Real World Qualitative Constraint Networks

- Computer Science
- FLAIRS Conference
- 2015

A simple decomposition scheme is proposed that retrieves the smaller QCNs that correspond to the biconnected component subgraphs of the underlying graph of a given large QCN, and it is shown that this approach is sound for a qualitative constraint language that has a particular constraint property for atomic QCNs, namely, patchwork. Expand

Large scale distributed spatio-temporal reasoning using real-world knowledge graphs

- Computer Science
- Knowl. Based Syst.
- 2019

ParQR is described, a parallel, distributed implementation of QSTR techniques that addresses the challenge of reasoning over large-scale qualitative spatial and temporal datasets and shows that it scales effectively, is able to handle constraint networks consisting of millions of relations, and outperforms current distributed implementations ofQSTR. Expand

A Practical Approach for Maximizing Satisfiability in Qualitative Spatial and Temporal Constraint Networks

- Computer Science
- 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI)
- 2015

This work introduces and studies the problem of obtaining a spatial or temporal configuration that maximizes the number of constraints satisfied in a qualitative constraint network (QCN), and proposes a complete generic branch and bound algorithm for solving the MAX-QCN problem. Expand

On the use and effect of graph decomposition in qualitative spatial and temporal reasoning

- Computer Science
- SAC
- 2015

It is proved that a recently proposed decomposition-based approach that was presented in [AAAI, 2014] for checking the satisfiability of qualitative spatial constraint networks lacks soundness. Expand

On Redundancy in Linked Geospatial Data

- Computer Science
- LDQ@ESWC
- 2015

This paper presents a practical approach for obtaining the prime networks of RCC8 networks that originate from the Semantic Web, by exploiting the sparse and loosely connected structure of their constraint graphs, and contributes towards offering Linked Geospatial Data of high quality. Expand

Fast learning of scale‐free networks based on Cholesky factorization

- Computer Science
- Int. J. Intell. Syst.
- 2018

Evaluation on synthetically generated examples and high‐impact applications from a biomedical domain of up to more than 900,000 variables provides evidence that for such tasks the SNETCH algorithm can learn the underlying structure more accurately, and an order of magnitude faster than state‐of‐the‐art approaches based on the L1 penalized precision matrix. Expand

Incrementally Building Partially Path Consistent Qualitative Constraint Networks

- Computer Science
- AIMSA
- 2014

A framework to compute the path consistency of a given Qualitative Constraint Network (QCN) and propose an algorithm for maintaining or enforcing partial path consistency for growing constraint networks, i.e., networks that grow with new temporal or spatial entities over time. Expand

Qualitatively correct bintrees: an efficient representation of qualitative spatial information

- Computer Science
- GeoInformatica
- 2019

It is proved that such correct bintrees always exist and that they can be constructed as a sum of local representations, allowing a practically efficient construction. Expand

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