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Architecture for the heterogeneous federation of Future Internet experimentation facilities
TLDR
An architecture to support a federation of Future Internet experimentation facilities, based on use cases and requirements from infrastructure owners, as well as services and first line support communities is presented. Expand
Data Models and Query Languages for Linked Geospatial Data
TLDR
Recent advances in geospatial query processing and reasoning are surveyed, concentrating on issues of data modeling and querying. Expand
Pushing the Envelope in Graph Compression
TLDR
The analysis and experimental evaluation of the improved state-of-the-art method for the compression of web and other similar graphs shows that it outperforms the currently best method of Boldi et al. by achieving a better compression ratio and retrieval time. Expand
Semantic Referee: A Neural-Symbolic Framework for Enhancing Geospatial Semantic Segmentation
TLDR
A semantic referee is proposed, which is able to extract qualitative features of the errors emerging from deep machine learning frameworks and suggest corrections and how to improve the performance of semantic segmentation for satellite imagery data. Expand
Consistency of Chordal RCC-8 Networks
TLDR
Chordal RCC-8 networks are considered and it is shown that they can be checked for consistency by enforcing partial path consistency with weak composition, and that it can be solved very efficiently with PyRCC∇ by making its underlying constraint graph chordal and running path consistency on this sparse graph instead of the completion of the given network. Expand
Efficient Approach to Solve the Minimal Labeling Problem of Temporal and Spatial Qualitative Constraints
TLDR
This paper focuses on the minimal labeling problem (MLP) and proposes an algorithm to efficiently derive all the feasible base relations of a QCN and considers chordal QCNs and a new form of partial consistency which is defined as G♦-consistency. Expand
An Efficient Approach for Tackling Large Real World Qualitative Spatial Networks
TLDR
This work proposes an implementation scheme that triangulates the constraint graphs of the input networks and uses a hash table based adjacency list to efficiently represent and reason with them and generates random scale-free-like qualitative spatial networks using the Barabasi-Albert model with a preferential attachment mechanism. Expand
Tackling Large Qualitative Spatial Networks of Scale-Free-Like Structure
TLDR
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 and establishes the implementation as the only possible solution to date to reason with large scale-free-like qualitative spatial networks efficiently. Expand
Efficiently Characterizing Non-Redundant Constraints in Large Real World Qualitative Spatial Networks
TLDR
An algorithm based on ⋄G-consistency to compute the unique prime network of a RCC8 network is devised, and it significantly progresses the state-of-the-art for practical reasoning with real R CC8 networks scaling up to millions of nodes. Expand
Efficiently Reasoning about Qualitative Constraints through Variable Elimination
TLDR
A novel algorithm is introduced, study, and evaluate, that is based on the idea of variable elimination, a simple and general exact inference approach in probabilistic graphical models, that enforces a particular directional local consistency on N, which is denote by ←-consistency. Expand
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