38,286 research outputs found
Rewriting Abstract Structures: Materialization Explained Categorically
The paper develops an abstract (over-approximating) semantics for
double-pushout rewriting of graphs and graph-like objects. The focus is on the
so-called materialization of left-hand sides from abstract graphs, a central
concept in previous work. The first contribution is an accessible, general
explanation of how materializations arise from universal properties and
categorical constructions, in particular partial map classifiers, in a topos.
Second, we introduce an extension by enriching objects with annotations and
give a precise characterization of strongest post-conditions, which are
effectively computable under certain assumptions
Failure dynamics of the global risk network
Risks threatening modern societies form an intricately interconnected network
that often underlies crisis situations. Yet, little is known about how risk
materializations in distinct domains influence each other. Here we present an
approach in which expert assessments of risks likelihoods and influence
underlie a quantitative model of the global risk network dynamics. The modeled
risks range from environmental to economic and technological and include
difficult to quantify risks, such as geo-political or social. Using the maximum
likelihood estimation, we find the optimal model parameters and demonstrate
that the model including network effects significantly outperforms the others,
uncovering full value of the expert collected data. We analyze the model
dynamics and study its resilience and stability. Our findings include such risk
properties as contagion potential, persistence, roles in cascades of failures
and the identity of risks most detrimental to system stability. The model
provides quantitative means for measuring the adverse effects of risk
interdependence and the materialization of risks in the network
Hierarchical D ∗ algorithm with materialization of costs for robot path planning
In this paper a new hierarchical extension of the D
∗ algorithm for robot path planning is introduced. The hierarchical D
∗
algorithm uses a down-top strategy and a set of precalculated paths (materialization of path costs) in order to improve performance.
This on-line path planning algorithm allows optimality and specially lower computational time. H-Graphs (hierarchical graphs)
are modified and adapted to support on-line path planning with materialization of costs and multiple hierarchical levels. Traditional
on-line robot path planning focused in horizontal spaces is also extended to vertical and interbuilding spaces. Some experimental
results are showed and compared to other path planning algorithms
LiteMat: a scalable, cost-efficient inference encoding scheme for large RDF graphs
The number of linked data sources and the size of the linked open data graph
keep growing every day. As a consequence, semantic RDF services are more and
more confronted with various "big data" problems. Query processing in the
presence of inferences is one them. For instance, to complete the answer set of
SPARQL queries, RDF database systems evaluate semantic RDFS relationships
(subPropertyOf, subClassOf) through time-consuming query rewriting algorithms
or space-consuming data materialization solutions. To reduce the memory
footprint and ease the exchange of large datasets, these systems generally
apply a dictionary approach for compressing triple data sizes by replacing
resource identifiers (IRIs), blank nodes and literals with integer values. In
this article, we present a structured resource identification scheme using a
clever encoding of concepts and property hierarchies for efficiently evaluating
the main common RDFS entailment rules while minimizing triple materialization
and query rewriting. We will show how this encoding can be computed by a
scalable parallel algorithm and directly be implemented over the Apache Spark
framework. The efficiency of our encoding scheme is emphasized by an evaluation
conducted over both synthetic and real world datasets.Comment: 8 pages, 1 figur
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