3,680 research outputs found
VELOS : a VR platform for ship-evacuation analysis
Virtual Environment for Life On Ships (VELOS) is a multi-user Virtual Reality (VR) system that aims to support designers to assess (early in the design process) passenger and crew activities on a ship for both normal and hectic conditions of operations and to improve ship design accordingly. This article focuses on presenting the novel features of VELOS related to both its VR and evacuation-specific functionalities. These features include: (i) capability of multiple users’ immersion and active participation in the evacuation process, (ii) real-time interactivity and capability for making on-the-fly alterations of environment events and crowd-behavior parameters, (iii) capability of agents and avatars to move continuously on decks, (iv) integrated framework for both the simplified and advanced method of analysis according to the IMO/MSC 1033 Circular, (v) enrichment of the ship geometrical model with a topological model suitable for evacuation analysis, (vi) efficient interfaces for the dynamic specification and handling of the required heterogeneous input data, and (vii) post-processing of the calculated agent trajectories for extracting useful information for the evacuation process. VELOS evacuation functionality is illustrated using three evacuation test cases for a ro–ro passenger ship
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
Virtual sensor networks: collaboration and resource sharing
This thesis contributes to the advancement of the Sensing as a Service (SeaaS),
based on cloud infrastructures, through the development of models and
algorithms that make an efficient use of both sensor and cloud resources while
reducing the delay associated with the data flow between cloud and client
sides, which results into a better quality of experience for users. The first models
and algorithms developed are suitable for the case of mashups being managed
at the client side, and then models and algorithms considering mashups
managed at the cloud were developed. This requires solving multiple problems:
i) clustering of compatible mashup elements; ii) allocation of devices
to clusters, meaning that a device will serve multiple applications/mashups;
iii) reduction of the amount of data flow between workplaces, and associated
delay, which depends on clustering, device allocation and placement of workplaces.
The developed strategies can be adopted by cloud service providers
wishing to improve the performance of their clouds.
Several steps towards an efficient Se-aaS business model were performed.
A mathematical model was development to assess the impact (of resource
allocations) on scalability, QoE and elasticity. Regarding the clustering of
mashup elements, a first mathematical model was developed for the selection
of the best pre-calculated clusters of mashup elements (virtual Things), and
then a second model is proposed for the best virtual Things to be built (non
pre-calculated clusters). Its evaluation is done through heuristic algorithms
having such model as a basis. Such models and algorithms were first developed
for the case of mashups managed at the client side, and after they
were extended for the case of mashups being managed at the cloud. For the
improvement of these last results, a mathematical programming optimization
model was developed that allows optimal clustering and resource allocation
solutions to be obtained. Although this is a computationally difficult
approach, the added value of this process is that the problem is rigorously
outlined, and such knowledge is used as a guide in the development of better
a heuristic algorithm.Esta tese contribui para o avanço tecnológico do modelo de Sensing as a Service
(Se-aaS), baseado em infraestrutura cloud, através do desenvolvimento
de modelos e algoritmos que resolvem o problema da alocação eficiente de
recursos, melhorando os métodos e técnicas atuais e reduzindo os tempos associados
`a transferência dos dados entre a cloud e os clientes, com o objetivo
de melhorar a qualidade da experiência dos seus utilizadores. Os primeiros
modelos e algoritmos desenvolvidos são adequados para o caso em que as
mashups são geridas pela aplicação cliente, e posteriormente foram desenvolvidos
modelos e algoritmos para o caso em que as mashups são geridas
pela cloud. Isto implica ter de resolver múltiplos problemas: i) Construção
de clusters de elementos de mashup compatíveis; ii) Atribuição de dispositivos
físicos aos clusters, acabando um dispositivo físico por servir m´ múltiplas
aplicações/mashups; iii) Redução da quantidade de transferência de dados
entre os diversos locais da cloud, e consequentes atrasos, o que dependente
dos clusters construídos, dos dispositivos atribuídos aos clusters e dos locais
da cloud escolhidos para realizar o processamento necessário. As diferentes
estratégias podem ser adotadas por fornecedores de serviço cloud que queiram
melhorar o desempenho dos seus serviços.(…
Allocation of resources in SAaaS Clouds managing thing mashups
The sensing and actuation as-a-service is an emerging business model to make sensors, actuators and data from the Internet of Things more attainable to everyday consumer. With the increase in the number of accessible Things, mashups can be created to combine services/data from one or multiple Things with services/data from virtual Web resources. These may involve complex tasks, with high computation requirements, and for this reason cloud infrastructures are envisaged as the most appropriate solution for storage and processing. This means that cloud-based services should be prepared to manage Thing mashups. Mashup management within the cloud allows not only the optimization of resources but also the reduction of the delay associated with data travel between client applications and the cloud. In this article, an optimization model is developed for the optimal allocation of resources in clouds under the sensing and actuation as-a-service paradigm. A heuristic algorithm is also proposed to quickly solve the problem.FCT (Foundation for Science and Technology) from Portugal within CEOT (Center for Electronic, Optoelectronic and Telecommunications) [UID/MULTI/00631/2020]info:eu-repo/semantics/publishedVersio
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