6,409 research outputs found
Ontology-based collaborative framework for disaster recovery scenarios
This paper aims at designing of adaptive framework for supporting
collaborative work of different actors in public safety and disaster recovery
missions. In such scenarios, firemen and robots interact to each other to reach
a common goal; firemen team is equipped with smart devices and robots team is
supplied with communication technologies, and should carry on specific tasks.
Here, reliable connection is mandatory to ensure the interaction between
actors. But wireless access network and communication resources are vulnerable
in the event of a sudden unexpected change in the environment. Also, the
continuous change in the mission requirements such as inclusion/exclusion of
new actor, changing the actor's priority and the limitations of smart devices
need to be monitored. To perform dynamically in such case, the presented
framework is based on a generic multi-level modeling approach that ensures
adaptation handled by semantic modeling. Automated self-configuration is driven
by rule-based reconfiguration policies through ontology
The Role of Ontologies in Disaster Recovery Planning
Disasters have the potential to cripple a country and those countries that are particularly susceptible to disasters must have adequate disaster recovery plans in place to ensure that the country can return to normalcy as soon as possible after the disaster. These plans will involve a number of autonomous entities that need to be able to communicate and coordinate their activities effectively. This paper describes how ontologies can be used to support this effective communication and coordination between entities and how they can help to ensure that the disaster recovery plan (DRP) is effective. The applicability of ontologies to DRPs will be demonstrated using a case study of an electric utility company in Jamaica, a country in the hurricane region
Computational environment for modeling and enhancing community resilience: Introducing the center for risk-based community resilience planning
The resilience of a community is defined as its ability to prepare for, withstand, recover from and adapt to the effects of natural or human-caused disasters, and depends on the performance of the built environment and on supporting social, economic and public institutions that are essential for immediate response and long-term recovery and adaptation. The performance of the built environment generally is governed by codes, standards, and regulations, which are applicable to individual facilities and residences, are based on different performance criteria, and do not account for the interdependence of buildings, transportation, utilities and other infrastructure sectors. The National Institute of Standards and Technology recently awarded a new Center of Excellence (NIST-CoE) for Risk-Based Community Resilience Planning, which is headquartered at Colorado State University and involves nine additional universities. Research in this Center is focusing on three major research thrusts: (1) developing the NIST-Community Resilience Modeling Environment known as NIST-CORE, thereby enabling alternative strategies to enhance community resilience to be measured quantitatively; (2) developing a standardized data ontology, robust data architecture and data management tools in support of NIST-CORE; and (3) performing a comprehensive set of hindcasts on disasters to validate the data architecture and NIST-CORE
Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)
This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio
Disaster Data Management in Cloud Environments
Facilitating decision-making in a vital discipline such as disaster management requires information gathering, sharing, and integration on a global scale and across governments, industries, communities, and academia. A large quantity of immensely heterogeneous disaster-related data is available; however, current data management solutions offer few or no integration capabilities and limited potential for collaboration. Moreover, recent advances in cloud computing, Big Data, and NoSQL have opened the door for new solutions in disaster data management.
In this thesis, a Knowledge as a Service (KaaS) framework is proposed for disaster cloud data management (Disaster-CDM) with the objectives of 1) facilitating information gathering and sharing, 2) storing large amounts of disaster-related data from diverse sources, and 3) facilitating search and supporting interoperability and integration. Data are stored in a cloud environment taking advantage of NoSQL data stores. The proposed framework is generic, but this thesis focuses on the disaster management domain and data formats commonly present in that domain, i.e., file-style formats such as PDF, text, MS Office files, and images. The framework component responsible for addressing simulation models is SimOnto. SimOnto, as proposed in this work, transforms domain simulation models into an ontology-based representation with the goal of facilitating integration with other data sources, supporting simulation model querying, and enabling rule and constraint validation.
Two case studies presented in this thesis illustrate the use of Disaster-CDM on the data collected during the Disaster Response Network Enabled Platform (DR-NEP) project. The first case study demonstrates Disaster-CDM integration capabilities by full-text search and querying services. In contrast to direct full-text search, Disaster-CDM full-text search also includes simulation model files as well as text contained in image files. Moreover, Disaster-CDM provides querying capabilities and this case study demonstrates how file-style data can be queried by taking advantage of a NoSQL document data store.
The second case study focuses on simulation models and uses SimOnto to transform proprietary simulation models into ontology-based models which are then stored in a graph database. This case study demonstrates Disaster-CDM benefits by showing how simulation models can be queried and how model compliance with rules and constraints can be validated
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Modelling Case-Based Reasoning in Situation-Aware Disaster Management
Situation-aware disaster management (SADIM) is a continuous decision-making and action taking process. This process requires prior knowledge of the ongoing environment and context. SADIM comprises two major processes: situation awareness (SA) - a cognitive process that assesses current situations and anticipate future situations in the environment; and, disaster management (DM) which is a decision-making process preventing, preparing, responding, and recovering for and from a disaster. One of the decision-making technologies used in current SADIM is case-based reasoning (CBR) CBR is used for the disaster management element only. Situation awareness process in current SADIM is carried out using domain rules, statistical reasoning and other methods. This paper therefore presents a method of using CBR to carry out both situation assessment and disaster management decision-making processes in SADIM, building on previous work focusing in SA alone. Using CBR for both processes provides the capability of using past experiences to understand the state of the environment and also solve specific disaster problems. The paper evaluates the method through implementation in disaster prevention in the petroleum drilling domain for early kick detection to prevent a blowout disaster. The results show a clear improvement in similarity assessment and problem solving prediction to prevent blowout
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