Research topics
The Research Topics investigated by the members of the World Alliance on Digitalization for Disaster & Emergency Management, span over a broad range of domains in various disciplines. In the following, examples of research topics are given under the following classifications:
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Disaster and emergency management:
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Awareness, disaster and emergency detection and monitoring: Aims to detect the effects of disasters efficiently and effectively, such as the damages made, conditions of humans and living creatures, etc.
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Demand modeling and generation: Based on the observed damages and the environmental conditions, demand lists are automatically inferred for the necessary resources so that aid operations can be executed efficiently and effectively. Demands for resources can be various, including rescue operations, fire fighters, ambulances, security forces, repair teams, shelters, transport vehicles for evacuation, heating and cooking facilities, food and water, etc.
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Optimization and scheduling : The available resources are optimally assigned to the inferred demands on time. In case of insufficient resources, prioritization, trade-off and/or dynamic selection techniques are applied.
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Performance measurement: The performance indicators of operations are defined formally so that the desired objectives can be specified and measured. Accordingly, ongoing operations can be monitored online and in case of deviations from the desired performance values, corrective actions can be executed.
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Tracking and control: This phase includes both monitoring, evaluation and controlling actions of the ongoing activities as well as coordination among the aid operations.
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Simulation and game playing: To determine the effect of a large set of prospective disaster scenarios, determine the effectiveness and efficiency of the disaster and emergency ecosystem platform, evaluate the necessary quantity of resources and to optimize the locations of logistics centers, simulation environments and techniques must be researched, designed and implemented. Even in times when no disasters are experienced, triggered by the simulated disaster scenarios, the platform must be continuously operational so that it can be optimized using online machine learning techniques.
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​​Computation: Realization of the disaster and emergency management system is not trivial and requires research in various
computer science disciplines: For example:
Event-based computation, Digital twins, Communication networks, Coordination, Platforms and Software Engineering Aspects.
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Sociological disaster research: It is the of the social relations amongst both natural and human-made disasters.
Software engineering aspects
Disaster and emergency management systems are long-living software-intensive systems constrainted with demands for high quality attributes such as security, availability, timeliness and correctneess. Software engineering as a discpline concerns with providing desired software qualities according to the requirements. Examples of research topics are:
Requirement engineering from multiple stakeholder viewpoints
Methods for user-centered design and implementation
Software development methods for the effective use of techniques such as frameworks, model-driven approaches, product-line architectures, systems of systems, and dynamically optimizing platforms through data collection and machine learning
Techniques for building disaster and emergency resilient systems
Support for fixed and mobile platforms
Design and development environments, for example, sensor and data fusion analysis and design environments, and environments for supporting different phases of disaster and emergency management including system simulation and game building.
Models and languages suitable for a large category of devices from IoT nodes, platform realizations, big-data systems to third party applications
Domain-specific languages for disaster and emergency management
Ontology-languages and integration of multi-paradigm language approaches
Event-based, aspect-oriented, and context-oriented approaches
Specifying, implementing, and testing the desired quality attributes of software and systems, including reduced complexity and enhanced reusability through modularization, assuring critical system parameters such as correctness, fault-tolerance, security, and performance.
Techniques for context-aware, such as energy and disaster and emergency aware systems
Support for software modularization and architecture design for enhancing quality attributes.
Verification and testing techniques and their combinations such as run-time verification and model-based verification and testing techniques for functional and critical parameters.
Verification of dynamic modules, plug-ins, application modules, etc.
Support of model-based and data-based approaches. Improving software models through machine learning techniques
Uncertainty management through fuzzy-probabilistic techniques and machine learning