An Empirical Evaluation of Data Interoperability—A Case of the Disaster Management Sector in Uganda

Mazimwe, Allan; Hammouda, Imed; Gidudu, Anthony

doi:10.3390/ijgi8110484

Cited by 7 publications

(2 citation statements)

References 26 publications

(38 reference statements)

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“…In particular, big data potential can only be achieved if legal, organizational, semantic, and technical interoperability is reached [143]. In particular, some researchers report [144] that while technical interoperability has reached a high level of maturity, semantic and legal interoperability remains a significant barrier for the sector. Future work should be carried out to address semantic interoperability, taking into account existing standards, such as OASIS Emergency Data Exchange Language (EDXL) Emergency Standards [145], and semantic interoperability based on ontologies [146,147] to exploit the potential of disaster knowledge graphs [148].…”

Section: Discussionmentioning

confidence: 99%

A Big Data Reference Architecture for Emergency Management

2020

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Nowadays, we are witnessing a shift in the way emergencies are being managed. On the one hand, the availability of big data and the evolution of geographical information systems make it possible to manage and process large quantities of information that can hugely improve the decision-making process. On the other hand, digital humanitarianism has shown to be very beneficial for providing support during emergencies. Despite this, the full potential of combining automatic big data processing and digital humanitarianism approaches has not been fully realized, though there is an initial body of research. This paper aims to provide a reference architecture for emergency management that instantiates the NIST Big Data Reference Architecture to provide a common language and enable the comparison of solutions for solving similar problems.

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Section: Discussionmentioning

confidence: 99%

A Big Data Reference Architecture for Emergency Management

2020

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“…Hazard/disaster-related data that is already available is frequently geographically dispersed and stored by a variety of organisations, making it challenging to acquire and use for disaster management objectives. [22].…”

Section: 3mentioning

confidence: 99%

Critical success factors for geospatial data sharing in disaster management

Valachamy

Sahibuddin

Ahmad

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Globally, the rise of disasters has caused billions of dollars lost each year. These include the loss of properties, life and has created a negative impact on socioeconomic level of a country. Currently, geospatial datasets are becoming crucial for situational awareness and management of disasters. The timely and accurate information on disastrous occurrences must be collected, maintained, and managed for efficient management of emergency. These geospatial datasets are from different data provider agencies. Thus, there is a need to focus on the geospatial data sharing that would benefits the authorities in decision making. This initiative entails high commitment and collaboration from the data provider agencies, which can be achieved through the sharing of geospatial datasets approach. This study aims to identify the critical success factors of geospatial data sharing in the context of natural disaster. A preliminary review, focus group discussion and interviews were conducted to get insights of the subject being studied. The findings revealed that there are thirteen (13) critical success factors for geospatial data sharing in disaster management. Technology, Organisation, Social, Environment, Ecology and Economy are the dimensions identified and mapped accordingly to the thirteen critical success factors.

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A Pattern Driven Approach to Knowledge Representation in the Disaster Domain

et al. 2020

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Access to integrated disaster-related data through querying is still a problem due to associated semantic barriers. The disaster domain largely relies on the top-down approach of ontology development. This limits reuse due to associated commitments and complex alignments within ontologies. Therefore, there is a need to utilize a bottom-up approach that reuses patterns for representing disaster knowledge. To bridge the availability gap of patterns for representing disaster knowledge, this study identifies existing and emerging patterns for reuse while organizing disaster data from multiple sector stakeholders. Based on the eXtreme Design (XD) methodology and key informant interviews, competency questions (CQs) were elicited from domain stakeholders. The CQs are matched with existing patterns from other contexts. Emerging patterns (e.g the Event Classification and Quality Dependence Description for Objects) are also developed for CQs not captured and subsequently tested using SPARQL queries characterising the CQs. It is in this context that this paper presents a characterisation of disaster risk knowledge using CQs and corresponding patterns (reusable and emerging) covering the knowledge. Accordingly, we illustrate a pattern-driven use case to organise drought hazard data for early warning purposes. This provides a powerful use case for adopting a pattern-based approach to knowledge representation in the disaster domain.

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An Empirical Evaluation of Data Interoperability—A Case of the Disaster Management Sector in Uganda

Cited by 7 publications

References 26 publications

A Big Data Reference Architecture for Emergency Management

A Big Data Reference Architecture for Emergency Management

Critical success factors for geospatial data sharing in disaster management

A Pattern Driven Approach to Knowledge Representation in the Disaster Domain

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