Survey of data management and analysis in disaster situations

Abstract-Cloud computing offers services which promise to meet continuously increasing computing demands by using a large number of networked resources. However, data heterogeneity remains a major hurdle for data interoperability and data integration. In this context, a Knowledge as a Service (KaaS) approach has been proposed with the aim of generating knowledge from heterogeneous data and making it available as a service. In this paper, a Collaborative Knowledge as a Service (CKaaS) architecture is proposed, with the objective of satisfying consumer knowledge needs by integrating disparate cloud knowledge through collaboration among distributed KaaS entities. The NIST cloud computing reference architecture is extended by adding a KaaS layer that integrates diverse sources of data stored in a cloud environment. CKaaS implementation is domain-specific; therefore, this paper presents its application to the disaster management domain. A use case demonstrates collaboration of knowledge providers and shows how CKaaS operates with simulation models.Keywords: cloud computing; data interoperability; data integration; data heterogeneity; knowledge as a service (KaaS)

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“…This agrees with the work of Hristidis et al (2010), who reported that most information is in PDF and MS Word files.…”

Section: Heterogeneous Data Sourcessupporting

confidence: 92%

Collaborative knowledge as a service applied to the disaster management domain

Grolinger

Mezghani

Capretz

et al. 2015

IJCC

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“…Some organizations (such as utilities, telecommunications, public sector, financial institutions, health care, etc.) have disaster recovery sites or, broadly speaking, a BCM due to legal responsibility; such disaster recovery sites need to be certified by international standards or national regulations [53]. Such international standards being related to IT disaster recovery include ISO, IEC and IUT, as summarized in Table 1 In some counties, disaster recovery sites are regulated by national government.…”

Section: International Standards Of Disaster Recoverymentioning

confidence: 99%

Key Determinant Derivations for Information Technology Disaster Recovery Site Selection by the Multi-Criterion Decision Making Method

2015

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Disaster recovery sites are an important mechanism in continuous IT system operations. Such mechanisms can sustain IT availability and reduce business losses during natural or human-made disasters. Concerning the cost and risk aspects, the IT disaster-recovery site selection problems are multi-criterion decision making (MCDM) problems in nature. For such problems, the decision aspects include the availability of the service, recovery time requirements, service performance, and more. The importance and complexities of IT disaster recovery sites increases with advances in IT and the categories of possible disasters. The modern IT disaster recovery site selection process requires further investigation. However, very few researchers tried to study related issues during past years based on the authors' extremely limited knowledge. Thus, this paper aims to derive the aspects and criteria for evaluating and selecting a modern IT disaster recovery site. A hybrid MCDM framework consisting of the Decision Making Trial and Evaluation Laboratory (DEMATEL) and the Analytic Network Process (ANP) will be proposed to construct the complex influence relations between aspects as well as criteria and further, derive weight associated with each aspect and criteria. The criteria with higher weight can be used for evaluating and selecting the most suitable IT disaster recovery sites. In the OPEN ACCESSSustainability 2015, 7 6150 future, the proposed analytic framework can be used for evaluating and selecting a disaster recovery site for data centers by public institutes or private firms.

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“…The reason why semantic-related technology is employed is that they can be used to identify and associate semantically-corresponding concepts with disasterrelated information so heterogeneous data can be integrated and ingested (Hristidis et al, 2010). Many previous studies have discussed the importance of semantic-related technology for solving problems in geographical information systems (Cohn, 1997;Guarino, 1998).…”

Section: Related Work On the Semantic Technology In Disaster Data Manmentioning

confidence: 99%

A Task-Driven Disaster Data Link Approach

Qiu

Zhu

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:With the rapid development of sensor networks and Earth observation technology, a large quantity of disaster-related data is available, such as remotely sensed data, historic data, cases data, simulation data, disaster products and so on. However, the efficiency of current data management and service systems has become increasingly serious due to the task variety and heterogeneous data. For emergency task-oriented applications, data searching mainly relies on artificial experience based on simple metadata index, whose high time-consuming and low accuracy cannot satisfy the requirements of disaster products on velocity and veracity. In this paper, a task-oriented linking method is proposed for efficient disaster data management and intelligent service, with the objectives of 1) putting forward ontologies of disaster task and data to unify the different semantics of multi-source information, 2) identifying the semantic mapping from emergency tasks to multiple sources on the basis of uniform description in 1), 3) linking task-related data automatically and calculating the degree of correlation between each data and a target task. The method breaks through traditional static management of disaster data and establishes a base for intelligent retrieval and active push of disaster information. The case study presented in this paper illustrates the use of the method with a flood emergency relief task.

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Survey of data management and analysis in disaster situations

Cited by 199 publications

References 50 publications

Collaborative knowledge as a service applied to the disaster management domain

Collaborative knowledge as a service applied to the disaster management domain

Key Determinant Derivations for Information Technology Disaster Recovery Site Selection by the Multi-Criterion Decision Making Method

A Task-Driven Disaster Data Link Approach

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