Using big data to enhance crisis response and disaster resilience for a smart city

Yang, Chuanjie; Su, Guofeng; Chen, Jianguo

doi:10.1109/icbda.2017.8078684

Cited by 48 publications

(35 citation statements)

References 5 publications

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“…A variety of datasets from smart buildings, city pollution, traffic simulators, and social media such as Twitter are utilized for the validation and evaluation of the system to detect and generate alerts for a fire in a building, pollution level in the city, emergency evacuation path, and the collection of information about natural disasters such as earthquakes and tsunamis. Furthermore, Yang et al [25] proposed real-time feedback loops on nature disasters to help real estate and city decision-makers make real-time updates, along with a precision and dynamic rescue plan that helps in in all four phases of disaster risk management: prevention, mitigation, response, and recovery; this can help the city and real estate planners and managers to take prompt and accurate actions to improve the city's resilience to disasters. disasters to help real estate and city decision-makers make real-time updates, along with a precision and dynamic rescue plan that helps in in all four phases of disaster risk management: prevention, mitigation, response, and recovery; this can help the city and real estate planners and managers to take prompt and accurate actions to improve the city's resilience to disasters.…”

Section: Big Data Applications For Disaster and Risk Managementmentioning

confidence: 99%

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Munawar

Qayyum

Ullah

et al. 2020

BDCC

115

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Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.

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Section: Big Data Applications For Disaster and Risk Managementmentioning

confidence: 99%

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Munawar

Qayyum

Ullah

et al. 2020

BDCC

115

View full text Add to dashboard Cite

show abstract

“…The frequency of natural disasters in the Philippines increased by 147% from 1980 to 2012 and continues to rise (Garcia and Hernandez, 2017). Big Data through data mining plays a significant role in creating real-time feedback loops on natural disasters to support disaster management in prevention, protection, mitigation processes as well as response and recovery, moreover, in increasing the resilience of citizens (Yang et al, 2017).…”

Section: Biodiversitymentioning

confidence: 99%

The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking

Sebestyén

Czvetkó

Abonyi

2021

Front. Environ. Sci.

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The aim of this paper is to provide an overview of the interrelationship between data science and climate studies, as well as describes how sustainability climate issues can be managed using the Big Data tools. Climate-related Big Data articles are analyzed and categorized, which revealed the increasing number of applications of data-driven solutions in specific areas, however, broad integrative analyses are gaining less of a focus. Our major objective is to highlight the potential in the System of Systems (SoS) theorem, as the synergies between diverse disciplines and research ideas must be explored to gain a comprehensive overview of the issue. Data and systems science enables a large amount of heterogeneous data to be integrated and simulation models developed, while considering socio-environmental interrelations in parallel. The improved knowledge integration offered by the System of Systems thinking or climate computing has been demonstrated by analysing the possible inter-linkages of the latest Big Data application papers. The analysis highlights how data and models focusing on the specific areas of sustainability can be bridged to study the complex problems of climate change.

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“…There have been many studies recently on utilizing datadriven approaches for improving operations of different aspects of smart cities. Examples include general city management [30], urban water management [31], public transportation management [32], vehicular network improvements [33], rail transit safety [34], crisis response and disaster resilience [35], communication performance management [36], load forecasting in buildings [37], energy management [38] [39] [40], and in general city decisionmaking processes [41] [42]. These studies show the advantages of using data-driven approaches for smart cities.…”

Section: Related Workmentioning

confidence: 99%

Data-Driven Security for Smart City Systems: Carving a Trail

et al. 2020

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Smart cities rely heavily on collecting and using data. Smart systems are implemented and deployed to provide intelligent features that help improve efficiency and quality of life. This creates a huge repository of data representing many aspects of smart city operations. Many data-driven applications can take advantage of this data to further improve the "smartness" of a smart city. At the same time, smart city systems, being very large-scale distributed systems and highly integrated with the physical infrastructure and residents of the city, pose immense security challenges as well. So why don't we take advantage of this data to improve security measures? In this paper we propose the use of data-driven security approaches to secure smart city systems. To illustrate the significance of this approach we first identify the different challenges for securing smart city systems given the unique characteristics of these systems. Then we discuss the benefits of using data-driven security. Furthermore, we categorize the different types of security applications (features) needed to help capitalize on the data needs and benefits. We also discuss the how these categories of applications can alleviate some of the challenges. In addition, we highlight possible future research directions to incorporate effective data-driven security in smart city systems.

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Using big data to enhance crisis response and disaster resilience for a smart city

Cited by 48 publications

References 5 publications

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking

Data-Driven Security for Smart City Systems: Carving a Trail

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