Big data and IoT-based applications in smart environments: A systematic review

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.

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

Disaster management is a growing concern and priority throughout the world and “resilience” is increasingly viewed as a key capacity related to disaster and post-disaster management and development. Recent research highlights how resilience is enhanced through the use of “big data” technologies that improve the speed and effectiveness of linkages between disaster information and systemic response. Summarizing and discussing this research, this study highlights and substantiates the potential of big data strategies to help mitigate the risks and impact of socio-ecological vulnerability. Based on a qualitative desk review and analyses of secondary data, resilience is framed as a function of the adaptive, absorptive and transformative capacity of socio-political systems to withstand and cope with the adverse effects of disaster. In addition, this study emphasizes the major principles and components of effective big data use; e.g., open source tools, strong infrastructure, local skill development, context-specific data sources, ethical data sharing and experiential learning. This study reveals some important big data technologies that can be easily used in the different phases of disaster management and enhancing resilience such as remote sensing imagery, social media data, crowdsourced data, geographic information system (GIS), and mobile metadata. The findings hold major relevancy for policymakers, administrators, and related stakeholders responsible for taking action before, during and after disasters through training, early warning systems, emergency evacuation, relief distribution and other key infrastructural components.

Disaster resilience through big data: Way to environmental sustainability

Towards resilient and smart cities: A real-time urban analytical and geo-visual system for social media streaming data

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

High population growth, urbanization, and global climate change drive up the frequency of disasters, causing grave losses of people's lives and property worldwide. Additionally, globalization, technological development, and the changing roles of individuals in society will require entirely new approaches, tools, and capabilities to help inform decision making under uncertain conditions. However, the mismatch between the high disaster vulnerability and the low crisis response and disaster resilience becomes a critical problem for emergency management. Recent years, key advances in computing — smartphones, worldwide mobile internet access, social media and industrial big data have all contributed to break through barriers of information exchange which help disaster managers working on data-driven solutions to disaster management problems. Based on big data, the emergency managers can identify and assesse risk through critical infrastructure operating data or sensor data, and then predict the affected population through smartphone data or social media data, and finally provide an operation plan for establishing the target capabilities for a community to response to the crisis. Also, big data plays a part in providing real-time clues of on-site disaster information through data mining. Based on the analyzed real-time disaster information, the research creates real-time feedback loops on nature disaster to help the decision-makers make the real-time update, precision and dynamic rescue plan. In combination, big data can help in all four phases of disaster management: prevention, protection, mitigation, response, and recovery, and can also help us take actions to improve our city's resilience to disasters.

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

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