Social Vulnerability and How It Matters: A Bibliometric Analysis

Interdisciplinary and cross-cultural studies of the impacts of environment and social vulnerability must be undertaken to address the problem of social vulnerability in the foreseeable future. Scientist or social scientists should first continuously strive towards approaches can integrate municipal technological expertise, experiences, knowledge, perceptions, and expectations into emergency circumstances, so that people can be sharper on issues and offer responses with their matters. In this paper. We performing the Bibliometric Analysis to review published papers on the keyword 'Social Vulnerability'. There are 29,468 papers published in the last 52 years from 1969 to November 2020. Disaster research by implementing the Internet of Things (IoT), data mining, machine learning is still needed

From Disaster Response Planning to e-Resilience: A Literature Review

Natural and man-made crises as well as IT-security issues foster the interest in robust and resilient business information systems. Information and Communication Technologies (ICT) are essential for successful e-business. If ICT technologies interrupt, the whole (e-) business continuity is threatened. ICT interruptions causing serious loss in organization’s reputation, trust and revenues. This circumstance should increase manager’s interest in the concepts of disaster recovery planning (DRP), business continuity management (BCM) and, the emerging imperative, resilience. This paper at hand presents the results of a database driven literature review on these concepts and its interrelation

BIG DATA APPLICATIONS AND CHALLENGES IN GISCIENCE (CASE STUDIES: NATURAL DISASTER AND PUBLIC HEALTH CRISIS MANAGEMENT)

This dissertation examines the application and significance of user-generated big data in Geographic Information Science (GIScience), with a focus on managing natural disasters and public health crises. It explores the role of social media data in understanding human-environment interactions and in informing disaster management and public health strategies. A scalable computational framework will be developed to model extensive unstructured geotagged data from social media, facilitating systematic spatiotemporal data analysis.The research investigates how individuals and communities respond to high-impact events like natural disasters and public health emergencies, employing both qualitative and quantitative methods. In particular, it assesses the impact of socio-economic-demographic characteristics and the digital divide on social media engagement during such crises. In addressing the opioid crisis, the dissertation delves into the spatial dynamics of opioid overdose deaths, utilizing Multiscale Geographically Weighted Regression to discern local versus broader-scale determinants. This analysis foregrounds the necessity for targeted public health responses and the importance of localized data in crafting effective interventions, especially within communities that are ethnically diverse and economically disparate. Using Hurricane Irma as a case study, this dissertation analyzes social media activity in Florida in September 2017, leveraging Multiscale Geographically Weighted Regression to explore spatial variations in social media discourse, its correlation with damage severity, and the disproportionate impact on racialized communities. It integrates social media data analysis with political-ecological perspectives and spatial analytical techniques to reveal structural inequalities and political power differentials. The dissertation also tackles the dissemination of false information during the COVID-19 pandemic, examining Twitter activity in the United States from April to July 2020. It identifies misinformation patterns, their origins, and their association with the pandemic\u27s incidence rates. Discourse analysis pinpoints tweets that downplay the pandemic\u27s severity or spread disinformation, while spatial modeling investigates the relationship between social media discourse and disease spread. By concentrating on the experiences of racialized communities, this research aims to highlight and address the environmental and social injustices they face. It contributes empirical and methodological insights into effective policy formulation, with an emphasis on equitable responses to public health emergencies and natural disasters. This dissertation not only provides a nuanced understanding of crisis responses but also advances GIScience research by incorporating social media data into both traditional and critical analytical frameworks

Large Scale Data Mining for IT Service Management

More than ever, businesses heavily rely on IT service delivery to meet their current and frequently changing business requirements. Optimizing the quality of service delivery improves customer satisfaction and continues to be a critical driver for business growth. The routine maintenance procedure plays a key function in IT service management, which typically involves problem detection, determination and resolution for the service infrastructure. Many IT Service Providers adopt partial automation for incident diagnosis and resolution where the operation of the system administrators and automation operation are intertwined. Often the system administrators\u27 roles are limited to helping triage tickets to the processing teams for problem resolving. The processing teams are responsible to perform a complex root cause analysis, providing the system statistics, event and ticket data. A large scale of system statistics, event and ticket data aggravate the burden of problem diagnosis on both the system administrators and the processing teams during routine maintenance procedures. Alleviating human efforts involved in IT service management dictates intelligent and efficient solutions to maximize the automation of routine maintenance procedures. Three research directions are identified and considered to be helpful for IT service management optimization: (1) Automatically determine problem categories according to the symptom description in a ticket; (2) Intelligently discover interesting temporal patterns from system events; (3) Instantly identify temporal dependencies among system performance statistics data. Provided with ticket, event, and system performance statistics data, the three directions can be effectively addressed with a data-driven solution. The quality of IT service delivery can be improved in an efficient and effective way. The dissertation addresses the research topics outlined above. Concretely, we design and develop data-driven solutions to help system administrators better manage the system and alleviate the human efforts involved in IT Service management, including (1) a knowledge guided hierarchical multi-label classification method for IT problem category determination based on both the symptom description in a ticket and the domain knowledge from the system administrators; (2) an efficient expectation maximization approach for temporal event pattern discovery based on a parametric model; (3) an online inference on time-varying temporal dependency discovery from large-scale time series data