Full Paper: Digital Resilience in Critical Infrastructures: A Systematic Literature Review

In times of disruptive events, effective response by organizations, critical systems, and society is paramount. The response process involves pre-event preparation, impact absorption, and system restoration, which together represent the concept of resilience. Critical infrastructures (CI) are essential to the functioning of society and require a high level of resilience to ensure that they can withstand and quickly recover from disruptive events. With the incorporation of Information Systems (IS) into CI, there is a need to study Digital Resilience to identify potential risks and develop strategies to mitigate them effectively. In this research, we conducted a Systematic Literature Review on Digital Resilience to understand its scope, and classified articles based on their scope, resilience dimensions, and phases they address, as well as interdependence between systems. We aim to contribute to the scientific understanding of Digital Resilience by analyzing existing gaps and proposing possible future research directions. This study provides an overview of the current state-of-the-art, the types of research conducted, and the resulting artifacts. Additionally, it introduces a new area of focus within the field of resilience: Digital Resilience

Visual Analytics Methods for Exploring Geographically Networked Phenomena

abstract: The connections between different entities define different kinds of networks, and many such networked phenomena are influenced by their underlying geographical relationships. By integrating network and geospatial analysis, the goal is to extract information about interaction topologies and the relationships to related geographical constructs. In the recent decades, much work has been done analyzing the dynamics of spatial networks; however, many challenges still remain in this field. First, the development of social media and transportation technologies has greatly reshaped the typologies of communications between different geographical regions. Second, the distance metrics used in spatial analysis should also be enriched with the underlying network information to develop accurate models. Visual analytics provides methods for data exploration, pattern recognition, and knowledge discovery. However, despite the long history of geovisualizations and network visual analytics, little work has been done to develop visual analytics tools that focus specifically on geographically networked phenomena. This thesis develops a variety of visualization methods to present data values and geospatial network relationships, which enables users to interactively explore the data. Users can investigate the connections in both virtual networks and geospatial networks and the underlying geographical context can be used to improve knowledge discovery. The focus of this thesis is on social media analysis and geographical hotspots optimization. A framework is proposed for social network analysis to unveil the links between social media interactions and their underlying networked geospatial phenomena. This will be combined with a novel hotspot approach to improve hotspot identification and boundary detection with the networks extracted from urban infrastructure. Several real world problems have been analyzed using the proposed visual analytics frameworks. The primary studies and experiments show that visual analytics methods can help analysts explore such data from multiple perspectives and help the knowledge discovery process.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Advancing Quantitative Risk Analysis for Critical Water Infrastructure

Critical infrastructure systems play a vital role in the supply of lifeline services to businesses and the wider public. It is of paramount importance for national security, public health, and economic prosperity that these critical structures function properly. Unfortunately, with respect to drinking water infrastructures in the US, much of the pipeline assets are nearing the end of their useful life and utilities are challenged with maintaining these systems with limited budgets and information. Risk analysis is a useful decision making tool which can allow managers to better identify weaknesses, and aid better investment decisions regarding maintenance, inspection, and repair. The current practice for risk analysis and management of critical water systems falls short of the approaches preferred by risk researchers. The aim of this thesis is to advance to practice and theory. This involves the evaluation of existing methods as well as the incorporation of modern analytical tools to fundamentally advance the state of practice. This thesis first critically analyzes a popular risk analysis standard (J100-10) to establish the knowledge gap between practice and theory in the water domain. Two quantitative methodologies are then explored: machine learning and mathematical optimization. The research here demonstrates how they can be integrated into a broader risk framework and used to improve assessments for water systems. The work presented in this dissertation represents a significant contribution to the field of infrastructure risk and reliability analysis. While the domain application is specific to drinking water systems, the techniques can be applied for other types of networked infrastructures.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153423/1/tyjchen_1.pd