Graphs are increasingly adopted to model and handle complex systems, but their processing is known to be resource-intensive due to the complexity of the algorithms and the size of the graphs. This hindrance limits their usage for those who do not possess sophisticated equipment with “unlimited” memory space. In this paper, a new graph format called compressed edge-centric (CEC) is proposed, aiming to reduce memory usage by compressing graph edges, facilitating more efficient large graph handling. It is then followed up by the implementation of several graph creation approaches, each using a different graph representation and parallelism level. The idea is to use a predictive model to estimate the execution time and used memory, along with a decision-making process that will automatically choose the best approach by selecting the lowest predicted time that does not overload the memory for a given graph and in a certain hardware environment. Experiments on diverse real-world graphs, sourced from the Network Repository, demonstrate that CEC-based creation methods are more efficient than vertex-centric techniques in terms of time (up to 60% decrease) and in many cases in terms of memory consumption (up to 50% decrease). Since this efficiency is not uniformly observed, this work relies on machine learning models to predict the time and memory of each approach for a given context. The results show that linear models are not adequate to thoroughly predict time and memory consumption, while tree-based solutions give considerably better results.

In this paper, we introduce the Analysis Platform for Risk, Resilience, and Expenditure in Disasters (APRED)-a disaster-analytic platform developed for crisis practitioners and economic developers across the United States (US). APRED provides practitioners with a centralized platform for exploring disaster resilience and vulnerability profiles of all counties across the US. The platform comprises five sections including: (1) Disaster Resilience Index, (2) Business Vulnerability Index, (3) Disaster Declaration History, (4) County Profile, and (5) Storm History sections. We further describe our end-to-end human-centered design and engineering process that involved contextual inquiry, community-based participatory design, and rapid prototyping with the support of US Economic Development Administration representatives and regional economic developers across the US. Findings from our study revealed that distributed cognition, content heuristic, shareability, and human-centered systems are crucial considerations for developing data-intensive visualization platforms for resilience planning. We discuss the implications of these findings and inform future research on developing sociotechnical visualization platforms to support resilience planning.

Pandemic-tracking apps may form a future infrastructure for public health surveillance. Yet, there has been relatively little exploration of the potential societal implications of such an infrastructure. In semi-structured interviews with 23 participants from India, the Middle East and North Africa (MENA), and the United States, we discussed attitudes and preferences regarding the deployment of apps that support contact tracing to contain the spread of COVID-19. Through interpretive analysis, we examined the relationship between persistent discomfort and vulnerability when using such apps. Such an examination yielded three temporal forms of vulnerability: real, anticipatory, and speculative. By identifying and defining the temporalities of vulnerability through an analysis of people's pandemic-related thoughts and experiences, we develop the overlapping discourses of humanistic infrastructure studies and infrastructural speculation. In doing so, we explore the concept of vulnerability itself and present implications for the study of vulnerability in Human-Computer Interaction (HCI) and for the oversight of app-based public health surveillance.

Pandemic-tracking apps may form a future infrastructure for public health surveillance. Yet, there has been relatively little exploration of the potential societal implications of such an infrastructure. In semi-structured interviews with 23 participants from India, the Middle East and North Africa (MENA), and the United States, we discussed attitudes and preferences regarding the deployment of apps that support contact tracing to contain the spread of COVID-19. Through interpretive analysis, we examined the relationship between persistent discomfort and vulnerability when using such apps. Such an examination yielded three temporal forms of vulnerability: real, anticipatory, and speculative. By identifying and defining the temporalities of vulnerability through an analysis of people's pandemic-related thoughts and experiences, we develop the overlapping discourses of humanistic infrastructure studies and infrastructural speculation. In doing so, we explore the concept of vulnerability itself and present implications for the study of vulnerability in Human-Computer Interaction (HCI) and for the oversight of app-based public health surveillance.