Software system for generating and analyzing quantitative restoration and recovery strategies and scenarios for man-made and natural complex networks

INV-15090

Background

Current network-based resilience solutions are ad-hoc and not generalizable across disciplines or systems.

Spatial data mining -- the process of discovering previously unknown and potentially useful patterns from spatiotemporal data – when combined with network analysis, social media datasets and location sensing technologies, yields predictive insights that can inform cyberinfrastructure network resilience decision-making.

An integrated platform that utilizes raw data in conjunction with user provided inputs yields actionable metrics for generating restoration strategies, making complex systems more resilient.

Technology Overview

This data-driven modeling technology translates asset-level geospatial data into network resilience quantification. Modeling outputs are used to develop cyberinfrastructure network recovery and restoration strategies, as well as informed resilience investment decision-making. Wide-ranging applications within human built infrastructure systems and natural systems are viable. The network nodes can be rail stations, airports, and seaports in transportation networks, habitats in ecological networks, or routers and servers in an Internet network. The links are weighted by considering throughput volume, for example the web traffic in Internet networks.

Depending on the exact use case, the user can specify parameters (such as resilience budget in dollars), upload data to augment or even override the default network data stored in the database (such as the estimated cost to repair any facility in the system), and export data from the analysis.

Key Benefits

- Quantifies the fragility and recovery rates of the real-world systems, modeled as networks
- Utilizes raw data in conjunction with user-provided inputs and data to yield actionable metrics for generating restoration strategies and for making complex systems more resilient
- Provides scores regarding the system’s ability to function on a relative (0 to 1) State of Critical Functionality (SCF) scale
- Generalizable across many natural and human-built systems ranging from global air-transportation network to water distribution networks operating at the scale of a county
- Analyzes a system of multiple connected facilities holistically

Commercial Applications

- Real-time scenario-based cyber-attack risk mitigation and recovery

- Optimizing resilience for multiple, interconnected systems such as a cyberinfrastructure network

- Reduces vulnerability to future hazards -- real-time monitoring and response to hazards using top-down system level strategies