Selecting efficient and reliable preservation strategies: modeling long-term information integrity using large-scale hierarchical discrete event simulation
This addresses the challenge of preserving digital information over long periods for organizations facing technical, legal, organizational, and economic threats.
The paper tackles the problem of formulating efficient and reliable operational preservation policies for long-term bit-level information integrity against diverse real-world threats, developing a systematic quantitative prediction framework that combines formal modeling, discrete-event simulation, hierarchical modeling, and empirically calibrated sensitivity analysis to identify effective strategies.
This article addresses the problem of formulating efficient and reliable operational preservation policies that ensure bit-level information integrity over long periods, and in the presence of a diverse range of real-world technical, legal, organizational, and economic threats. We develop a systematic, quantitative prediction framework that combines formal modeling, discrete-event-based simulation, hierarchical modeling, and then use empirically calibrated sensitivity analysis to identify effective strategies. The framework offers flexibility for the modeling of a wide range of preservation policies and threats. Since this framework is open source and easily deployed in a cloud computing environment, it can be used to produce analysis based on independent estimates of scenario-specific costs, reliability, and risks.