Analysis of Nature-Inspired Algorithms for Long-Term Digital Preservation

Abstract

Digital preservation is a research area devoted to keeping digital assets preserved and usable for many years. Out of the many approaches to digital preservation, the present research article follows a new object-centered digital preservation paradigm where digital objects share part of the responsibility for preservation: they can move, replicate, and evolve to a higher-quality format inside a digital ecosystem. In the new framework, the behavior of digital objects needs to be modeled in order to obtain the best preservation strategy. Thus, digital objects are programmed with the mission of their own long-term self-preservation, which entails being accessible and reproducible by users at any time in the future regardless of frequent technological changes due to software and hardware upgrades. Three nature-inspired computational intelligence algorithms, based on the collective behavior of decentralized and self-organized systems, were selected for the modeling approach: multipopulation genetic algorithm, ant colony optimization, and a virus-based algorithm. TiM, a simulated environment for running distributed digital ecosystems, was used to perform the experiments. The results map the relation between the models and the expected object diversity obtained in short- and mid-term digital preservation scenarios. Comparing the results, the best performance corresponded to the multipopulation genetic algorithm. The article aims to be a first step in the digital self-preservation field. Building nature-inspired model behaviors is a good approach and opens the door to future tests with other AI-based methods.

Evaluating Auction Mechanisms for the Preservation of Cost-Aware Digital Objects under Constrained Digital Preservation Budgets

“…Digital objects are mainly files and, if they are programmed with the mission to persist and remain accessible, their strategy to do so is achieved by requesting preservation services spread among the Internet in order to guarantee the quality of their main defining attributes-accessibility, readability, integrity and authenticity-over time. In [15], the final simulated results obtained demonstrated that a digital preservation environment with objects programmed for self-preservation is possible. The experiments presented there were a first step towards creating real structured environments where models coexist under a predefined system of rules and constraints and concluded that more tests were needed in order to characterize the desirable properties that could be required from intelligent behaviors, the distributed interaction environment and the response of the The results presented in the current work follow the opened path and add a new constrain to DO behavior inside the DP paradigm initially proposed.…”

“…The objective of this research document is to present new results obtained as a continuation of the work published in [15]. That previous publication contained a proposal for a novel object-centered paradigm to address the digital preservation problem where digital objects, programmed with using basic computational intelligence behaviors, shared part of the responsibility for self-preservation.…”

“…The authors think that the presented bottom-up cost-aware budgetbased model would be able to cope better with the exponential growth of preservation needs. This article is a continuation of the works discussed in [36], focused on the budget optimization of SPDOs through micro-negotiations, and the work detailed in [15], centered in the development and testing of SPDOs through nature inspired computational intelligence methods. The presented article expands previous work on the field, searching for an efficient cost-management approach to be applied inside a self-preserving digital object context.…”

“…Initially conceived as a simulated environment for running a wide number of agent-based ecosystems, today it has evolved to a simulation platform for DP studies in SPDO contexts. Recently, the TiM simulator has been used for testing DO behaviors using nature-inspired computation intelligence methods in a SPDO environment [15]. The platform, programmed in JAVA, has been developed as a modular platform.…”

“…This change of paradigm, from machine-to-human intelligence to machine-to-machine intelligence, imports concepts from computational ecologies [14] and defines the conceptual term of self-preserving digital object (SPDO). Thus, if the previous work presented in [15] explored how computational intelligence can model digital object behaviors in order to obtain the best preservation strategy, the work proposed in this article goes one step further and adds one more constrain into the self-preservation equation: the long term economical sustainability of SPDOs, where the SPDOs spend a given budget efficiently searching for the best preservation services. Avoiding future data deluge will have a cost, and it makes sense that, in a distributed environment where intelligent digital objects are programmed to autonomously find the best preservation strategies, part of this intelligence is dedicated to assure optimized costs and high efficiency at budget management.…”