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Cultural history enthusiasts have asserted the urgent need to protect digital information from imminent loss. Without action, much of what has been created in digital form is likely to become unusable. Although a decade has already elapsed since this challenge was clearly articulated, nobody has described a complete procedure for preventing such loss - until now.
Leading industry consultant Henry M. Gladney outlines a technical solution and justifies its correctness and optimality. His presentation focuses on long-term digital preservation principles as a basis for producing the software that will be needed. The method described will work for any kind of digital document, multimedia file, business record collection, or scientific information, and is believed to be optimal with respect to both the quality of the preserved information and end-user convenience. Additionally, Dr. Gladney explains the requirements of the related software, and sketches how to implement it.
Preserving Digital Information presents an up-to-date description of its field, together with a solution for all technical problems identified in the pertinent professional literature. It is for archivists, research librarians, and museum curators who need to understand digital technology in order to manage their institutions; software engineers and computer scientists whose work requires sound information about digital preservation; and attorneys, medical professionals, government officials, and business executives who depend on the long-term reliability of digital records.
From the reviews:
"The book provides a wide range of examples that illustrate the various types of information. It is for producers and users who may be interested in long-term archiving and retrieval of digital information. This book states that today there is no single solution that will work for the many kinds of digital information being used by many different users. . Gladney is an expert on this subject, and this book offers a thorough treatment of it." (Stan Kurkovsky, ACM Computing Reviews, Vol. 49 (8), August, 2008)
Henry M. Gladney is an industry consultant for digital preservation and document management. In 2001, he founded his own company, HMG Consulting, based in Saratoga, CA, after having worked for IBM Research for decades, designing - among other systems - a digital library service that is the core of today's IBM Content Manager®. He is a regular author in the top ACM periodicals, holds eleven patents, and produces the "Digital Document Quarterly", an online newsletter that has discussed preservation extensively.
12 Durable Representation (p. 235-236) We want unambiguous communication with future generations with whom dialog is impossible, without restricting what today’s authors can communicate. For this, we need language that we can confidently expect our descendants to understand easily. This challenge is the kind of language problem that has been central to computer science since it emerged as a discipline in the 1960s. Its core can be restated as, "ensure that an arbitrary computer program will execute correctly on a machine whose architecture is unknown when the program is saved." The English logician A. M. Turing showed in 1937 (and various computing machine experts have put this into practice since then in various particular ways) that it is possible to develop code instruction systems for a computing machine which cause it to behave as if it were another, specified, computing machine. … A code, which according to Turing's schema is supposed to make one machine behave as if it were another specific machine … must do the following things. It must contain, in terms that the machine will understand and (purposively obey), instructions … that will cause the machine to examine every order it gets and determine whether this order has the structure appropriate to an order of the second machine. It must then contain, in terms of the order system of the first machine, sufficient orders to make the machine cause the actions to be taken that the second machine would have taken under the influence of the order in question. The important result of Turing's is that in this way the first machine can be caused to imitate the behavior of any other machine. von Neumann 1956, The Computer and the Brain, pp.70–71 Durable encoding, described in this chapter, represents difficult content types with the aid of programs written in virtual machine code - the code of a machine we call a UVC (Universal Virtual Computer). This Turing- Machine-equivalent virtual machine is simple compared to the designs of practical hardware. Its design can be specified completely, concisely, and unambiguously for future interpretation. Objects to be preserved might consist of several source files, each represented as a bit-stream in a Fig. 32 digital object collection, with labeled links between parts of the complete package. Much of each TDO will be encoded using XML, relations, encryption algorithms, and identifiers. These are governed by relatively simple standards that are widely used - standards that we can be reasonably confident will be completely and correctly understood many years into the future. As described in §11.1, metadata can, and should, record the representation of each TDO component. The means for making each Fig. 32 content blob interpretable forever remains to be provided. What follows describes how this can be accomplished for a single content blob. 12.1 Representation Alternatives We want information representation methods that can be embodied in tools whose use would be practical for information producers and consumers who do not have specialized skills or equipment.
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