Digital preservation is an active area of research, and recent years have brought forward an increasing number of characterisation tools for the object-level analysis of digital content. However, there is a profound lack of objective, standardised and comparable metrics and benchmark collections to enable experimentation and validation of these tools. While fields such as Information Retrieval have for decades been able to rely on benchmark collections annotated with ground truth to enable systematic improvement of algorithms and systems along objective metrics, the digital preservation field is yet unable to provide the necessary ground truth for such benchmarks. Objective indicators, however, are the key enabler for quantitative experimentation and innovation.This paper presents a systematic model-driven benchmark generation framework that aims to provide realistic approximations of real-world digital information collections with fully known ground truth that enables systematic quantitative experimentation, measurement and improvement against objective indicators. We describe the key motivation and idea behind the framework, outline the technological building blocks, and discuss results of the generation of page-based and hierarchical documents from a ground truth model. Based on a discussion of the benefits and challenges of the approach, we outline future work.

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Free benchmark corpora for preservation experiments: using model-driven engineering to generate data sets

© ACM, 2016 This is the author's version of the work It is posted here by permission of ACM for your personal use Not for redistribution The definitive version is forthcoming and will be published in JDIQ in 2016

/pdf/the-challenge-of-test-data-quality-in-data-processing-27eb5844nf.pdf

The Challenge of Test Data Quality in Data Processing

Exploits based on data processing bugs are delivered through crafted data that seems to follow a data format, yet is altered in some way to trigger a specific bug during processing, eg. in order to execute contained malicious code. Decomposing crafted data according to the purported data format and the function of its components that are not format-compliant is a step towards understanding the delivery mechanism of an exploit and fixing the vulnerable application. This paper demonstrates the use of bitstream segment graphs for describing the structure of exploits on the example of the TIFF Jailbreak exploit for the Apple iPhone and iPod Touch with firmware 1.1.1.

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Describing Data Format Exploits Using Bitstream Segment Graphs

Describing Data Format Exploits using Bitstream Segment Graphs (best paper award)

Processing information stored as data in a specific data format is tightly coupled with software implementations that handle necessary elementary processes such as reading and writing. These implementations depend on specific technological environments and thus age due to rapid technological change. The resulting effective loss of information is a major problem for Digital Preservation. In order to provide for persistent, authentic access to stored information, this paper presents a logic-based approach for the formal specification of data formats.

A Logic-Based Approach to the Formal Specification of Data Formats.

Manual development of format-compliant software components is complex, time-consuming and thus errorprone and expensive, as data formats are defined in semi-formal, textual specifications for human engineers. Existing approaches on a formal description of data formats remain at high-level descriptions and fail to describe phenomena such as compression or fragmentation that are especially common in Multimedia file formats. As a step-stone towards the description of data formats as a whole, this paper presents Bitstream Segment Graphs as a complete model on data format instances and presents an example PNGwhere a complete model on data format instances is required.

/pdf/using-bitstream-segment-graphs-for-complete-description-of-3hvu442tz3.pdf

Slaven Travar

Papers

Using bitstream segment graphs for complete description of data format instances