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Abstract

The enormous amount of valuable information that is produced today and needs to be made available over the long-term has led to increased efforts in scalable, automated solutions for long-term digital preservation. The mission of preservation planning is to define the optimal actions to ensure future access to digital content and react to changes that require adjustments in repository operations. Considerable effort has been spent in the past on defining, implementing, and validating a framework and system for preservation planning. This article sheds light on the actual decision criteria and influence factors to be considered when choosing digital preservation actions. It is based on an extensive evaluation of case studies on preservation planning for a range of different types of objects with partners from different institutional backgrounds. We categorize decision criteria from a number of real-world decision-making instances in a taxonomy. We show that a majority of the criteria can be evaluated by applying automated measurements under realistic conditions, and demonstrate that controlled experimentation and automated measurements can be used to substantially improve repeatability of decisions and reduce the effort needed to evaluate preservation components. The presented measurement framework enables scalable preservation and monitoring and supports trust in preservation decisions because extensive evidence is produced in a reproducible, automated way and documented as the basis of decision making in a standardized form.

Type

a

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Type

a

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Abstract

Bias quantification of retrieval functions with the help of document retrievability scores has recently evolved as an important evaluation measure for recall-oriented retrieval applications. While numerous studies have evaluated retrieval bias of retrieval functions, solid validation of its impact on realistic types of queries is still limited. This is due to the lack of well-accepted criteria for query generation for estimating retrievability. Commonly, random queries are used for approximating documents retrievability due to the prohibitively large query space and time involved in processing all queries. Additionally, a cumulative retrievability score of documents over all queries is used for analyzing retrieval functions (retrieval) bias. However, this approach does not consider the difference between different query characteristics (QCs) and their influence on retrieval functions' bias quantification. This article provides an in-depth study of retrievability over different QCs. It analyzes the correlation of lower/higher retrieval bias with different query characteristics. The presence of strong correlation between retrieval bias and query characteristics in experiments indicates the possibility of determining retrieval bias of retrieval functions without processing an exhaustive query set. Experiments are validated on TREC Chemical Retrieval Track consisting of 1.2 million patent documents.

Type

a

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Source

Proceedings of the 2nd International Workshop on Semantic Digital Archives held in conjunction with the 16th Int. Conference on Theory and Practice of Digital Libraries (TPDL) on September 27, 2012 in Paphos, Cyprus [http://ceur-ws.org/Vol-912/proceedings.pdf]. Eds.: A. Mitschik et al