Digital Preservation, Archival Science and Methodological Foundations for Digital Libraries

Digital libraries, whether commercial, public or personal, lie at the heart of the information society. Yet, research into their long‐term viability and the meaningful accessibility of their contents remains in its infancy. In general, as we have pointed out elsewhere, ‘after more than twenty years of research in digital curation and preservation the actual theories, methods and technologies that can either foster or ensure digital longevity remain startlingly limited.’ Research led by DigitalPreservationEurope (DPE) and the Digital Preservation Cluster of DELOS has allowed us to refine the key research challenges – theoretical, methodological and technological – that need attention by researchers in digital libraries during the coming five to ten years, if we are to ensure that the materials held in our emerging digital libraries are to remain sustainable, authentic, accessible and understandable over time. Building on this work and taking the theoretical framework of archival science as bedrock, this paper investigates digital preservation and its foundational role if digital libraries are to have long‐term viability at the centre of the global information society.

Using a Markov-Chain Model for Assessing Accuracy Degradation and Developing Data Maintenance Policies

Accuracy reflects the extent of correctness of data. It is often evaluated by comparing the values recorded to a baseline perceived as correct. Even when data values are accurate at the time of recording – their accuracy may degrade over time, as certain properties of real-world entities may change, while the data values that reflect them are not being updated. This study uses the Markov-Chain model to develop an analytical framework that describes accuracy degradation over time – this by assessing the likelihood of certain data attributes to transition between states within a given time period. Evaluation of the framework with real-world data shows its potential contribution for key data-quality management tasks, such as the prediction of accuracy degradation, and the development of data auditing and maintenance policies

An Ontology Based Approach to Data Quality Initiatives Cost-Benefit Evaluation

In order to achieve higher data quality targets, organizations need to identify the data quality dimensions that are affected by poor quality, assess them, and evaluate which improvement techniques are suitable to apply. Data quality literature provides methodologies that support complete data quality management by providing guidelines that organizations should contextualize and apply to their scenario. Only a few methodologies use the cost-benefit analysis as a tool to evaluate the feasibility of a data quality improvement project. In this paper, we present an ontological description of the cost-benefit analysis including the most important contributes already proposed in literature. The use of ontologies allows the knowledge improvement by means of the identification of the interdependencies between costs and benefits and enables different complex evaluations. The feasibility and usefulness of the proposed ontology-based tool has been tested by means of a real case study

An Economics-Driven Decision Model for Data Quality Improvement – A Contribution to Data Currency

As poor data quality usually leads to high costs, managing data quality is essential for organizations. Thereby, comparing thecurrent with the required data quality level is necessary for an effective and economics-driven data quality management.Otherwise decision makers might decide in favor of unsuitable or inefficient data quality improvement measures with respectto cost and benefit. Existing methodologies for assessing and improving data quality often neglect providing methods fordetermining the required data quality level or argue on a managerial rather than an operational level. As a consequence, aneconomics-driven and context-dependent decision model for updating data at the level of attribute values is presented. Thismodel contains a metric for currency, errors and error costs, and a currency threshold for attribute values. The decision modelis illustrated using a direct marketing example

A Framework for Economics-Driven Assessment of Data Quality Decisions

Economic perspectives have raised growing attention in recent data quality (DQ) literature, as studies have associated DQ decisions with major cost-benefit tradeoffs. Despite the growing interest, DQ research has not yet developed a robust, agreedupon view for assessing and studying the link between DQ and economic outcome. As a contribution, this study proposes a framework, which links costs to the decisions made in managing the information process and improving the DQ, and benefits to the use of information-product outcomes by data consumers. Considering past research contributions, we develop this framework further into a high-level optimization model that permits quantitative assessment of cost-benefit tradeoffs, towards economically-optimal DQ decisions. We demonstrate a possible use of the proposed framework and the derived model, and highlight their potential contribution to an economics-driven view of DQ issues in both research and practice

A Framework for Classification of the Data and Information Quality Literature and Preliminart Results (1996-2007)

The value of management decisions, the security of our nation, and the very foundations of our business integrity are all dependent on the quality of data and information. However, the quality of the data and information is dependent on how that data or information will be used. This paper proposes a theory of data quality based on the five principles defined by J. M. Juran for product and service quality and extends Wang et al’s 1995 framework for data quality research. It then examines the data and information quality literature from journals within the context of this framework