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Due to the expansion and growth of information technologies, much of human knowledge is now recorded on digital media. A new problem in the digital universe has arisen: Digital Preservation. This chapter addresses the problems of Digital Preservation and focuses on the conceptual model within a specific class of digital objects: Relational Databases. Previously, a neutral format was adopted to pursue the goal of platform independence and to achieve a standard format in the digital preservation of relational databases, both data and structure (logical model). The authors address the preservation of relational databases by focusing on the conceptual model of the database, considering the database semantics as an important preservation “property.” For the representation of this higher layer of abstraction present in databases, they use an ontology-based approach. At this higher abstraction level exists inherent Knowledge associated to the database semantics that the authors tentatively represent using “Web Ontology Language” (OWL). From the initial prototype, they develop a framework (supported by case studies) and establish a mapping algorithm for the conversion between databases and OWL. The ontology approach is adopted to formalize the knowledge associated to the conceptual model of the database and also a methodology to create an abstract representation of it. The system is based on the functional axes (ingestion, administration, dissemination, and preservation) of the OAIS reference model.

In today's software industry, systems are constantly changing. To maintain their quality and to prevent failures at controlled costs is a challenge. One way to foster quality is through thorough and systematic testing. Therefore, the definition of adequate tests is crucial for saving time, cost and effort. This paper presents a framework that generates software test cases automatically based on user interaction data. We propose a data-driven software test generation solution that combines the use of frequent sequence mining and Markov chain modeling. We assess the quality of the generated test cases by empirically evaluating their coverage with respect to observed user interactions and code. We also measure the plausibility of the distribution of the events in the generated test sets using the Kullback-Leibler divergence.