Visualization of Variants in Textual Collations to Analyze the Evolution of Literary Works in the Cervantes Project

As part of the Cervantes Project digital library, we are developing an Electronic Variorum Edition (EVE) of Don Quixote de la Mancha. Multiple editors can create an EVE with our Multi Variant Editor for Documents (MVED), which allows collation of one base text against several comparison texts to identify, link and edit all existing variants among them. In this context we are investigating the use of visualizations to depict graphically variants in order to validate the accuracy of the textual transcriptions and to understand the similarities and differences among different printings and editions. Our broader goal is to enable users to analyze the collation's results and to discover facts about the evolution of the Quixote textual history, and to provide evidence to eliminate printing and compositor's errors and thus to produce a more correct edition closer to Cervantes' original manuscript. This paper describes the visualization tool, and presents the initial results of its use

Feature identification framework and applications (FIFA)

Large digital libraries typically contain large collections of heterogeneous resources intended to be delivered to a variety of user communities. One key challenge for these libraries is providing tight integration between resources both within a single collection and across the several collections of the library with out requiring hand coding. One key tool in doing this is elucidating the internal structure of the digital resources and using that structure to form connections between the resources. The heterogeneous nature of the collections and the diversity of the needs in the user communities complicates this task. Accordingly, in this thesis, I describe an approach to implementing a feature identification system to support digital collections that provides a general framework for applications while allowing decisions about the details of document representation and features identification to be deferred to domain specific implementations of that framework. These deferred decisions include details of the semantics and syntax of markup, the types of metadata to be attached to documents, the types of features to be identified, the feature identification algorithms to be applied, and which features should be indexed. This approach results in strong support for the general aspects of developing a feature identification system allowing future work to focus on the details of applying that system to the specific needs of individual collections and user communities