A Knowledge Visualization of Database Content Created by a Database Taxonomy

The synergy between information visualization and knowledge visualization is explored using the "DatabaseTaxonomy" to guide the way. Drawing extensively on Burgin\´s mathematical theory of named sets, this new knowledge visualization treats all database content as ifit were scientific data, regardless of the database application. This mathematical tool penetrates deep into the logical structure of data and data relations, enabling a single algorithm to generate a conceptual knowledge structure, which pre-structures raw data in the database into a list of nested data-topic lists that works like a book index. For end-users, this visualization is familiar, convenient and precise. For the research community, this knowledge structure and the techniques used to build it offer an empirical tool for investigating the underlying properties of data, information, and knowledge on a computing device. The transformation from data to information to knowledge is automatic and seamless, thanks to a novel analysis of the logical structure of the symbols on these mechanical devices, one which reveals meta-symbols consisting of physical values(v) and constructed-types (t). The Database Taxonomy also provides a first glimpse into how navigating this structure can generate a predicate logic expression, an outcome which the author believes promises to advance our theoretical understanding of knowledge visualization and of the influence of a digital media on symbolic logic.