CONTENT 2018

Data, to an increasing degree, is not used directly as content represented in documents, but it serves as a foundation for content tailored for and delivered to users working in different and varying contexts. To this end, the actual content is dynamically assembled from base data with respect to a certain context. This is particularly true for content management applications, e.g., for websites that are targeted at a user’s context. The notion of context comprises various dimensions of parameters like language, location, time, user, and user’s device. Most data modeling languages, including programming languages, are not well prepared to cope with variants of content, though. They are designed to manage universal, consistent, and complete sets of data. The Minimalistic Meta Modeling Language (M3L) as a language for content representation has proven particularly useful for modeling content in context. Towards an operational M3L execution environment, we are researching data schemas to efficiently store and utilize M3L models. Such schemas serve as a testbed to discuss context-aware data representation and retrieval in this paper. This is done by expressing contextaware models, in particular M3L statements, by means of traditional persistence technology. Keywords-data modeling; content modeling; context-aware data modeling; content; content management; context.