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ThinkMind // International Journal On Advances in Software, volume 11, numbers 3 and 4, 2018 // View article soft_v11_n34_2018_9


Context-aware Storage and Retrieval of Digital Content: Database Model and Schema Considerations for Content Persistence

Authors:
Hans-Werner Sehring

Keywords: data modeling,data schema,databases,content modeling,context-aware data modeling,content,content management,content management systems,context

Abstract:
In increasingly many information systems that publish digital content, the documents that are generated for publication are tailored for and delivered to users working in different and varying contexts. To this end, the content from which an actual document is created is dynamically selected with respect to a specific context. The task of content selection incorporates queries to an underlying database that hosts data representing content. Such queries are parameterized with a description of the context at hand. 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) can be applied as a language for content representation. M3L has proven particularly useful for modeling content in context. Towards an operational M3L execution environment, we are researching mappings to databases of different data models, and for each data model schemas to efficiently store and utilize M3L models. This article discusses such schemas for context-aware data representation and retrieval. The main focus lies on efficiency of queries used for M3L evaluation with the goal of context-dependent content selection. This is achieved by expressing context-aware models, in particular M3L statements, by means of existing persistence technology.

Pages: 311 to 322

Copyright: Copyright (c) to authors, 2018. Used with permission.

Publication date: December 30, 2018

Published in: journal

ISSN: 1942-2628

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