EMMA - A formal basis for querying enhanced multimedia meta objects

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Abstract

Today's multimedia content formats primarily encode the presentation of content but not the information the content conveys. However, this presentation-oriented modeling only permits the inflexible, hard-wired presentation of multimedia content. For the realization of advanced operations like the retrieval and reuse of content, automatic composition, or adaptation to a user's needs, the multimedia content has to be enriched by additional semantic information, e.g. the semantic interrelationships between single multimedia content items. Enhanced Multimedia Meta Objects (EMMOs) are a novel approach to multimedia content modeling, which combines media, semantic relationships between those media, as well as functionality on the media (such as rendering) into tradeable and versionable knowledge-enriched units of multimedia content. For the processing of EMMOs and the knowledge they incorporate, suitable querying facilities are required. Based on the formal definition of the EMMO model, in this paper, we propose and formally define the EMMO Algebra EMMA, a query algebra that is adequate and complete with regard to the EMMO model. EMMA offers a rich set of orthogonal query operators, which are sufficiently expressive to provide access to all aspects of EMMOs and enable efficient query rewriting and optimization. In addition, they allow for the seamless integration of ontological knowledge within queries, such as supertype/subtype relationships, transitive and inverse associations, etc. Thus, EMMA represents a sound and adequate foundation for the realization of powerful EMMO querying facilities. We have finished the implementation of an EMMO container environment and an EMMA query execution engine, and are currently in the process of evaluating the query algebra in several case studies. © Springer-Verlag Berlin Heidelberg 2005.

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APA

Zillner, S., & Winiwarter, W. (2005). EMMA - A formal basis for querying enhanced multimedia meta objects. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3730 LNCS, pp. 35–68). Springer Verlag. https://doi.org/10.1007/11603412_2

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