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<i title="Springer Nature">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qrfhtnmiezbrbd2wgd2gragrxi" style="color: black;">Journal of Grid Computing</a>
This work proposes a novel workflow composition approach that hinges upon ontologies and planning as its core technologies within an integrated framework. Video processing problems provide a fitting domain for investigating the effectiveness of this integrated method as tackling such problems have not been fully explored by the workflow, planning and ontological communities despite their combined beneficial traits to confront this known hard problem. In addition, the pervasiveness of video data<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10723-013-9263-6">doi:10.1007/s10723-013-9263-6</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cikk7uvegvfkba5xk6pkwinwum">fatcat:cikk7uvegvfkba5xk6pkwinwum</a> </span>
more »... has proliferated the need for more automated assistance for image processing-naive users, but no adequate support has been provided as of yet. The integrated approach was evaluated on a video set originating from open sea environment of varying quality. Experiments to evaluate the efficiency, adaptability to user's changing needs and user learnability of this approach were conducted on users who did not possess image processing expertise. The findings indicate that using this integrated workflow composition and execution method: 1) provides a speed up of over 90% in execution time for video classification tasks using full automatic processing compared to manual methods without loss of accuracy; 2) is more flexible and adaptable in response to changes in user requests than modifying existing image processing programs when the domain descriptions are altered; 3) assists the user in selecting optimal solutions by providing recommended descriptions.
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