Map reduce and match aggregate pipeline performance analysis in metadata identification and analysis for document, audio, image, and video
Автор: Mardhani Riasetiawan
Журнал: International Journal of Information Engineering and Electronic Business @ijieeb
Статья в выпуске: 4 vol.10, 2018 года.
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The study observes the metadata identification and analysis for Document, Audio, Image, and Videos. The process uses MapReduce and Match Aggregate Pipeline to identify, classify, and categories for identification purposes. The inputs are FITS array results and processed in form of XML. The works consist of the extraction process, identification and analysis, classification, and metadata information. The objective is establishing the file information based on volume, variety, veracity, and velocity criteria as part of task identification component in Self-Assignment Data Management. Testing is done for all file types with the number of files and the size of the file according to the grouping. The results show that there is a pattern where the match-aggregate-pipeline has a longer processing time than MapReduce on a small block size, shown in a block size of 64 Mb, 128 Mb, and 256 Mb. But once the block size is magnified the match-aggregate-pipeline has faster processing time at 1024 Mb and 2048 Mb. The results have a contribution in the metadata processing for large files can be done by arranging the block sizes in Match Aggregate Pipeline.
Metadata, Document, Audio, Image, Video, MapReduce, Match Aggregate Pipeline, FITS, Self-Assignment Data Management
Короткий адрес: https://sciup.org/15016136
IDR: 15016136 | DOI: 10.5815/ijieeb.2018.04.01
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