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Speeding up the multimedia feature extraction: a comparative study on the big data approach

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MERA PÉREZ David BATKO Michal ZEZULA Pavel

Rok publikování 2017
Druh Článek v odborném periodiku
Časopis / Zdroj Multimedia Tools and Applications
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
www http://dx.doi.org/10.1007/s11042-016-3415-1
Doi http://dx.doi.org/10.1007/s11042-016-3415-1
Obor Informatika
Klíčová slova Big data;Image feature extraction;Map Reduce;Apache Storm;Apache Spark;Grid computing
Popis The current explosion of multimedia data is significantly increasing the amount of potential knowledge. However, to get to the actual information requires to apply novel content-based techniques which in turn require time consuming extraction of indexable features from the raw data. In order to deal with large datasets, this task needs to be parallelized. However, there are multiple approaches to choose from, each with its own benefits and drawbacks. There are also several parameters that must be taken into consideration, for example the amount of available resources, the size of the data and their availability. In this paper, we empirically evaluate and compare approaches based on Apache Hadoop, Apache Storm, Apache Spark, and Grid computing, employed to distribute the extraction task over an outsourced and distributed infrastructure.
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