Speeding up the multimedia feature extraction: a comparative study on the big data approach
| Authors | |
|---|---|
| Year of publication | 2017 |
| Type | Article in Periodical |
| Magazine / Source | Multimedia Tools and Applications |
| MU Faculty or unit | |
| Citation | |
| Web | http://dx.doi.org/10.1007/s11042-016-3415-1 |
| Doi | http://dx.doi.org/10.1007/s11042-016-3415-1 |
| Field | Informatics |
| Keywords | Big data;Image feature extraction;Map Reduce;Apache Storm;Apache Spark;Grid computing |
| Description | 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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