MERA PÉREZ, David, Michal BATKO and Pavel ZEZULA. Speeding up the multimedia feature extraction: a comparative study on the big data approach. Multimedia Tools and Applications. Springer, vol. 76, No 5, p. 7497-7517. ISSN 1380-7501. doi:10.1007/s11042-016-3415-1. 2017.
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Basic information
Original name Speeding up the multimedia feature extraction: a comparative study on the big data approach
Authors MERA PÉREZ, David (724 Spain), Michal BATKO (203 Czech Republic, guarantor, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, belonging to the institution).
Edition Multimedia Tools and Applications, Springer, 2017, 1380-7501.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
WWW URL
Impact factor Impact factor: 1.541
RIV identification code RIV/00216224:14330/17:00094702
Organization unit Faculty of Informatics
Doi http://dx.doi.org/10.1007/s11042-016-3415-1
UT WoS 000397278400062
Keywords in English Big data;Image feature extraction;Map Reduce;Apache Storm;Apache Spark;Grid computing
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 27/4/2018 10:32.
Abstract
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.
Links
GBP103/12/G084, research and development projectName: Centrum pro multi-modální interpretaci dat velkého rozsahu
Investor: Czech Science Foundation
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