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@inproceedings{1298256,
author = {Zezula, Pavel},
address = {Berlin},
booktitle = {Scalable Information Systems - 5th International Conference},
doi = {http://dx.doi.org/10.1007/978-3-319-16868-5_1},
editor = {Jason J. Jung, Costin Badica, and Attila Kiss},
keywords = {similarity search; scalability; big data; chllenges},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Berlin},
isbn = {978-3-319-16867-8},
pages = {3-12},
publisher = {Springer},
title = {Scalable Similarity Search for Big Data - Challenges and Research Objectives},
year = {2015}
}
TY - JOUR
ID - 1298256
AU - Zezula, Pavel
PY - 2015
TI - Scalable Similarity Search for Big Data - Challenges and Research Objectives
PB - Springer
CY - Berlin
SN - 9783319168678
KW - similarity search
KW - scalability
KW - big data
KW - chllenges
N2 - Analysis of contemporary Big Data collections require an effective and efficient content-based access to data which is usually unstructured. This first implies a necessity to uncover descriptive knowledge of complex and heterogeneous objects to make them findable. Second, multimodal search structures are needed to efficiently execute complex similarity queries possibly in outsourced environments while preserving privacy. Four specific research objectives to tackle the challenges are outlined and discussed. It is believed that a relevant solution of these problems is necessary for a scalable similarity search operating on Big Data.
ER -
ZEZULA, Pavel. Scalable Similarity Search for Big Data - Challenges and Research Objectives. Online. In Jason J. Jung, Costin Badica, and Attila Kiss. \textit{Scalable Information Systems - 5th International Conference}. Berlin: Springer, 2015. s.~3-12. ISBN~978-3-319-16867-8. Dostupné z: https://dx.doi.org/10.1007/978-3-319-16868-5\_{}1. [citováno 2024-04-23]
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