Scaling Big Data Applications in Smart City with Coresets

D 2019

Scaling Big Data Applications in Smart City with Coresets

TRANG, Le Hong, Hind BANGUI, Mouzhi GE a Barbora BÜHNOVÁ

Základní údaje

Originální název

Scaling Big Data Applications in Smart City with Coresets

Autoři

TRANG, Le Hong, Hind BANGUI (504 Maroko, domácí), Mouzhi GE (156 Čína, domácí) a Barbora BÜHNOVÁ (203 Česká republika, garant, domácí)

Vydání

Prague, Czech Republic, Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1, od s. 357-363, 7 s. 2019

Nakladatel

SciTePress

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Německo

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Kód RIV

RIV/00216224:14610/19:00109826

Organizační jednotka

Ústav výpočetní techniky

ISBN

978-989-758-377-3

DOI

http://dx.doi.org/10.5220/0007958803570363

UT WoS

000570730200042

Klíčová slova anglicky

Big Data; Classification; Coreset; Clustering; Sampling; Smart City

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 27. 3. 2020 14:29, Mgr. Alena Mokrá

Anotace

V originále

With the development of Big Data applications in Smart Cities, various Big Data applications are proposed within the domain. These are however hard to test and prototype, since such prototyping requires big computing resources. In order to save the effort in building Big Data prototypes for Smart Cities, this paper proposes an enhanced sampling technique to obtain a coreset from Big Data while keeping the features of the Big Data, such as clustering structure and distribution density. In the proposed sampling method, for a given dataset and an e > 0, the method computes an e-coreset of the dataset. The e-coreset is then modified to obtain a sample set while ensuring the separation and balance in the set. Furthermore, by considering the representativeness of each sample point, our method can helps to remove noises and outliers. We believe that the coreset-based technique can be used to efficiently prototype and evaluate Big Data applications in the Smart City.

Návaznosti

EF16_013/0001802, projekt VaV

Název: CERIT Scientific Cloud

Citovat

TRANG, Le Hong, Hind BANGUI, Mouzhi GE a Barbora BÜHNOVÁ. Scaling Big Data Applications in Smart City with Coresets. Online. In Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1. Prague, Czech Republic: SciTePress, 2019, s. 357-363. ISBN 978-989-758-377-3. Dostupné z: https://dx.doi.org/10.5220/0007958803570363.

@inproceedings{1536678,
   author = {Trang, Le Hong and Bangui, Hind and Ge, Mouzhi and Bühnová, Barbora},
   address = {Prague, Czech Republic},
   booktitle = {Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1},
   doi = {http://dx.doi.org/10.5220/0007958803570363},
   keywords = {Big Data; Classification; Coreset; Clustering; Sampling; Smart City},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Prague, Czech Republic},
   isbn = {978-989-758-377-3},
   pages = {357-363},
   publisher = {SciTePress},
   title = {Scaling Big Data Applications in Smart City with Coresets},
   url = {http://dx.doi.org/10.5220/0007958803570363},
   year = {2019}
}

TY  - JOUR
ID  - 1536678
AU  - Trang, Le Hong - Bangui, Hind - Ge, Mouzhi - Bühnová, Barbora
PY  - 2019
TI  - Scaling Big Data Applications in Smart City with Coresets
PB  - SciTePress
CY  - Prague, Czech Republic
SN  - 9789897583773
KW  - Big Data
KW  - Classification
KW  - Coreset
KW  - Clustering
KW  - Sampling
KW  - Smart City
UR  - http://dx.doi.org/10.5220/0007958803570363
L2  - http://dx.doi.org/10.5220/0007958803570363
N2  - With the development of Big Data applications in Smart Cities, various Big Data applications are proposed within the domain. These are however hard to test and prototype, since such prototyping requires big computing resources. In order to save the effort in building Big Data prototypes for Smart Cities, this paper proposes an enhanced sampling technique to obtain a coreset from Big Data while keeping the features of the Big Data, such as clustering structure and distribution density. In the proposed sampling method, for a given dataset and an e > 0, the method computes an e-coreset of the dataset. The e-coreset is then modified to obtain a sample set while ensuring the separation and balance in the set. Furthermore, by considering the representativeness of each sample point, our method can helps to remove noises and outliers. We believe that the coreset-based technique can be used to efficiently prototype and evaluate Big Data applications in the Smart City.
ER  -

TRANG, Le Hong, Hind BANGUI, Mouzhi GE a Barbora BÜHNOVÁ. Scaling Big Data Applications in Smart City with Coresets. Online. In \textit{Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1}. Prague, Czech Republic: SciTePress, 2019, s.~357-363. ISBN~978-989-758-377-3. Dostupné z: https://dx.doi.org/10.5220/0007958803570363.

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