Scaling Big Data Applications in Smart City with Coresets

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14610%2F19%3A00109826" target="_blank" >RIV/00216224:14610/19:00109826 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.5220/0007958803570363" target="_blank" >http://dx.doi.org/10.5220/0007958803570363</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0007958803570363" target="_blank" >10.5220/0007958803570363</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Scaling Big Data Applications in Smart City with Coresets

Popis výsledku v původním jazyce

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 &gt; 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.

Název v anglickém jazyce

Scaling Big Data Applications in Smart City with Coresets

Popis výsledku anglicky

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 &gt; 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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/EF16_013%2F0001802" target="_blank" >EF16_013/0001802: CERIT Scientific Cloud</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1

ISBN

9789897583773

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

357-363

Název nakladatele

SciTePress

Místo vydání

Prague, Czech Republic

Místo konání akce

Prague, Czech Republic

Datum konání akce

1. 1. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000570730200042