Big Data pre-processing techniques within thewireless sensors networks
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099069" target="_blank" >RIV/61989100:27240/16:86099069 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/61989100:27740/16:86099069
Výsledek na webu
<a href="http://dx.doi.org/10.1007/978-3-319-29504-6_61" target="_blank" >http://dx.doi.org/10.1007/978-3-319-29504-6_61</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-29504-6_61" target="_blank" >10.1007/978-3-319-29504-6_61</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Big Data pre-processing techniques within thewireless sensors networks
Popis výsledku v původním jazyce
The recent advances in sensors and communications technologies have emerged the interaction between physical resources and the need for sufficient storage volumes for keeping the continuously generated data. These storage volumes are one of the components of the Big Data to be used in future prediction processes in a broad range of fields. Usually, these data are not ready for analysis as they are incomplete or redundant. Therefore one of the current challenge related to the Big Data is how to save relevant data and discard noisy and redundant data. On the other hand, Wireless Sensor Networks (WSNs) (as a source of Big Data) use a number of techniques that significantly reduce the required data transmissions ratio. These techniques not only improve the operational lifetime of these networks but also raise the level of the refinement at the Big Data side. This article gives an overview and classifications of the data reduction and compression techniques proposed to do data pre-processing in-networks (i.e. in-WSNs). It compares and discusses which of these techniques would be adopted or modified to enhance the functionality of the WSNs while minimizing any further pre-processing at the Big Data side, thus reducing the computational and storage cost at the Big Data side. (C) Springer International Publishing Switzerland 2016.
Název v anglickém jazyce
Big Data pre-processing techniques within thewireless sensors networks
Popis výsledku anglicky
The recent advances in sensors and communications technologies have emerged the interaction between physical resources and the need for sufficient storage volumes for keeping the continuously generated data. These storage volumes are one of the components of the Big Data to be used in future prediction processes in a broad range of fields. Usually, these data are not ready for analysis as they are incomplete or redundant. Therefore one of the current challenge related to the Big Data is how to save relevant data and discard noisy and redundant data. On the other hand, Wireless Sensor Networks (WSNs) (as a source of Big Data) use a number of techniques that significantly reduce the required data transmissions ratio. These techniques not only improve the operational lifetime of these networks but also raise the level of the refinement at the Big Data side. This article gives an overview and classifications of the data reduction and compression techniques proposed to do data pre-processing in-networks (i.e. in-WSNs). It compares and discusses which of these techniques would be adopted or modified to enhance the functionality of the WSNs while minimizing any further pre-processing at the Big Data side, thus reducing the computational and storage cost at the Big Data side. (C) Springer International Publishing Switzerland 2016.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
IN - Informatika
OECD FORD obor
—
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2016
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Advances in Intelligent Systems and Computing. Volume 427
ISBN
978-3-319-29503-9
ISSN
2194-5357
e-ISSN
—
Počet stran výsledku
11
Strana od-do
667-677
Název nakladatele
Springer Verlag
Místo vydání
London
Místo konání akce
Paříž
Datum konání akce
9. 9. 2015
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—