Big Data pre-processing techniques within thewireless sensors networks
The result's identifiers
Result code in 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>
Alternative codes found
RIV/61989100:27740/16:86099069
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Big Data pre-processing techniques within thewireless sensors networks
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Intelligent Systems and Computing. Volume 427
ISBN
978-3-319-29503-9
ISSN
2194-5357
e-ISSN
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Number of pages
11
Pages from-to
667-677
Publisher name
Springer Verlag
Place of publication
London
Event location
Paříž
Event date
Sep 9, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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