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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao UnissResearcharrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
UnissResearch
Part of book or chapter of book . 2013
Data sources: UnissResearch
https://doi.org/10.1201/b16014...
Part of book or chapter of book . 2013 . Peer-reviewed
Data sources: Crossref
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Big Data Application

Analyzing Real-Time Electric Meter Data
Authors: Simonov M.; Caragnano G.; Mossucca L.; Ruiu P.; Terzo O.;

Big Data Application

Abstract

New electricity metering devices operating within the distribution networks originate data flows. Metering data are created at remote locations and processed somewhere else. The term “Data” denotes a substance, almost all useful data are “given” to us either by nature, as a reward for careful observation of physical processes, or by other people, usually inadvertently. By the term “Big Data,” we mean the enormous volume, velocity, and type of data that come from different application fields and have the potential to be turned into business value. More and more companies store great amounts of data and its volume is expanding at a terrifying rate in today’s hyperconnected world where people and businesses are creating more and more data every day. For example, very frequent metering—up to subsecond sampling—depicts the true picture about the energy dynamics in power system. Big Data issue concerns: (a) complexity within the data set; (b) amount of value that can be derived from optimized and not analysis techniques; and (c) support data for the analysis. One could describe “big” in terms of the number of useful permutations of sources making useful querying difficult (such as the sensors in an aircraft) and complex interrelationships making cleaning data difficult. We can consider two primary attributes. However, the term “Big” refers to big complexity rather than big volume. Usually relevant and complex data sets of this sort tend to grow rapidly and so Big Data quickly becomes truly astronomical.

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Average
Average
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