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Human-Centric Computing and Information Sciences
Article . 2017 . Peer-reviewed
License: CC BY
Data sources: Crossref
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The convergence computing model for big sensor data mining and knowledge discovery

Authors: Finogeev, Alexey G.; Parygin, Danila S.; Finogeev, Anton A.;

The convergence computing model for big sensor data mining and knowledge discovery

Abstract

AbstractThe article considers the model and method of converged computing and storage to create SCADA systems based on wireless networks for the energy industry. Computing power of modern wireless sensor network nodes allow the transfer to them some operations sensor data mining and offload the dispatching data centre servers. This fog computing model is used for the aggregation of primary data, forecast trends controlled variables as well as to warn about abnormal and emergency situations on distributed SCADA systems objects. Large arrays of sensor data, integral indicators and heterogeneous information from other sources (e.g., weather stations, security and fire alarm systems, video surveillance systems, etc.) is more appropriate to process via GRID computing model. GRID computing model has three-tier architecture, which includes the main server at the first level, a cluster of servers at the second level, and a lot of GPU video card with support for Compute Unified Device Architecture at the third level. The model of cloud computing and cloud storage today is the basis for the accumulation of the results of data mining and knowledge discovery. Means of communication and remote access can solve the problem of intellectual processing and visualization of information with elements of augmented reality and geo-information technologies within the framework of mobile computing model. The implementation of these four computing models for the operation of components of SCADA system is the convergent approach to distributed sensor data processing, which is discussed in the article.

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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!
38
Top 10%
Top 10%
Top 10%
gold