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Decreasing data analytics time : hybrid architecture mapreduce-massive parallel processing for a smart grid
Joint Authors
Mihani, Abd al-Salam
Alimazigui, Zaia
Ahmad Nasir, Muhammad
Source
Issue
Vol. 13, Issue 1 (31 Mar. 2017), pp.74-85, 12 p.
Publisher
Publication Date
2017-03-31
Country of Publication
Algeria
No. of Pages
12
Main Subjects
Abstract EN
As our populations grow in a world of limited resources enterprise seek ways to lighten our load on the planet.
The idea of modifying consumer behavior appears as a foundation for smart grids.
Enterprise demonstrates the value available from deep analysis of electricity consummation histories, consumers’ messages, and outage alerts, etc.
Enterprise mines massive structured and unstructured data.
In a nutshell, smart grids result in a flood of data that needs to be analyzed, for better adjust to demand and give customers more ability to delve into their power consumption.
Simply put, smart grids will increasingly have a flexible data warehouse attached to them.
The key driver for the adoption of data management strategies is clearly the need to handle and analyze the large amounts of information utilities are now faced with.
New approaches to data integration are nauseating moment; Hadoop is in fact now being used by the utility to help manage the huge growth in data whilst maintaining coherence of the Data Warehouse.
In this paper we define a new Meter Data Management System Architecture repository that differ with three leaders MDMS, where we use MapReduce programming model for ETL and Parallel DBMS in Query statements(Massive Parallel Processing MPP).
American Psychological Association (APA)
Mihani, Abd al-Salam& Alimazigui, Zaia& Ahmad Nasir, Muhammad. 2017. Decreasing data analytics time : hybrid architecture mapreduce-massive parallel processing for a smart grid. Journal of Electrical Systems،Vol. 13, no. 1, pp.74-85.
https://search.emarefa.net/detail/BIM-723423
Modern Language Association (MLA)
Mihani, Abd al-Salam…[et al.]. Decreasing data analytics time : hybrid architecture mapreduce-massive parallel processing for a smart grid. Journal of Electrical Systems Vol. 13, no. 1 (2017), pp.74-85.
https://search.emarefa.net/detail/BIM-723423
American Medical Association (AMA)
Mihani, Abd al-Salam& Alimazigui, Zaia& Ahmad Nasir, Muhammad. Decreasing data analytics time : hybrid architecture mapreduce-massive parallel processing for a smart grid. Journal of Electrical Systems. 2017. Vol. 13, no. 1, pp.74-85.
https://search.emarefa.net/detail/BIM-723423
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 84-85
Record ID
BIM-723423