Machine Learning for Big Data--Big Data Service 2019

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: closed (20 February 2020) | Viewed by 6597

Special Issue Editor


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Guest Editor
Department of Electrical Engineering, San Jose State University, San Jose, CA 95192, USA
Interests: machine learning; signal processing; communications; wireless networks

Special Issue Information

Dear Colleagues,

Big Data has incredible potential in fields such as health case, self-driving vehicles, online advertising, energy, and finance. However, its potential highly relies on the performance of the data analytics methods used. Machine learning is a field that deals with understanding data and building models. Traditional machine-learning techniques rely on assumptions that are no longer valid in the context of Big Data. This Special Issue will address challenges and possible solutions specific to machine learning in the context of Big Data. These issues could be related to any of the aspects of Big Data: its volume, variety, velocity, veracity, or valence.

Dr. Birsen Sirkeci
Guest Editor

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Keywords

  • machine learning
  • big data

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Published Papers (1 paper)

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Research

13 pages, 1245 KiB  
Article
Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance
by Sofia Fernandes, Mário Antunes, Ana Rita Santiago, João Paulo Barraca, Diogo Gomes and Rui L. Aguiar
Information 2020, 11(4), 208; https://doi.org/10.3390/info11040208 - 14 Apr 2020
Cited by 32 | Viewed by 6210
Abstract
Heating appliances consume approximately 48 % of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before [...] Read more.
Heating appliances consume approximately 48 % of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data. Full article
(This article belongs to the Special Issue Machine Learning for Big Data--Big Data Service 2019)
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