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Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations

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dc.citedby36
dc.contributor.authorAL-Jumaili A.H.A.en_US
dc.contributor.authorMuniyandi R.C.en_US
dc.contributor.authorHasan M.K.en_US
dc.contributor.authorPaw J.K.S.en_US
dc.contributor.authorSingh M.J.en_US
dc.contributor.authorid57212194331en_US
dc.contributor.authorid14030355800en_US
dc.contributor.authorid55057479600en_US
dc.contributor.authorid58168727000en_US
dc.contributor.authorid58765817900en_US
dc.date.accessioned2024-10-14T03:18:42Z
dc.date.available2024-10-14T03:18:42Z
dc.date.issued2023
dc.description.abstractTraditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges. � 2023 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo2952
dc.identifier.doi10.3390/s23062952
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85151433401
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85151433401&doi=10.3390%2fs23062952&partnerID=40&md5=650f5518d5807ee40bbf001e64c4fd79
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34261
dc.identifier.volume23
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleSensors
dc.subjectbig data
dc.subjectcloud computing
dc.subjectdata mining
dc.subjectparallel computing
dc.subjectpower system
dc.subjectCloud analytics
dc.subjectComputational efficiency
dc.subjectComputer architecture
dc.subjectComputing power
dc.subjectData mining
dc.subjectGreen computing
dc.subjectInformation management
dc.subjectParallel processing systems
dc.subjectParallel programming
dc.subjectPower management
dc.subjectCloud-computing
dc.subjectData analytics
dc.subjectParallel com- puting
dc.subjectPower
dc.subjectPower management systems
dc.subjectPower system
dc.subjectProcess delay
dc.subjectProcess time
dc.subjectSystem conditions
dc.subjectSystem status
dc.subjectBig data
dc.titleBig Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendationsen_US
dc.typeReviewen_US
dspace.entity.typePublication
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