Big data processing using hybrid Gaussian mixture model with salp swarm algorithm

The traditional methods used in big data, like cluster creation and query-based data extraction, fail to yield accurate results on massive networks. To address such issues, the proposed approach involves using the Hadoop Distributed File System (HDFS) for data processing, the map-reduce programming...

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Veröffentlicht in:	Journal of big data Jg. 11; H. 1; S. 167 - 29
Hauptverfasser:	Saravanakumar, R., TamilSelvi, T., Pandey, Digvijay, Pandey, Binay Kumar, Mahajan, Darshan A., Lelisho, Mesfin Esayas
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.12.2024 Springer Nature B.V SpringerOpen
Schlagworte:	Algorithms Big Data Clustering Communications Engineering Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Data processing Database Management Extraction Gaussian mixture model (GMM) Gaussian process Hadoop distributed file system (HDFS) Information Storage and Retrieval Interconnections Latency Map-reduce Mathematical Applications in Computer Science Networks Optimization Optimization techniques Parameter sensitivity Probabilistic models Salp swarm algorithm (SSA) Secure hash algorithms (SHA) Secure hash algorithms (SHA) Gaussian mixture model (GMM) Hadoop distributed file system (HDFS) Salp swarm algorithm (SSA) Map-reduce
ISSN:	2196-1115, 2196-1115
Online-Zugang:	Volltext
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