About the Journal
The Journal of Big Data Analytics and Cloud Computing (JBDACC) is a multidisciplinary scholarly publication dedicated to advancing the theory, methodologies, and applications of big data analytics and cloud computing. It serves as a comprehensive platform for researchers, practitioners, and industry professionals to disseminate and exchange knowledge in these rapidly evolving fields.
The journal invites high-quality original research articles, reviews, case studies, and technical notes that explore the various aspects of big data analytics and cloud computing. It encompasses a wide range of topics, including but not limited to:
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Big Data Analytics:
- Data mining and knowledge discovery
- Machine learning and deep learning algorithms
- Predictive modeling and pattern recognition
- Sentiment analysis and opinion mining
- Text mining and natural language processing
- Social network analysis and graph mining
- Real-time and stream data analytics
- Visual analytics and data visualization
- Scalable and distributed data analytics
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Cloud Computing:
- Cloud architecture and infrastructure
- Cloud resource management and scheduling
- Virtualization techniques and technologies
- Cloud security and privacy
- Cloud-based data storage and retrieval
- Cloud-based machine learning and AI
- Fog and edge computing
- Serverless computing
- Cloud-native applications and microservices
The Journal of Big Data Analytics and Cloud Computing encourages contributions that demonstrate novel approaches, methodologies, and frameworks, as well as their practical applications and potential impact across various domains, such as healthcare, finance, e-commerce, transportation, social media, and more.
