Data Science Methods in Big Data Era
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 7408
Special Issue Editors
Interests: data mining; big data; machine learning; information retrieval; misinformation; correlation statistical measures; fuzzy logic and fuzzy sets theory; sentence quantification and fuzzy quantification; information fusion; energy efficiency; federated learning
Special Issues, Collections and Topics in MDPI journals
Interests: data science; text mining; big data; artificial intelligence; machine learning; knowledge management; federated learning; misinformation; sentimental analysis; social network analysis; natural language processing; food computing; energy efficiency; health
2. UCL Department of Experimental Psychology, University College London, London WC1H 0AP, UK
Interests: data mining; big data; machine learning; misinformation; fuzzy association rules; information fusion; energy efficiency; explainable artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Today, the amount of data generated every day on the Internet, in social media channels or in economic transactions exceeds the usual limits for its analysis using conventional data mining and machine learning techniques. In the last decade, numerous approaches have been proposed in different fields such as security, economy, energy, health, tourism, biological processes, customer profiles, anomaly detection, emergency management, etc. Therefore, it is necessary to continue investigating new methodologies and approaches following the big data paradigm in order to improve the analysis and obtain valuable information from massive datasets.
This Special Issue aims to discuss critical issues and challenges that the development of analysis and learning methods may face when dealing with massive amounts of data. Therefore, it aims to collect works at the forefront of data mining or machine learning with a focus on big data applications.
Topics include but are not limited to:
- Theoretical and/or technical application of data or text mining methods in big data;
- Theoretical and/or technical application of machine learning methods in big data;
- Cloud computing in big data analysis;
- Semantic models and knowledge representation for big data mining;
- Parallel and distributed algorithms for big data mining or machine learning;
- Social media analysis or web mining;
- Stream mining and time series analysis;
- Big data in fuzzy sets;
- Information summarization and/or visualization in big data;
- Novel applications of big data algorithms in several ambits: security, economy, health, tourism, energy, biological process, customer profiles, anomaly detection, emergency management, situation recognition, etc.
Dr. M. Dolores Ruiz
Prof. Dr. Maria J. Martin Bautista
Guest Editors
Dr. Carlos Fernández-Basso
Dr. Karel Gutiérrez-Batista
Guest Editor Assistants
Manuscript Submission Information
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Keywords
- data mining
- machine learning
- big data
- stream mining
- cloud computing
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