The 10th International Conference on Data Storage and Data Engineering (DSDE 2027)

The 10th International Conference on Data Storage and Data Engineering (DSDE 2027) examines advances in data storage, engineering, and analytics through topics like big data infrastructure, database systems, and intelligent data processing.

Key Topics

• Data Management and Quality
  • Mobile Data Management and IoT Data
  • Architectural Concepts for Data Management
  • Open Data and Transparency in Research Data
  • Large Data Systems Modeling and Management
  • Data and Information Quality Management
  • Smart Cities and Urban Data Analytics
  • Data Modeling, Visualization, and Virtualization
  • Data Management for Analytics
  • Linked Data and Semantic Web Technologies
  • Industry 4.0 and Sensor Data Management
  • City Data Management and Governance
  • Organizational Concepts and Best Practices in Data Management
• Big Data and Analytics
  • Computational Models for Big Data
  • Big Data Applications and Case Studies
  • Social Data Analytics and Web Mining
  • Data Standards and Interoperability
  • Big Data Infrastructure and Cloud/Grid/Stream Computing
  • Big Data as a Service
  • Big Data Security, Privacy, and Trust
  • Big Data Search, Mining, and Visualization
  • Energy-Efficient Computing and In-Memory Databases
  • Deep Learning for Big Data
  • Edge Computing and In-Network Data Processing
  • Information Visualization and Visual Analytics
• Databases and Data Security
  • Large-Scale and Distributed Databases
  • Database Architecture and Performance Optimization
  • Object-Oriented and Open Source Databases
  • Mobile and NoSQL Databases
  • Data Integrity and Consistency
  • Query Processing and Optimization
  • WWW and Databases
  • Data Privacy, Security, and Confidentiality
  • Database Management in Cloud Environments
  • Blockchain Technology for Data Storage and Security
• Data Science and Machine Learning
  • Pattern Recognition and Predictive Modeling
  • Data Fusion and Integration
  • Data Mining and Knowledge Discovery
  • Support Vector Machines and Hybrid Methods
  • Evolutionary Computing and Optimization
  • Deep Learning and Neural Network Applications
  • Fuzzy Computing and Uncertainty in Data Analysis
  • Feature Selection and Granular Computing
  • Data Science Applications and Case Studies
  • Process Mining and Workflow Analytics