7th International Conference on Data Storage and Data Engineering (DSDE 2024) will take place during February 27-29, 2024, in Shanghai, China. Organized by Donghua University, China, this conference serves as a platform for researchers, academics, industry professionals, and experts in the field of data storage and data engineering to come together and exchange knowledge, ideas, and insights.
Data storage and data engineering are critical components in today's rapidly evolving digital landscape. As the volume and complexity of data continue to increase exponentially, there is a growing demand for innovative solutions, advanced techniques, and efficient strategies to manage and analyze data effectively. During this conference, we aim to foster meaningful discussions, collaborations, and the dissemination of cutting-edge research in various areas of Data Storage and Data Engineering. The conference program features keynote speeches and invited speeches from esteemed experts and technical sessions, providing valuable opportunities to engage with the latest research findings and industry trends.
The purpose of the 7th International Conference on Data Storage and Data Engineering (DSDE 2024) is to bring together researchers, engineers and practitioners interested on databases, big data, data mining, data management, data security and other aspects of information systems and technology involving advanced applications of data.
Papers describing advanced methodologies, prototypes, systems, tools and techniques and general survey papers indicating future directions are also encouraged. Papers describing original work are invited in any of the areas listed.
Each of these topic areas is expanded below but the sub-topics list is not exhaustive. Unlisted but related sub-topics are also acceptable, provided they fit in one of the following main topic areas:
Track 1: Big Data and Analytics
▪ Big Data Applications and Case Studies
▪ Computational Models for Big Data
▪ Data Standards and Interoperability
▪ Social Data Analytics and Web Mining
▪ Big Data as a Service
▪ Big Data Infrastructure and Cloud/Grid/Stream Computing
▪ Big Data Search, Mining, and Visualization
▪ Big Data Security, Privacy, and Trust
▪ Deep Learning for Big Data
▪ Energy-Efficient Computing and In-Memory Databases
▪ Information Visualization and Visual Analytics
▪ Edge Computing and In-Network Data Processing
Track 2: Data Management and Quality
▪ Architectural Concepts for Data Management
▪ Mobile Data Management and IoT Data
▪ Large Data Systems Modeling and Management
▪ Open Data and Transparency in Research Data
▪ Smart Cities and Urban Data Analytics
▪ Data and Information Quality Management
▪ Data Management for Analytics
▪ Data Modeling, Visualization, and Virtualization
▪ Industry 4.0 and Sensor Data Management
▪ Linked Data and Semantic Web Technologies
▪ Organizational Concepts and Best Practices in Data Management
▪ City Data Management and Governance
Track 3: Data Science and Machine Learning
▪ Data Fusion and Integration
▪ Pattern Recognition and Predictive Modeling
▪ Support Vector Machines and Hybrid Methods
▪ Data Mining and Knowledge Discovery
▪ Deep Learning and Neural Network Applications
▪ Evolutionary Computing and Optimization
▪ Feature Selection and Granular Computing
▪ Fuzzy Computing and Uncertainty in Data Analysis
▪ Process Mining and Workflow Analytics
▪ Data Science Applications and Case Studies
Track 4: Databases and Data Security
▪ Database Architecture and Performance Optimization
▪ Large-Scale and Distributed Databases
▪ Mobile and NoSQL Databases
▪ Object-Oriented and Open Source Databases
▪ Query Processing and Optimization
▪ Data Integrity and Consistency
▪ Data Privacy, Security, and Confidentiality
▪ WWW and Databases
▪ Blockchain Technology for Data Storage and Security
▪ Database Management in Cloud Environments
