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DSDE 2026: International Conference on Data Storage and Data Engineering

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投稿締切日:
2026-05-10 Extended
通知日:
2026-05-25
開催日:
2026-06-12
開催地:
Chengdu, China
開催回数:
9
QUALIS: B4   閲覧: 33736   フォロー: 10   参加: 0

論文募集

DSDE 2026 (International Conference on Data Storage and Data Engineering) is a QUALIS B4 conference held in Chengdu, China on 2026-06-12. The paper submission deadline is 2026-05-10 (extended). Acceptance notifications are sent on 2026-05-25.

The purpose of The 9th International Conference on Data Storage and Data Engineering (DSDE 2026) 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
最終更新：Dunn Carl（）

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関連ジャーナル

CCF正式名称インパクトファクター出版社ISSN
AIEEE Transactions on Multimedia9.7IEEE1520-9210
CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
AIEEE Transactions on Computers3.8IEEE0018-9340
CFuture Generation Computer Systems6.1Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.9Elsevier0167-8655
BPattern Recognition7.6Elsevier0031-3203
IEEE Access3.6IEEE2169-3536
AIEEE Transactions on Dependable and Secure Computing7.5IEEE1545-5971

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