DSIT 2026 welcomes relevant paper submissions from researchers in academia, industry, and government, such as students, engineers, practitioners, scientists, and policy makers. We welcome paper submissions with original technical and scientific research results in relevant topics.

Main Topics of Interest:

Data Science:

Theory of Data Science 	
Foundations of Data Science
Data Standards and Protocols 	
Data Structures and Algorithms
Data Metrics and Metrology 	
Data and Knowledge Representation
Big Data Characteristics and Solutions 	
Data Semantics and Ontology
Parallel and Distributed Data Computing 	
Data and Information Visualization
Database Management 	
Deep Learning and Applications
Supervised Learning / Semi-Supervised Learning 	
Unsupervised / Self-Supervised Learning
Feature Selection and Representation 	
Dimensional Reduction Theory and Practice
Graph Mining / Network Analysis 	
Stream Data Processing Algorithms / Distributed Data Mining Algorithms
Text and Web Data Mining 	
Temporal, Spatial, and High Dimensional Databases
Multimedia Data Mining 	
Smart City Data Management
Educational Data Mining 	
Machine Learning and Its Applications
Data Science Applications 	
Natural Language Processing

Data and Information Technology:

Data and Information Privacy Technology 	
Data-Intensive Information Technology Applications
Unstructured / Structured Database Technology 	
Data-Intensive Software Engineering
Data Management Technology in Smart City 	
Information Retrieval and Recommendation Systems
Big Data Algorithms and Technology 	
Information Security and Assurance in Big Data
Cloud/Grid Computing Technology in Big Data 	
Real-time System Technology in Big Data
High-Performance Data Computing Technology 	
Compliance and Governance for Big Data
Big Data Meet Green Challenges 	
Next-Generation Big Data Platform and Technology
Data Ecosystem Concepts and Technology 	
Trust, Fairness, Diversity, and Transparency in Big Data
Scientific / Commercial / Industrial Data System 	
Scientific / Commercial / Industrial Case Studies in Big Data