SCOPE of DSIT 2024

DSIT 2024 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