About the Journal

Operating as an open-access, peer-reviewed international journal, CompData is devoted to the science, engineering, and application of data at scale. Spanning the full data lifecycle—from acquisition and storage to processing, analysis, and interpretation—the journal prioritizes scalable methods, algorithmic innovation, and reproducible research. CompData recognizes that data’s real value lies in the insights it generates, and thus encourages both methodological advances in machine learning, statistics, and optimization, and domain-driven applications across science, engineering, and society.

Focus and Scope

CompData covers theoretical foundations, computational methods, and practical applications of large-scale data analysis. The journal addresses algorithmic challenges in processing massive datasets, statistical modeling, and machine learning, alongside infrastructure for scalable data management and analytics. Topics include but are not limited to:

  • Scalable machine learning and data mining
  • Deep learning for structured and unstructured data
  • Statistical modeling and probabilistic inference
  • Big data infrastructure and distributed systems
  • Data quality, integration, and governance
  • Data streams and real-time analytics
  • Graph analytics and network science
  • Natural language processing and text mining
  • Time series and spatial-temporal data analysis
  • Recommender systems and personalization
  • Anomaly detection and predictive modeling
  • Privacy-preserving data analysis and federated learning
  • Reproducibility, benchmarking, and open science
  • Visualization and exploratory data analysis
  • Ethical data practices and fairness in algorithmic systems

CompData welcomes original research, reviews, case studies, perspectives, and opinions that advance data science theory or practice, and encourages interdisciplinary work bridging computer science, statistics, applied mathematics, and domain sciences—particularly methodologies that address real-world data challenges at scale.

CompDataOpen Access

CompData

CompData is owned by Sin-Chn Scientific Press Pte. Ltd.