About the Journal

Journal Aims
  • IJDTA aims to facilitate and support research related to database theory and application technology.
  • Our Journal provides a chance for academic and industry professionals to discuss recent progress in the area of database theory and application technology.
Journal Topics
  • Advanced databases (object-oriented DB, web-based DB, multimedia DB, temporal and spatial
  • Biomedical and healthcare data mining privacy
  • Classification and Ranking
  • Clustering
  • Cryptographic tools for privacy preserving data mining
  • Data Analysis
  • Data and Knowledge Visualization
  • Data integration and interoperability
  • Data management for ubiquitous and mobile computing
  • Data mining and information extraction
  • Data modeling and architectures
  • Data provenance
  • Data quality
  • Data security, privacy and trust
  • Data streaming
  • DB, deductive and active DB, etc.
  • Dynamic Data Mining
  • Heterogeneous databases
  • Inference and disclosure control for data mining
  • Integration of Data Warehousing
  • Interactive and Online Mining
  • Intermittently connected data
  • KDD Process and Human Interaction
  • Mining Trends, Opportunities or Risks
  • OLAP and Data Mining
  • Parallel and Distributed Data Mining
  • Physical database design and performance evaluation
  • Privacy and security when mining outsourced data
  • Privacy preserving data aggregation and integration
  • Privacy Preserving Data Mining
  • Privacy threats due to data mining
  • Query processing and optimisation
  • Reliability and Robustness Issues
  • Scientific Databases
  • Security and privacy in spatio-temporal data mining
  • Semantic web and ontologies
  • Semi-structured data, metadata
  • Social and mathematical statistics
  • Software Warehouse and Software Mining
  • Temporal Data
  • Text Mining
  • Trust management for data mining
  • Web Data and the Internet
  • XML and databases, web services
  •  Others