Data Sciences, Technology, and Applications (DSTA)

Topics include, but not limited to the following:

  • Data Sciences
    • Machine Learning technologies
    • Big Data technologies
    • Data Mining
    • Information Theoretic Learning
    • Statistical Learning Theory
    • Artificial Neural Networks
    • Fuzzy logic
    • Support Vector Machines and Rough sets.
    • Dimensionality Reduction
  • Data Management
    • Multi-Dimensional Data Models/Indices/Database Systems
    • Multi-Media Databases
    • No-SQL/Big-Table Database Systems Real-Time Analytics
    • Data Stream Models
    • Languages for Stream and continuous Query
    • Clustering from Data Streams
    • Association, Decision Trees, and Decision Rules from Data Streams
    • Bayesian networks from Data Streams
    • Feature Selection from Data Streams
    • Visualization Techniques for Data Streams
    • Incremental on-line Learning Algorithms
    • Distributed Stream Mining
    • Social Network Stream Mining
    • Cooperative Database Systems and Workflow Management
    • Data Warehousing, Data Cubes, and Aggregate Processing
    • Disk Arrays and Tertiary Storage Systems for Very Large Databases
    • Knowledge-based System & Representation
    • Mobile Data Management and Mobile Database Systems
    • Scientific, Biological and Bioinformatics Data Management and Mining
    • Semi-Structural Data Management, Meta Data, and XML
    • Spatial and Temporal databases and data mining
    • Statistical and Historical databases and data mining

For information about the submission details and instructions please visit the submission page here.