Scope & Topics

International Conference on Data Science and Applications (DSA 2020) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Data Science and Applications. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Data Science & Applications

Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in Data Science & Applications.

Topics of interest include, but are not limited to, the following:

Data mining foundations

Parallel and Distributed Data Mining Algorithms
Data Streams Mining, Graph Mining, Spatial Data Mining
Text video, Multimedia Data Mining, Web Mining
Pre-Processing Techniques, Visualization
Security and Information Hiding in Data Mining
 
Data mining Applications

Databases
Bioinformatics
Biometrics
Image Analysis
Financial Modeling
Forecasting, Classification, Clustering
Social Networks, Educational Data Mining
 
Knowledge Processing

Data and Knowledge Representation
Knowledge Discovery Framework and Process, Including Pre- and Post-Processing
Integration of Data Warehousing
OLAP and Data Mining,
Integrating Constraints and Knowledge in the KDD Process
Exploring Data Analysis
Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge
Statistical Techniques for Generation a Robust
Consistent Data Model
Interactive Data Exploration / Visualization and Discovery
Languages and Interfaces for Data Mining
Mining Trends, Opportunities and Risks
Mining from Low-Quality Information Sources
 
Data Mining & Machine learning Tasks

Machine Learning Applications
Learning in knowledge-intensive systems
Learning Methods andanalysis
Learning Problems
Deep Learning
 
Big Data

Big Data Algorithms
Big Data Fundamentals
Infrastructures for Big Data
Big Data Management and Frameworks
Big Data Search
Big Data security
Big Data Applications