We accept files in .docx/.doc/.pdf/Latex Pdf format - as per the call for papers schedule.
Papers should be thoroughly checked and proofread before submission. After you have submitted your article you are unable to make any changes to it during the refereeing process—although if accepted, you will have a chance to make minor revisions after refereeing and before the final submission of your paper.

Paper content must be original and relevant to one of the many conference topics.
Authors are required to ensure accuracy of quotations, citations, diagrams, maps, and tables.
Figures and tables need to be placed where they are to appear in the text and must be clear and easy to view.
Papers must follow format according to the template which will be sent to accepted papers.

Data Science

Data Mining/Machine Learning Tasks
Regression/Classification
Time series forecasting
Segmentation/Clustering/Association
Deviation and outlier detection
Explorative and visual data mining
Web mining
Mining text and semi-structured data
Temporal and spatial data mining
Multimedia mining (audio/video)
Mining „Big Data“
Others
Data Mining Algorithms
Artificial neural networks / Deep Learning
Fuzzy logic and rough sets
Decision trees/rule learners
Support vector machines
Evolutionary computation/meta heuristics
Statistical methods
Collaborative filtering
Case based reasoning
Link and sequence analysis
Ensembles/committee approaches
Others
Data Mining Integration
Mining large scale data/big data
Data and knowledge representation
Data warehousing and OLAP integration
Integration of prior domain knowledge
Metadata and ontologies
Agent technolog ies for data mining
Legal and social aspects of data mining
Others
Data Mining Process
Data cleaning and preparation
Feature selection and transformation
Attribute discretisation and encoding
Sampling and rebalancing
Missing value imputation
Model selection/assessment and comparison
Induction principles
Model interpretation
Others
Data Mining Applications
Bioinformatics
Medicine Data Mining
Business / Corporate / Industrial Data Mining
Credit Scoring
Direct Marketing
Database Marketing
Engineering Mining
Military Data Mining
Security Data Mining
Social Science Mining
Data Mining in Logistics
Others

Data Mining Software
All aspects, modules, frameworks
Alternative and additional examples of possible topics include:
Data Mining for Business Intelligence
Emerging technologies in data mining
Computational performance issues in data mining
Data mining in usability
Advanced prediction modelling using data mining
Data mining and national security
Data mining tools
Data analysis
Data preparation techniques (selection, transformation, and preprocessing)
Information extraction methodologies >
Clustering algorithms used in data mining
Genetic algorithms and categorization techniques used in data mining
Data and information integration
Microarray design and analysis
Privacy-preserving data mining
Active data mining
Statistical methods used in data mining
Multidimensional data
Case studies and prototypes
Automatic data cleaning
Data visualization
Theory and practice – knowledge representation and discovery
Knowledge Discovery in Databases (KDD)
Uncertainty management
Data reduction methods
Data engineering
Content mining
Indexing schemes
Information retrieval
Metadata use and management
Multidimensional query languages and query optimization
Multimedia information systems
Search engine query processing
Pattern mining
Applications 
Algorithms for Big Data
Data and Information Fusion
Algorithms (including Scalable methods)
Natural Language Processing
Signal Processing
Simulation and Modeling
Data-Intensive Computing
Parallel Algorithms
Testing Methods
Multidimensional Big Data
Multilinear Subspace Learning
Sampling Methodologies
Streaming
Others
Big Data Fundamentals
Novel Computational Methodologies
Algorithms for Enhancing Data Quality
Models and Frameworks for Big Data
Graph Algorithms and Big Data
Computational Science
Computational Intelligence
Others
Infrastructures for Big Data
Cloud Based Infrastructures (applications, storage & computing resources)
Grid and Stream Computing for Big Data
High Performance Computing, Including Parallel & Distributed Processing
Autonomic Computing
Cyber-infrastructures and System Architectures
Programming Models and Environments to Support Big Data
Software and Tools for Big Data
Big Data Open Platforms
Emerging Architectural Frameworks for Big Data
Paradigms and Models for Big Data beyond Hadoop/MapReduce, …
Others
Big Data Management and Frameworks
Database and Web Applications
Federated Database Systems
Distributed Database Systems
Distributed File Systems
Distributed Storage Systems
Knowledge Management and Engineering
Massively Parallel Processing (MPP) Databases
Novel Data Models
Data Preservation and Provenance
Data Protection Methods
Data Integrity and Privacy Standards and Policies
Data Science
Novel Data Management Methods
Crowdsourcing
Stream Data Management
Scientific Data Management
Others
Big Data Search
Multimedia and Big Data
Social Networks
Data Science
Web Search and Information Extraction
Scalable Search Architectures
Cleaning Big Data (noise reduction), Acquisition & Integration
Visualization Methods for Search
Time Series Analysis
Recommendation Systems
Graph Based Search and Similar Technologies
Others
Privacy in the Era of Big Data
Cryptography
Threat Detection Using Big Data Analytics
Privacy Threats of Big Data
Privacy Preserving Big Data Collection
Intrusion Detection
Socio-economical Aspect of Big Data in the Context of Privacy and Security
Others
Applications of Big Data
Big Data as a Service
Big Data Analytics in e-Government and Society
Applications in Science, Engineering, Healthcare, ...
Others