Conference Information
ICDATA 2020: International Conference on Data Science
Submission Date:
2020-06-08 Extended
Notification Date:
Conference Date:
Las Vegas, Nevada, USA
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Call For Papers

You are invited to submit a paper for consideration. All accepted papers will be published in printed conference books/proceedings (each with a unique international ISBN number) and will also be made available online. The proceedings will be indexed in science citation databases that track citation frequency/data. In addition, like prior years, extended versions of selected papers (about 40%) will appear in journals and edited research books; publishers include, Springer, Elsevier, BMC, and others).

The Congress is composed of a number of tracks (joint-conferences, tutorials, sessions, workshops, poster and panel discussions); all will be held simultaneously, same location and dates: July 27-30, 2020. The complete list of CSCE joint conferences can be found here. ICDATA is part of the Congress.

SCOPE: Submitted papers should be related to Data Science, Data Mining, Machine Learning and similar topics.

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

Data Mining/Machine Learning Tasks

    Time series forecasting
    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“

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

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

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

Data Mining Applications

        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

We particularly encourage submissions of industrial applications and case studies from practitioners. These will not be evaluated using solely theoretical research criteria, but will take general interest and presentation into consideration.
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 (examples: data mining in education, marketing, finance and financial services, business applications, medicine, bioinformatics, biological sciences, science and technology, industry and government, …)

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

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

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, …

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
    Stream Data Management
    Scientific Data Management

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

Privacy in the Era of Big Data

    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

Applications of Big Data

    Big Data as a Service
    Big Data Analytics in e-Government and Society
    Applications in Science, Engineering, Healthcare, Visualization, Business, Education, Security, Humanities, Bioinformatics, Health Informatics, Medicine, Finance, Law, Transportation, Retailing, Telecommunication, all Search-based applications, …
Last updated by Dou Sun in 2020-05-23
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