SCOPE

The purpose of the International Conference on Data Science, Technology and Applications (DATA) is to bring together researchers, engineers and practitioners interested on databases, big data, data mining, data management, data security and other aspects of information systems and technology involving advanced applications of data.

Papers describing advanced methodologies, prototypes, systems, tools and techniques and general survey papers indicating future directions are also encouraged. Papers describing original work are invited in any of the areas listed. Accepted papers, presented at the conference by one of the authors, will be published in the conference proceedings. Acceptance will be based on quality, relevance and originality. Both full research papers and work-in-progress are welcome. There will be both oral and poster sessions.

Special sessions, dedicated to case-studies and commercial presentations, as well as demos or tutorials dedicated to technical/scientific topics are also envisaged: companies interested in presenting their products/methodologies or researchers interested in holding a tutorial, workshop or special session are invited to contact the conference secretariat or visit the conference website.

CONFERENCE AREAS

Each of these topic areas is expanded below but the sub-topics list is not exhaustive. Papers may address one or more of the listed sub-topics, although authors should not feel limited by them. Unlisted but related sub-topics are also acceptable, provided they fit in one of the following main topic areas:
1. BUSINESS ANALYTICS
2. DATA SCIENCE
3. DATA MANAGEMENT AND QUALITY
4. BIG DATA
5. DATABASES AND DATA SECURITY

AREA 1: BUSINESS ANALYTICS

    Business Intelligence
    Statistics Exploratory Data Analysis
    Text Analytics
    Web Analytics
    Change Detection
    Data Analytics
    Data Warehouse Management
    Decision Support Systems
    Dimensional Modelling
    Information Integration
    Information Retrieval
    Semi-Structured and Unstructured Data
    Data Governance

AREA 2: DATA SCIENCE

    Data Fusion
    Pattern Recognition
    Predictive Modelling
    Support Vector Machines
    Data Science
    Data Mining
    Deep Learning
    Evolutionary Computing and Optimization
    Feature Selection
    Fuzzy Computing
    Hybrid Methods
    Neural Network Applications
    Process Mining
    Granular Computing

AREA 3: DATA MANAGEMENT AND QUALITY

    Architectural Concepts
    Mobile Data Management
    Modeling and Managing Large Data Systems
    Open Data
    Organizational Concepts and Best Practices
    Smart Cities and Urban Data Analytics
    Transparency in Research Data
    City Data Management
    Data and Information Quality
    Data Management for Analytics
    Data Modeling and Visualization
    Data Structures and Data Management Algorithms
    Data Virtualization
    Industry 4.0
    Management of Sensor Data
    Linked Data
    Semantic Web Technologies for Data Management

AREA 4: BIG DATA

    Big Data Applications
    New Computational Models for Big Data
    New Data Standards
    Social Data Analytics
    Social Web Search and Mining
    Big Data as a Service
    Big Data Infrastructure
    Big Data Search and Mining
    Big Data Security, Privacy and Trust
    Cloud/Grid/Stream Computing for Big Data
    Data and Information Quality for Big Data
    Deep Learning and Big Data
    Information Visualization
    Edge Computing
    In-Network Data Processing
    Energy-Efficient Computing
    In-Memory Databases

AREA 5: DATABASES AND DATA SECURITY

    Data Integrity
    WWW and Databases
    Data Privacy and Security
    Database Architecture and Performance
    Large Scale Databases
    Mobile Databases
    NoSQL Databases
    Object-Oriented Database Systems
    Open Source Databases
    Query Processing and Optimization