Introduction

As an interdisciplinary area, Data Science draws scientific inquiry from a broad range of subject areas such as statistics, mathematics, computer science, machine learning, optimization, signal processing, information retrieval, databases, cloud computing, computer vision, natural language processing, etc. Data Science is on the essence of deriving valuable insights from data. It is emerging to meet the challenges of processing very large datasets, i.e. Big Data, with the explosion of new data continuously generated from various channels, such as smart devices, web, mobile and social media.

Data Systems are posing many challenges in exploiting parallelism of current and upcoming computer architectures. Data volumes of applications in the fields of sciences and engineering, finance, media, online information resources, etc. are expected to double every two years over the next decade and further. The importance of data intensive systems has been raising and will continue to be the foremost fields of research. This raise brings up many research issues, in forms of capturing and accessing data effectively and fast, processing it while still achieving high performance and high throughput, and storing it efficiently for future use.

DSS (Data Science and Systems) was created to provide a prime international forum for researchers, industry practitioners and domain experts to exchange the latest advances in Data Science and Data Systems as well as their synergy.

Scope and Topics

Topics of particular interest include, but are not limited to:
    
        I. Data Science

        - Foundational Theories of Data Science
        - Data Classification and Taxonomy
        - Data Metrics and Metrology
        - Data Analytics
        - Complex Network Analysis and Mining
        - Security, Privacy and Trust in Data

        II. Data Processing Technology

        - Data Representation and Processing
        - Machine Learning and Deep Learning
        - Graph Neural Networks
        - Semi-supervised and Unsupervised Learning
        - Statistical, Mathematical and Probabilistic Modeling and Theories
        - Information visualization
        - Data Visualization

        III. Data Systems

        - Storage and File Systems
        - High Performance Access Toolkits
        - Compiler and Runtime Support
        - Real-time Data Intensive Systems
        - Multi/Many-Core Platforms
        - Big Data and Cloud Computing

        IV. Data Applications

        - Business and Finance Applications
        - Industrial Data Applications
        - Bioinformatics Applications
        - Healthcare and Medical Services
        - Applications in Soil and Water
        - HPC Systems for Data Applications
        - Future Data Applications