Proceedings will be published by IEEE CS Press. Distinguished papers will be invited to  special issues of selected journals listed on the conference website.
       
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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 and 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. With this continuing data explosion, it is necessary to store and process data efficiently by utilizing enormous computing power. The importance of data intensive systems has been raised 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. Innovative programming models, high performance scalable computing platforms, efficient storage systems and expression of data requirements are at immediate need. 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

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  
       
Submission Guidelines
Submissions must include an abstract, keywords, the e-mail address of the corresponding author and should not exceed 8 pages for main conference, including tables and figures in IEEE CS format. The template files for LATEX or WORD can be downloaded here. All paper submissions must represent original and unpublished work. Each submission will be peer reviewed by at least three program committee members. Submission of a paper should be regarded as an undertaking that, should the paper be accepted, at least one
of the authors will register for the conference and present the work. Submit your paper(s) in PDF file at the submission site: http://www.swinflow.org/confs/2023/dss/submission.htm.

      
Publications
Proceedings will be published by IEEE CS Press. Distinguished papers will be invited to  special issues of selected journals listed on the conference website.  
       
General Chairs

Jamal Deen, McMaster University, Canada          
Stephen S. Yau, Arizona State University, USA

Program Chairs
Dong Yuan, The University of Sydney, Australia
Deepak Puthal, Khalifa University, UAE