JCST CFP: Special Section on Scalable Data Science

Call for Papers 

 Special Section on ”Scalable Data Science” 

AIMS AND SCOPE 

Data science research targets the data life cycle of real-world applications, studying phenomena at scale, complexities, and granularities never before possible. This data life cycle encompasses databases and data engineering often leveraging statistical and machine learning methods, and in many instances, using massive and heterogeneous collections of potentially noisy datasets. This special issue calls for papers that are expected to focus on data-intensive components of data science pipelines (including data discovery, data cleaning & integration, data labeling, data visualization, etc.); and solve problems in areas of interest to data management community (e.g., data curation, optimization, performance, storage, systems). 

Submissions are expected to describe: (a) deployed solutions to data science pipelines and/or (b) fundamental experiences and insights from evaluating real-world data science problems and/or theories of data-centric machine learning problems. We expect that the related systems and/or datasets will be accessible for the data management research community in order to promote future research directions.

This special section of JCST journal papers will focus on new technologies and solutions related, but not limited to: 

·Data Science Theory

·Data Science Pipeline

·Data Science Algorithms 

·Data Science Tools and Systems

·Data Science Benchmark

SCHEDULE 

·Manuscript Submission: April 1, 2022

·First Revision/Reject Notification: May 1, 2022

·Revision due of revised papers: June 1, 2022

·Final Decision: July 1, 2022

·Camera-Ready: July 15, 2022

·Expected Publication: September 2022

SUBMISSION PROCEDURE 

All submissions must be done electronically through JCST's e-submission system at: 

https://mc03.manuscriptcentral.com/jcst 

with a manuscript type: "Special Section on Scalable Data Science". 

LEADING EDITOR 

- Guoliang Li (Tsinghua University, China) 

GUEST EDITORS 

- Chengliang Chai (Tsinghua University, China) 

- Nan Tang (Qatar Computing Research Institute, Hamad Bin Khalifa University, Qatar)