IEEE International Workshop on Data Quality for Intelligent Systems (DQIS 2022)

Description

Both structured and unstructured data are exponentially produced in massive amounts, with increasing volume, velocity, variety, and value. Big data are harvested for building intelligent systems for supporting a broad array of applications from biomedicine, healthcare, education to smartcity and autopilot. Data quality could signifcantly impact the performance of the intelligent system that is built on it. This special track will explore methodologies, tools and frameworks that have been or need to be developed to evaluate data quality and enhance data for building high quality intelligent systems.

Topics

The list of topics includes, but is not limited to:

• Data Quality Evaluation: Dimensions and Measurement
• Quality Evaluation of Knowledge Graph
• Quality Evaluation of Ontology System
• Exploratory Data Analysis
• Data Security and Privacy
• Data Augmentation
• Data Quality Management
• Fairness in Machine Learning (e.g. how to handle missing data)
• Ethics in Machine Learning (e.g. biased data leads to biased results)
• Data Quality Management in Machine Learning Lifecycle
• High-quality Machine Learning Dataset Construction in High-stake Domains
• Strategies for Solid Model Testing in Machine Learning and Deep Learning
• Trustworthy in Machine Learning

Submission

Authors are invited to submit original unpublished research papers as well as industrial practice papers. Simultaneous submissions to other conferences are not permitted. Detailed instructions for electronic paper submission, panel proposals, and review process can be found at https://qrs22.techconf.org/submission.

The length of a camera ready paper will be limited to eight pages. Each paper should include a title, the name and affiliation of each author, a 150-word abstract, and up to 6 keywords. Shorter version papers (up to four pages) are also allowed.

All the papers accepted by the workshop will be invited to submit an extended version to the special issue “Data Quality for Big Data and Machine Learning” on Frontiers in Big Data (SCIE journal). For more information, please visit the special issue website: https://www.frontiersin.org/research-topics/35131/data-quality-for-big-data-and-machine-learning

All papers must conform to the QRS conference proceedings format (PDF | Word DOCX | Latex) and Submission Guideline set in advance by QRS 2022 co-located workshops. At least one of the authors of each accepted paper is required to pay full registration fee and present the paper at the workshop. Arrangements are being made to publish selected accepted papers in reputable journals. Submissions must be in PDF format and uploaded to the conference submission site.

Program Chairs

Jeffrey Saltz

Syracuse University

Junhua Ding

University of North Texas

Co-Chairs

Haihua Chen

University of North Texas

Lei Li

Beijing Normal University

Program Committee

Previous DQIS

• DQIS 2021 - Hainan Island (in conjunction with QRS 2021)
• DQIS 2020 - Macau (in conjunction with QRS 2020)