We invite the submission of papers describing innovative and original research contributions in the areas of data management, data science, machine learning and AI. Papers can range from theoretical contributions to systems and algorithms to experimental research and benchmarking. The research track invites two type of submissions:
- Full papers: 8 pages + (unlimited) references.
- Short papers: 4 pages + (unlimited) references.
The goal of the short papers is to provide a venue for innovative ideas such as engineered solutions, exciting work-in-progress or even negative results that would be interesting to the broader community.
The review process will take place in two stages.
- In the first stage of the review, papers will be grouped as Accept, Major Revision or Reject.
- In the second stage of the review, authors can revise and resubmit Major Revision papers. They will then be regrouped as Accept or Reject.
Authors of accepted papers will get an opportunity to showcase their work as an oral presentation. Accepted papers will appear in the proceedings of the conference, which will be published in ACM Digital Library (approval pending).
Topics of interest include, but are not limited to the following:
- Data Management: Transaction processing, query processing and optimisation, indexing and storage, distributed data platforms, spatio-temporal databases, RDBMS, NoSQL systems, key-value stores, cloud data management, big data systems, data systems for machine learning, Scientific databases, data cleansing, data provenance, data analytics, data integration, performance benchmarking, database tuning, graph database management, data streams management, uncertain and probabilistic databases, crowdsourcing, data warehousing and OLAP, database usability, data management using modern hardware, security and privacy.
- Data Science, Machine Learning and AI: Data discovery, data preprocessing and wrangling, Classification and regression, parallel and distributed learning, semi- and unsupervised learning, matrix and tensor methods, graph mining, network analytics, reinforcement learning, feature engineering, deep learning, Bayesian methods, time series analysis, optimization, graphical models, relational models, text analytics and NLP, information retrieval, knowledge representation, knowledge-based systems, human-in-the-loop learning, planning and reasoning, ML for mobiles and other resource constrained environments, data mining, causality, fairness, accountability and transparency, interpretability, data visualisation.
- Applications: Social network analysis, recommender systems, online advertising, bioinformatics, computational neuroscience, systems biology, multimedia processing, crowdsourcing, robotics and autonomous systems, analytics on sensor networks and IoT, computer vision, surveillance/monitoring and anomaly detection in networked systems, urban computing, and technology for emerging markets.
Sharing and Reproducibility: To enable reproducibility and data reuse, authors are encouraged to share artefacts including software, algorithms, protocols, code, datasets and other useful materials related to the research.
Additionally, please see this page to help you decide between the Research Track and Applied Data Science Track.
Please read the Dual submission, Plagiarism and Conflict of Interest policies before finalising your submission.
Several technical awards are available for best paper, etc. Please see the Awards page for details.
Partial travel Grants will be available for students (both domestic and international) whose papers are accepted. Presenters will also have the option to present the papers virtually.
|All deadlines are Anywhere on Earth (AoE, UTC-1200)|
|July 3, 2022: Abstract submission|
|July 10, 2022: Submission of papers|
|September 11, 2022: First Stage Accept/Major Revision/ Reject decisions.|
|October 9, 2022: Re-submission of revised manuscripts.|
|October 30, 2022: Final Notifications of Accept / Reject decisions.|
|November 15, 2022: Camera ready due|
Please see this page for submission instructions
Research Track Chairs
- Praneeth Netrapalli, Google Research India
- Louiqa Raschid, University of Maryland, USA
- For more details, please reach out to the track chairs at [email protected]
Please see this page for program committee members.