Call for Research Track Papers

Research Track

We invite the submission of papers describing innovative and original research contributions in the areas of data science, data management, data mining, machine learning, and artificial intelligence. 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 full papers must present their work as both a talk and a poster at the conference; accepted short papers must present their work as a poster. All accepted papers will appear in the proceedings of the conference, which will be published in the ACM Digital Library.

Topics of Interest include, but are not limited to, the following:

  • AI, ML, and Data Mining: Classification and regression; Knowledge discovery; knowledge representation and knowledge-based systems; data preprocessing and wrangling; feature engineering; reinforcement learning; deep learning; Bayesian methods; time series analysis; optimization; graphical models; statistical relational learning; matrix and tensor methods; parallel and distributed learning; semi- and unsupervised learning; graph mining; network analytics; text analytics and NLP; information retrieval; learning-based computer vision; multimodal learning and analytics; human-in-the-loop learning; planning and reasoning; ML for mobiles and other resource-constrained environments; federated learning; AutoML; causality; fairness, accountability, transparency, and explainability in AI/ML; weak supervision and data augmentation; new benchmark tasks and datasets for AI/ML/data mining.
  • Data Science-based Computing: Social network analysis; social computing; recommender systems; computational advertising; bioinformatics; computational neuroscience; multimedia processing; crowdsourcing; robotics and autonomous systems; analytics on sensor networks and IoT; surveillance/monitoring and anomaly detection in networked systems; urban computing; technology for emerging markets.
  • Data Management: Data models and query languages; query processing and optimisation; indexing and storage systems; key-value and NoSQL stores; transaction processing; blockchains; distributed and cloud data systems; big data and dataflow systems; data warehousing and OLAP; spatio-temporal and graph data management; scientific and multimedia databases; data management for ML/AI workloads; ML/AI methods for data management; data cleaning and integration; data provenance; data streams; uncertain and probabilistic databases; data crowdsourcing; database usability and query interfaces; data visualisation and visual analytics; data management on modern hardware; data privacy, security, and ethics; performance tuning and benchmarking; new benchmark tasks and datasets for data management.

Sharing and Reproducibility

To enable reproducibility and data reuse, authors are encouraged to share artifacts 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.

Double Anonymity Requirement

CODS-COMAD 2024 will be using double-anonymous reviewing for Research Track papers (but not the ADS Track papers). Since this is the first time this requirement is being added, please review the instructions below carefully.

Authors’ names and affiliations must not appear on the title page or anywhere else in the submission. Funding sources must not be acknowledged anywhere in the submission. Research group members, or other colleagues or collaborators, must not be acknowledged anywhere in the submission. Only after acceptance at the camera ready stage should the author list, acknowledgments, and funding sources be added to the paper.

The file names of any documents submitted must not identify the authors of the submission. Source file naming must also be done with care, to avoid identifying the authors’ names in the submission’s associated metadata.

To avoid compromising the double-anonymity requirement, we request that the authors refrain from publicizing and uploading versions of their submitted manuscripts to pre-publication servers, such as arXiv, and other online forums during the reviewing period. If a version of a submission already resides on a pre-publication server, such as arXiv, the authors do not need to remove it before submitting to CODS-COMAD.

Be careful when referring to related past work, particularly your own, in the paper. Authors must refer to their own past work in the third person. This allows setting the context for your submission, while at the same time preserving anonymity. Do not omit referring to your own past related work because that could reveal your identity by negation. Limit self-references to only the essential ones. Extended versions of the submitted paper (e.g., technical reports or URLs for downloadable versions) must not be referenced. Many ACM conferences have successfully followed double anonymity for decades to offer more equity for all authors in the reviewing process. Common sense and careful writing can go a long way toward preserving double anonymity without diminishing the quality or impact of a paper. It is the responsibility of the authors to do their very best to preserve double anonymity.

Papers that do not follow the guidelines here, or otherwise potentially reveal the identity of the authors, are subject to Desk Rejection. No exceptions will be made for Research Track papers. If the authors of a submission feel that double anonymity needs to be violated, for example to reveal the identity of a publicly deployed system, they may consider submitting their work to the ADS Track that does not impose this double anonymity requirement.

Important dates

All deadlines are Anywhere on Earth (AoE, UTC-1200)

  • July 10, 2023: Abstract submission
  • July 17, 2023: Submission of papers
  • September 11, 2023: First stage decision notifications (Accept/Reject/Revision)
  • October 11, 2023: Submission of revised papers
  • November 3, 2023: Final decision notifications (Accept/Reject)
  • November 30, 2023: Camera ready due

Submission Instructions

Please see this page for submission instructions

Research Track Chairs

Arun Kumar, University of California, San Diego
Richa Singh, IIT Jodhpur
For more details, please reach out to the track chairs at [email protected]

Program Committee

Please see this page for program committee members.