The Conference on Information and Knowledge Management (CIKM) provides a unique venue for industry and academia to present and discuss state-of-the-art research on artificial intelligence, search and discovery, data mining and database systems, all at a single conference. CIKM is uniquely situated to highlight technologies and insights that materialize the big data and artificial intelligence vision of the future. CIKM 2021 will take place online in a lively and interactive manner.
AUTHORS TAKE NOTE: The official publication date is the date the proceedings are made available in the ACM Digital Library. This date may be up to two weeks prior to the first day of your conference. The official publication date affects the deadline for any patent filings related to published work. (For those rare conferences whose proceedings are published in the ACM Digital Library after the conference is over, the official publication date remains the first day of the conference.)
All deadlines are at 11:59pm in the Anywhere on Earth timezone.
We seek demonstrations that showcase exciting new technologies and early prototypes within the scope of CIKM, as well as case studies from more mature systems with innovative features and functionalities. We welcome submissions on all topics in the general areas of artificial intelligence, data science, databases , information retrieval, and knowledge management.
Demo papers should describe the intended audience, point out the innovative aspects of the system being presented, and explain how those aspects contribute to the state of the art in knowledge, data, and information management. The submissions must also make clear what the audience will experience during the demo, what kind of functionality is supported, user scenarios, interface and interaction options provided, and how it is compared with existing systems (if any). Submissions that are case studies should also explain the case being demonstrated. Authors may also include a URL of a screencast video (up to 10 minutes), screenshots, and additional external material (e.g., shared code on GitHub). A demo paper must be no more than 4 pages long, plus unlimited references.
Manuscripts should be submitted to CIKM’21’s Easychair site in PDF format, using the ACM sigconf template, see https://www.acm.org/publications/proceedings-template. At least one author of each accepted paper must register to present the work as scheduled in the conference program. Additional details for running the online conference will be published on the website shown above.
The review of the demo papers will be single-blind. All submissions will be reviewed by the Program Committee of the demos track, who will evaluate the novelty of the technical features and/or research being presented, the research and/or development challenges, its expected impact, and its timeliness and relevance for the CIKM audience of practitioners and researchers. Demo papers that describe case studies from mature systems will also be evaluated on the relevance of the system and innovative features and functionalities that are being included in the demonstration.
Accepted demo papers will be part of the main conference proceedings.
It is not allowed to submit papers that are identical (or substantially similar) to versions that have been previously published, or accepted for publication, or that have been submitted in parallel to other conferences. Such submissions violate our dual submission policy. There are several exceptions to this rule:
Submission is permitted for papers presented or to be presented at conferences or workshops without proceedings, or with only abstracts published.
Submission is permitted for papers that have previously been made available as a technical report (or similar, e.g., in arXiv). In this case, the authors should not cite the report, so as to preserve anonymity.
All authors and participants must adhere the the ACM discrimination policy. For full details, please visit this site:
For more information, contact the appropriate chairs:
Demo Track Email: email@example.com