Research Track vs Applied Data Science Track

Research Track and Applied Data Science Track

CODS-COMAD 2022 will accept research papers in two tracks: 1. Regular Research Track, and 2. Applied Data Sciences (ADS) Track. The latter is a new addition from the 2021 edition. Authors are invited to submit their research papers to one of these two tracks.

The Research Track solicits submissions that present original and innovative research challenges and solutions. Papers can range from theoretical contributions to systems and algorithms to experimental research. The Applied Data Science (ADS) Track is distinct from the Research Track in that submissions should focus on applied challenges addressing real-world problems and / or systems demonstrating tangible impact/value in their respective domains. The ADS Track submissions should highlight the target user needs and potential users.

For example, a research track paper may address some novel challenges around question-answering that may apply across many domains, languages, etc. An ADS Track paper may have a focus on a corpus from the bio-chemical domain, and may address problems that are specific to that domain. The target users may be bio-chemical engineers and researchers. As another example, a deployment of an app or website that helps farmers keep track of market prices of various products would be suitable for the ADS track, and farmers or agricultural traders may benefit from the solutions.

It is the authors’ responsibility to submit their paper into the appropriate track. Papers that do not satisfy the requirements of the track may be rejected without a review. For example, a paper that does not address a novel research problem or make generalizable contributions may not be suitable for the research track. On the other hand, a paper that does not target a specific real world user case and group of users may be rejected without review from the ADS Track. It is strongly recommended that authors read the Call for Papers for both tracks carefully before choosing a track.