How to Reject a VIS Paper, or Not?

While it is necessary for most (if not all) visualization and visual analytics (VIS) publication venues to use peer review processes to assure the quality of the papers to be published, it is also necessary for the VIS community to appraise and improve the quality of peer review processes from time to time. In recent years, rejecting a VIS paper seems to have become rather easy, as many rejection reasons are available to criticize a given paper. In this article, we analyze possible causes of this phenomenon and recommend possible remedies. In particular, over the past decades, the visualization field has rapidly grown to include many types of contributions and specialized research areas. Given this large landscape of topics, we need to ensure that good contributions within each area are reviewed properly, published, and built upon to make significant advancement in the area concerned. Therefore, it is crucial that our review process applies specific criteria for each area and does not expect individual publications to satisfy many review criteria designed for other areas. In this way, we hope VIS review processes will enable more VIS research with X factors (original, innovative, significant, impactful, rigorous, insightful, or inspirational) to be published promptly, allowing VIS researchers and practitioners to make even more impactful contributions to data sciences.