ACM SIGMOD Reproducibility 2021 ------------------------------------------- All authors of research papers in SIGMOD 2020 are invited to submit an entry by December 18, 2020. ------------------------------------------- http://reproducibility.sigmod.org/ The material needed for the reproducibility will be submitted at the CMT website: https://cmt3.research.microsoft.com/User/Login?ReturnUrl=%2FSIGMODRepro2021 The submission at the CMT should contain two PDF files. The original accepted paper and a PDF with at least the following information: (1) a link to download the code, data, and scripts; and (2) a step by step description on how to use the scripts for (a) code compilation, (b) data generation, and (c) repeating the paper experiments. In addition, please include a link to the the ACM digital library page for the paper and a detailed description of the hardware used. A readme template can be found here: http://reproducibility.sigmod.org/documents/readme.txt. ------------------------------------------- What is SIGMOD Reproducibility? ------------------------------------------- SIGMOD Reproducibility has three goals: * Highlight the impact of database research papers. * Enable easy dissemination of research results. * Enable easy sharing of code and experimentation set-ups. In short, the goal is to assist in building a culture where sharing results, code, and scripts of database research is the norm rather than an exception. The challenge is to do this efficiently, which means building technical expertise on how to do better research via creating repeatable and shareable research. The SIGMOD Reproducibility committee is here to help you with this. ------------------------------------------- Why should I be part of this? ------------------------------------------- You will be making it easy for other researchers to compare with your work, to adopt and extend your research. This instantly means more recognition directly visible through ACM badges for your work and higher impact. Taking part in the SIGMOD Reproducibility process enables your paper to take the ACM Results Reproduced label. This is embedded in the PDF of your paper in the ACM digital library. There is an option to also host your data, scripts and code in the ACM digital library as well to make them available to a broad audience, which will award the ACM Artifacts Available label. ACM Results Reproduced label The main results of the paper have been obtained in a subsequent study by a person or team other than the authors, using, in part, artifacts provided by the author. ACM Artifacts Available label Author-created artifacts relevant to this paper (data,code,scripts) have been placed on a publicly accessible archival repository. A DOI or link to this repository along with a unique identifier for the object is provided. Both the ACM Results Reproduced label and the ACM Artifacts Available label are visible in the ACM digital library. Successful papers will be advertised at DBworld and the list of award winners are maintained in the main SIGMOD website. In addition, the official ACM Digital Library maintains all reproduced SIGMOD papers, and when possible it will contain the experimentation material of SIGMOD with available artifacts. Important Dates: - Submisison of the reproducibility package due : December 18, 2020 ACM SIGMOD Reproducibility Committee Chair: Manos Athanassoulis, Boston University Advisory Committee * Juliana Freire, New York University, USA * Stratos Idreos, Harvard University, USA * Dennis Shasha, New York University, USA Committee Members * Angelos-Christos Anadiotis, Ecole Polytechnique, France * Raja Appuswamy, Eurecom, France * Joy Arulraj, Georgia Institute of Technology, USA * Dmytro Bogatov, Boston University, USA * Renata Borovica-Gajic, University of Melbourne, Australia * Shimin Chen, Institute of Computing Technology, Chinese Academy of Sciences, China * Raul Castro Fernandez, University of Chicago, USA * Thomas Heinis, Imperial College, UK * Asterios Katsifodimos, Delft University of Technology, Netherlands * Andreas Kipf, Massachusetts Institute of Technology, USA * Wolfgang Lehner, TU Dresden, Germany * John Paparrizos, University of Chicago, USA * Ilia Petrov, Reutlingen University, Germany * Mirek Riedewald, Northeastern University, USA * Yingjun Wu, Amazon, USA * Dong Xie, Penn State University, USA * Huanchen Zhang, Snowflake, USA * Kostas Zoumpatianos, Snowflake, USA