SIGMOD Availability & Reproducibility has three goals:
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 Availability & Reproducibility committee is here to help you with this.
The SIGMOD Availability & Reproducibility effort works in coordination with the PVLDB Reproducibility to encourage the database community to develop a culture of sharing and cross-validation.
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 Availability & 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 Reusable and/or the ACM Artifacts Available 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.
Author-created artifacts relevant to this paper (data,code,scripts) are of a quality that significantly exceeds minimal functionality. They are documented, well-structured, and can facilitate reuse and repurposing.
The author-created artifacts associated with the paper 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.
All threeACM Results Reproduced,ACM Artifacts Reusable, and theACM Artifacts Availablelabels 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. The official SIGMOD Availability & Reproducibility website serves as a centralized location where researchers can learn about the reproducibility process and find all reproduced papers. We will continue to enhance the functionality and material on this website to make it attractive and useful for the community, so stop by often!
The awards are selected by the Reproducibility Awards Committee, chaired by Dennis Shasha. The committee is formed after all submissions are received so that there are no conflicts. Decisions are made based on scores that reviewers assign to each paper for all factors described above.
At first, making research shareable seems like an extra overhead for authors. You just had your paper accepted in a major conference; why should you spend more time on it? The answer is to have more impact!
If you ask any experienced researcher in academia or in industry, they will tell you that they in fact already follow the reproducibility principles on a daily basis! Not as an afterthought, but as a way of doing good research.
Maintaining easily reproducible experiments, simply makes working on hard problems much easier by being able to repeat your analysis for different data sets, different hardware, different parameters, etc. Like other leading system designers, you will save significant amounts of time because you will minimize the set up and tuning effort for your experiments. In addition, such practices will help bring new students up to speed after a project has lain dormant for a few months.
Ideally reproducibility should be close to zero effort.
Each submitted experiment should contain: (1) A prototype system provided as a white box (source, configuration files, build environment) or a black-box system fully specified. (2) Input Data: Either the process to generate the input data should be made available, or when the data is not generated, the actual data itself or a link to the data should be provided. (3) The set of experiments (system configuration and initialization, scripts, workload, measurement protocol) used to produce the raw experimental data. (4) The scripts needed to transform the raw data into the graphs included in the paper.
The central results and claims of the paper should be supported by the submitted experiments, meaning we can recreate result data and graphs that demonstrate similar behavior with that shown in the paper. Typically when the results are about response times, the exact numbers will depend on the underlying hardware. We do not expect to get identical results with the paper unless it happens that we get access to identical hardware. Instead, what we expect to see is that the overall behavior matches the conclusions drawn in the paper, e.g., that a given algorithm is significantly faster than another one, or that a given parameter affects negatively or positively the behavior of a system.
One important characteristic of strong research results is how flexible and robust they are in terms of the parameters and the tested environment. For example, testing a new algorithm for several input data distributions, workload characteristics and even hardware with diverse properties provides a complete picture of the properties of the algorithm. Of course, a single paper cannot always cover the whole space of possible scenarios. Typically the opposite is true. For this reason, we expect authors to provide a short description as part of their submission about different experiments that one could do to test their work on top of what already exists in the paper. Ideally, the scripts provided should enable such functionality so that reviewers can test these cases. This would allow reviewers to argue about how “replicable” the results of the paper are under different conditions. Replicability is not mandatory for getting the ACM Availability & Reproducibility labels. It is though the ultimate goal of this effort and is an essential criterion for the Most Reproducible Paper Award.
We do not expect the authors to perform any additional experiments on top of the ones in the paper. Any additional experiments submitted will be considered and tested but they are not required. As long as the flexibility report shows that there is a reasonable set of existing experiments, then a paper meets the flexibility criteria. What reasonable means will be judged on a case-by-case basis based on the topic of each paper and in practice all accepted papers in top database conferences meet these criteria. You should see the flexibility report mainly as a way to describe the design space covered by the paper and the design space which is interesting to cover in the future for further analysis that may inspire others to work on open problems triggered by your work.
Each paper is reviewed by one database group. The process happens in communication with the reviewers so that authors and reviewers can iron out any technical issues that arise. The end result is a short report which describes the result of the process. For successful papers the report will be hosted in the ACM digital library along with the data and code.
The goal of the committee is to properly assess and promote database research! While we expect that authors try as best as possible to prepare a submission that works out of the box, we know that sometimes unexpected problems appear and that in certain cases experiments are very hard to fully automate. The committee will not dismiss submissions if something does not work out of the box; instead, they will contact the authors to get their input on how to properly evaluate their work.
Every case is slightly different. Sometimes the Availability & Reproducibility committee can simply rerun software (e.g., rerun some existing benchmark). At other times, obtaining raw data may require special hardware (e.g., sensors in the arctic). In the latter case, the committee will not be able to reproduce the acquisition of raw data, but then you can provide the committee with a protocol, including detailed procedures for system set-up, experiment set-up, and measurements.
Whenever raw data acquisition can be produced, the following information should be provided.
Authors should explicitly specify the OS and tools that should be installed as the environment. Such specification should include dependencies with specific hardware features (e.g., 25 GB of RAM are needed) or dependencies within the environment (e.g., the compiler that should be used must be run with a specific version of the OS).
System setup is one of the most challenging aspects when repeating experiments. System setup will be easier to conduct if it is automatic rather than manual. Authors should test that the system they distribute can actually be installed in a new environment. The documentation should detail every step in system setup:
The above tasks should be achieved by executing a set o scripts provided by the authors that will download needed components (systems, libraries), initialize the environment, check that software and hardware is compatible, and deploy the system.
The committee strongly suggests using one of the following tools to streamline the process of reproducibility. These tools can be used to capture the environment, the input files, the expected output files, and the required libraries in a container-like suite. This will help both the authors and the evaluators to seamlessly rerun experiments under specific environments and settings. If using all these tools proves to be difficult for a particular paper, the committee will work with the authors to find the proper solution based on the specifics of the paper and the environment needed. Below is a list of the tools recommended by the SIGMOD Reproducibility Committee.
Given a system, the authors should provide the complete set of experiments to reproduce the paper's results. Typically, each experiment will consist of the following parts.
The authors should document (i) how to perform the setup, running and clean-up phases, and (ii) how to check that these phases complete as they should. The authors should document the expected effect of the setup phase (e.g., a cold file cache is enforced) and the different steps of the running phase, e.g., by documenting the combination of command line options used to run a given experiment script.
Experiments should be automatic, e.g., via a script that takes a range of values for each experiment parameter as arguments, rather than manual, e.g., via a script that must be edited so that a constant takes the value of a given experiment parameter.
For each graph in the paper, the authors should describe how the graph is obtained from the experimental measurements. The submission should contain the scripts (or spreadsheets) that are used to generate the graphs. We strongly encourage authors to provide scripts for all their graphs. The authors are free to choose from their favorite plotting tool using a tool such as Gnuplot, Matlab, Matplotlib, R, or Octave.
At a minimum the authors should provide a complete set of scripts to install the system, produce the data, run experiments and produce the resulting graphs along with a detailed Readme file that describes the process step by step so it can be easily reproduced by a reviewer.
The ideal reproducibility submission consists of a master script that:
... to produce a new PDF for the paper that contains the new graphs. It is possible!
A good source of dos and don’ts can be found in the ICDE 2008 tutorial by Ioana Manolescu and Stefan Manegold (and a subsequent EDBT 2009 tutorial).
They include a road-map of tips and tricks on how to organize and present code that performs experiments, so that an outsider can repeat them. In addition, the ICDE 2008 tutorial discusses good practices on experiment design more generally, addressing, for example, how to chose which parameters to vary and in what domain.
A discussion about reproducibility in research including guidelines and a review of existing tools can be found in the SIGMOD 2012 tutorial by Juliana Freire, Philippe Bonnet, and Dennis Shasha.
Manos Athanassoulis, Boston University, USA [email]
Holger Pirk, Imperial College London, UK
Juliana Freire, New York University, USA
Stratos Idreos, Harvard University, USA
Dennis Shasha, New York University, USA
Ziawasch Abedjan, Leibniz Universität Hannover, Germany
Angelos Christos Anadiotis, Ecole Polytechnique & IPP, France and EPFL, Switzerland
Raja Appuswamy, Eurecom, France
Spyros Blanas, The Ohio State University, USA
Matthias Boehm, Graz University of Technology, Austria
Dmytro Bogatov, Boston University, USA
Philippe Bonnet, ITU Copenhagen, Denmark
Paris Carbone, RISE Research Institutes of Sweden, Sweden
Philippe Cudre-Mauroux, University of Fribourg, Switzerland
Maria Daltayanni, University of San Francisco, USA
Jens Dittrich, Saarland University, Germany
Herodotos Herodotou, Cyprus University of Technology, Cyprus
Yannis Ioannidis, University of Athens & Athena Research and Innovation Center, Greece
Vasiliki Kalavri, Boston University, USA
Asterios Katsifodimos, TU Delft, Netherlands
Wolfgang Lehner, TU Dresden, Germany
Milos Nikolic, University of Edinburgh, UK
John Paparrizos, University of Chicago, USA
Ilia Petrov, Reutlingen University, Germany
Kostas Stefanidis, Tampere University, Finland
Dimitrios Tsoumakos, National Technical University of Athens, Greece
Tianzheng Wang, Simon Fraser University, Canada
Yingjun Wu, Singularity Data
Dorde Zivanovic, University of Oxford, UK
Sonia Horchidan, KTH Royal Institute of Technology, Sweden
Tianxun Hu, Simon Fraser University, Canada
Kaisong Huang, Simon Fraser University, Canada
Andy Huynh, Boston University, USA
Fotios Kounelis, Imperial College London, UK
Alexander Krause, TU Dresden, Germany
Chunwei Liu, University of Chicago, USA
Baotong Lu, Chinese University of Hong Kong, Hong Kong
Edson Lucas Filho, Universidade Federal do Paraná, Brazil
Madhulika Mohanty, Inria Saclay, France
Hubert Mohr-Daurat, Imperial College London, UK
Ju Hyoung Mun, Boston University, USA
Thorsten Papenbrock, Philipps University of Marburg, Germany
Aneesh Raman, Boston University, USA
Georgios Theodorakis, Imperial College London, UK
Georgia Troullinou, FORTH-ICS, Greece
Prajna Upadhyay, INRIA Saclay, France
Bo Zhao, Imperial College London, UK
Zichen Zhu, Boston University, USA
Subhadeep Sarkar, Boston University, USA