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Nullius in Verba: Reproducibility for Database Systems Research, Revisited

  • Over the last decade, reproducibility of experimental results has been a prime focus in database systems research, and many high-profile conferences award results that can be independently verified. Since database systems research involves complex software stacks that non-trivially interact with hardware, sharing experimental setups is anything but trivial: Building a working reproduction package goes far beyond providing a DOI to some repository hosting data, code, and setup instructions.This tutorial revisits reproducible engineering in the face of state-of-the-art technology, and best practices gained in other computer science research communities. In particular, in the hands-on part, we demonstrate how to package entire system software stacks for dissemination. To ascertain long-term reproducibility over decades (or ideally, forever), we discuss why relying on open source technologies massively employed in industry has essential advantages over approaches crafted specifically for research. Supplementary material shows how version control systems that allow for non-linearly rewriting recorded history can document the structured genesis behind experimental setups in a way that is substantially easier to understand, without involvement of the original authors, compared to detour-ridden, strictly historic evolution.

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Metadaten
Verfasserangaben:Wolfgang MauererORCiDGND, Stefanie ScherzingerORCiD
DOI:https://doi.org/10.1109/ICDE51399.2021.00270
ISBN:978-1-7281-9184-3
Titel des übergeordneten Werkes (Englisch):2021 IEEE 37th International Conference on Data Engineering (ICDE 2021): 19-22 April 2021, Chania, Greece
Verlag:IEEE
Verlagsort:Piscataway, NJ
Herausgeber*in:Anastasia Ailamaki
Dokumentart:Konferenzveröffentlichung (Beitrag)
Sprache der Veröffentlichung:Englisch
Jahr der Veröffentlichung:2021
Datum der Freischaltung:11.04.2022
Freies Schlagwort / Tag:Control systems; Data engineering; Database systems; Hardware; Industries; Reproducibility of results; Tutorials; docker; git; reproducible science; reproduction, reproduction package; scientific attribution; scientific method
Erste Seite:2377
Letzte Seite:2380
Fakultäten / Institute / Einrichtungen:Fakultät Informatik und Mathematik
Fakultät Informatik und Mathematik / Labor für Digitalisierung (LFD)
Begutachtungsstatus:peer-reviewed
Forschungsschwerpunkt:Digitalisierung
Lizenz (Deutsch):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG