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BBQ Independent Research InitiativeIndependent research initiative · not an accredited institution · Warsaw, Poland
Operating Form
Online initiative
Domain
bbq.edu.pl
Eligibility
Not established
ILLUSTRATIVE PROJECT CONCEPT

Project PROVENANCE-X: Cryptographic Workflow Provenance for Exascale Science

Engineering low-overhead, kernel-level cryptographic provenance capture and deterministic container runtimes for petascale computational pipelines.

Principal Investigator
Prof. dr hab. Tomasz Wiśniewski
Laboratory
Scientific Provenance & High-Performance Systems Lab (SP-HPC Lab)
Funding Agency
National Centre for Research and Development (NCBR) & FNP TEAM Programme
Grant ID
POIR.04.04.00-00-5C29/18
Allocated Budget
PLN 3,420,000 (€770,000)
Project Period
2023–2026

Scientific Objective & Core Research Questions

Can complete scientific lineage graphs be captured at the kernel level across thousands of distributed cluster nodes with less than 2% runtime overhead?

Work Packages & Methodological Roadmap

  1. Building Linux eBPF probes for non-invasive capture of process trees, file descriptors, network sockets, and shared memory.
  2. Designing content-addressable directed acyclic graphs (Merkle DAGs) for storing execution lineages.
  3. Integrating provenance verification into Nextflow, Snakemake, and Slurm workload managers.

Project Deliverables & Software Artefacts

  • ProvKernel: Open-source eBPF provenance tracer for scientific Linux clusters.
  • Provenance-X Storage Engine: Distributed Merkle-DAG database for provenance queries.
  • Reproducibility validation suite integrated into PL-Grid supercomputers.

Project Description

Modern computational science requires that every published figure and dataset be completely traceable back to the raw source data, software environment, and exact execution parameters. Project PROVENANCE-X delivers the systems software infrastructure needed to achieve seamless, automated provenance logging in large-scale high-performance computing environments.

Project Milestones & Reporting

  • Final project dissemination and industrial showcase scheduled for November 2026.
  • Adopted across 4 national supercomputing facilities in Central Europe.

Foundation References & Publications

External scholarly reference · 2016Third-party work

The FAIR Guiding Principles for Scientific Data Management and Stewardship

Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Bonino da Silva Santos, Philip E. Bourne, Jildau Bouwman, Anthony J. Brookes, Tim Clark, Mercè Crosas, Ingrid Dillo, Olivier Dumon, Scott Edmunds, Chris T. Evelo, Richard Finkers, Alejandra Gonzalez-Beltran, Alasdair J. G. Gray, Paul Groth, Carole Goble, Jeffrey S. Grethe, Jaap Heringa, Peter A. C. 't Hoen, Rob Hooft, Tobias Kuhn, Ruben Kok, Joost Kok, Scott J. Lusher, Maryann E. Martone, Albert Mons, Abel L. Packer, Bengt Persson, Philippe Rocca-Serra, Marco Roos, Rene van Schaik, Susanna-Assunta Sansone, Erik Schultes, Thierry Sengstag, Ted Slater, George Strawn, Morris A. Swertz, Mark Thompson, Johan van der Lei, Erik van Mulligen, Jan Velterop, Andra Waagmeester, Peter Wittenburg, Katherine Wolstencroft, Jun Zhao, Barend Mons

Scientific Data Vol. 3, pp. 160018(2016). DOI: 10.1038/sdata.2016.18

Defines the principles that digital research objects should be Findable, Accessible, Interoperable, and Reusable for people and machines.

External scholarly reference · 2017Third-party work

A Manifesto for Reproducible Science

Marcus R. Munafò, Brian A. Nosek, Dorothy V. M. Bishop, Katherine S. Button, Christopher D. Chambers, Nathalie Percie du Sert, Uri Simonsohn, Eric-Jan Wagenmakers, Jennifer J. Ware, John P. A. Ioannidis

Nature Human Behaviour Vol. 1, pp. 0021(2017). DOI: 10.1038/s41562-016-0021

Sets out practical measures to improve the reliability and efficiency of scientific research through stronger methods, transparency, and incentives.

External scholarly reference · 2021Third-party work

Datasheets for Datasets

Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, Kate Crawford

Communications of the ACM Vol. 64(12), pp. 86-92(2021). DOI: 10.1145/3458723

Introduces a documentation framework for recording the motivation, composition, collection process, recommended uses, and limitations of datasets used in machine learning.