Validity constraints for data analysis workflows. Schintke, F., Belhajjame, K., Mecquenem, N. D., Frantz, D., Guarino, V. E., Hilbrich, M., Lehmann, F., Missier, P., Sattler, R., Sparka, J. A., Speckhard, D. T., Stolte, H., Vu, A. D., & Leser, U. Future Generation Computer Systems, 157:82–97, 2024. ![Validity constraints for data analysis workflows [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Porting a scientific data analysis workflow (DAW) to a cluster infrastructure, a new software stack, or even only a new dataset with some notably different properties is often challenging. Despite the structured definition of the steps (tasks) and their interdependencies during a complex data analysis in the DAW specification, relevant assumptions may remain unspecified and implicit. Such hidden assumptions often lead to crashing tasks without a reasonable error message, poor performance in general, non-terminating executions, or silent wrong results of the DAW, to name only a few possible consequences. Searching for the causes of such errors and drawbacks in a distributed compute cluster managed by a complex infrastructure stack, where DAWs for large datasets typically are executed, can be tedious and time-consuming. We propose validity constraints (VCs) as a new concept for DAW languages to alleviate this situation. A VC is a constraint specifying logical conditions that must be fulfilled at certain times for DAW executions to be valid. When defined together with a DAW, VCs help to improve the portability, adaptability, and reusability of DAWs by making implicit assumptions explicit. Once specified, VCs can be controlled automatically by the DAW infrastructure, and violations can lead to meaningful error messages and graceful behavior (e.g., termination or invocation of repair mechanisms). We provide a broad list of possible VCs, classify them along multiple dimensions, and compare them to similar concepts one can find in related fields. We also provide a proof-of-concept implementation for the workflow system Nextflow.
@article{schintke_validity_2024,
title = {Validity constraints for data analysis workflows},
volume = {157},
issn = {0167-739X},
url = {https://www.sciencedirect.com/science/article/pii/S0167739X24001079},
doi = {https://doi.org/10.1016/j.future.2024.03.037},
abstract = {Porting a scientific data analysis workflow (DAW) to a cluster infrastructure, a new software stack, or even only a new dataset with some notably different properties is often challenging. Despite the structured definition of the steps (tasks) and their interdependencies during a complex data analysis in the DAW specification, relevant assumptions may remain unspecified and implicit. Such hidden assumptions often lead to crashing tasks without a reasonable error message, poor performance in general, non-terminating executions, or silent wrong results of the DAW, to name only a few possible consequences. Searching for the causes of such errors and drawbacks in a distributed compute cluster managed by a complex infrastructure stack, where DAWs for large datasets typically are executed, can be tedious and time-consuming. We propose validity constraints (VCs) as a new concept for DAW languages to alleviate this situation. A VC is a constraint specifying logical conditions that must be fulfilled at certain times for DAW executions to be valid. When defined together with a DAW, VCs help to improve the portability, adaptability, and reusability of DAWs by making implicit assumptions explicit. Once specified, VCs can be controlled automatically by the DAW infrastructure, and violations can lead to meaningful error messages and graceful behavior (e.g., termination or invocation of repair mechanisms). We provide a broad list of possible VCs, classify them along multiple dimensions, and compare them to similar concepts one can find in related fields. We also provide a proof-of-concept implementation for the workflow system Nextflow.},
journal = {Future Generation Computer Systems},
author = {Schintke, Florian and Belhajjame, Khalid and Mecquenem, Ninon De and Frantz, David and Guarino, Vanessa Emanuela and Hilbrich, Marcus and Lehmann, Fabian and Missier, Paolo and Sattler, Rebecca and Sparka, Jan Arne and Speckhard, Daniel T. and Stolte, Hermann and Vu, Anh Duc and Leser, Ulf},
year = {2024},
keywords = {Dependability, Integrity and conformance checking, Scientific workflow systems, Validity constraints, Workflow specification languages},
pages = {82--97},
}
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