Abstract
We present a data ingestion quality validation approach using conditional metrics, a novel form of metrics that compute data quality metrics over specific parts of the ingestion data. We propose a method that automatically derives conditional metrics from historical ingestion sequences, using stability as a selection criterion for implementing these metrics as data unit tests. If an ingestion batch fails any unit tests, we show how conditional metrics can be utilized to identify potential errors. We show the effectiveness of our approach through an evaluation on a real world data set under various error scenarios.
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Notes
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The techniques in this paper can easily be generalized to multiple relations. We focus on one relation, however, to keep the presentation simple.
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Acknowledgements.
We thank Kris Luyten for many helpful discussions on this paper. S. Vansummeren was supported by the Bijzonder Onderzoeksfonds (BOF) of Hasselt University under Grant No. BOF20ZAP02. This work is partially funded by the Research Foundation - Flanders (FWO-grant G055219N). The computing resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders (FWO) and the Flemish Government.
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Bylois, N., Neven, F., Vansummeren, S. (2023). Data Ingestion Validation through Stable Conditional Metrics with Ranking and Filtering. In: Abelló, A., Vassiliadis, P., Romero, O., Wrembel, R. (eds) Advances in Databases and Information Systems. ADBIS 2023. Lecture Notes in Computer Science, vol 13985. Springer, Cham. https://doi.org/10.1007/978-3-031-42914-9_15
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DOI: https://doi.org/10.1007/978-3-031-42914-9_15
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