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Detecting quality problems in research data: a model-driven approach

Authors:

Gabriele TaentzerAuthors Info & Claims

MODELS '20: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems

Pages 354 - 364

https://doi.org/10.1145/3365438.3410987

Published: 16 October 2020 Publication History

Abstract

As scientific progress highly depends on the quality of research data, there are strict requirements for data quality coming from the scientific community. A major challenge in data quality assurance is to localise quality problems that are inherent to data. Due to the dynamic digitalisation in specific scientific fields, especially the humanities, different database technologies and data formats may be used in rather short terms to gain experiences. We present a model-driven approach to analyse the quality of research data. It allows abstracting from the underlying database technology. Based on the observation that many quality problems show anti-patterns, a data engineer formulates analysis patterns that are generic concerning the database format and technology. A domain expert chooses a pattern that has been adapted to a specific database technology and concretises it for a domain-specific database format. The resulting concrete patterns are used by data analysts to locate quality problems in their databases. As proof of concept, we implemented tool support that realises this approach for XML databases. We evaluated our approach concerning expressiveness and performance in the domain of cultural heritage based on a qualitative study on quality problems occurring in cultural heritage data.

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Cited By

Lima KIovino LRossi MHeldal ROyetoyan TDe Sanctis M(2023)Marine Data Observability using KPIS: An MDSE Approach2023 ACM/IEEE 26th International Conference on Model Driven Engineering Languages and Systems (MODELS)10.1109/MODELS58315.2023.00016(24-35)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/MODELS58315.2023.00016

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Published In

cover image ACM Conferences

MODELS '20: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems

October 2020

406 pages

ISBN:9781450370196

DOI:10.1145/3365438

General Chairs:
Eugene Syriani
Université de Montréal, Canada
,
Houari Sahraoui
Université de Montréal, Canada

Copyright © 2020 ACM.

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Published: 16 October 2020

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German Federal Ministry of Education and Research

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MODELS '20

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SIGSOFT

MODELS '20: ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems

October 16 - 23, 2020

Virtual Event, Canada

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MODELS '20 Paper Acceptance Rate 35 of 127 submissions, 28%;

Overall Acceptance Rate 144 of 506 submissions, 28%

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Cited By

Lima KIovino LRossi MHeldal ROyetoyan TDe Sanctis M(2023)Marine Data Observability using KPIS: An MDSE Approach2023 ACM/IEEE 26th International Conference on Model Driven Engineering Languages and Systems (MODELS)10.1109/MODELS58315.2023.00016(24-35)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/MODELS58315.2023.00016

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