Home // International Journal On Advances in Software, volume 15, numbers 3 and 4, 2022 // View article
Designing Context-aware Data Plausibility Automation Using Machine Learning
Authors:
Mohaddeseh Basiri
Johannes Himmelbauer
Lisa Ehrlinger
Mihhail Matskin
Keywords: Artificial intelligence; Machine learning; Automation; Plausibility check; Anomaly detection; Ontology; Context-aware
Abstract:
In the last two decades, computing and storage technologies have experienced enormous advances. Leveraging these recent advances, Artificial Intelligence (AI) is making the leap from traditional classification use cases to automation of complex systems through advanced machine learning and reasoning algorithms. While the literature on AI algorithms and applications of these algorithms in automation is mature, there is a lack of research on trustworthy AI, i.e., how different industries can trust the developed AI modules. AI algorithms are data-driven, i.e., they learn based on the received data, and also act based on the received status data. Then, an initial step in addressing trustworthy AI is investigating the plausibility of the data that is fed to the system. In this work, we study the state-of-the-art data plausibility check approaches. Then, we propose a novel approach that leverages machine learning for an automated data plausibility check. This novel approach is context-aware, i.e., it leverages potential contextual data related to the dataset under investigation for a plausibility check. We investigate three machine learning solutions that leverage auto-correlation in each feature of dataset, correlation between features, and hidden statistics of each feature for generating the checkpoints. Performance evaluation results indicated the outstanding performance of the proposed scheme in the detection of noisy data in order to do the data plausibility check.
Pages: 235 to 249
Copyright: Copyright (c) to authors, 2022. Used with permission.
Publication date: December 31, 2022
Published in: journal
ISSN: 1942-2628