A process mining-based error correction approach to improve data quality of an IoT-sourced event log
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916335799238656 |
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| author | Shirali, Mohsen Ahmadi, Zahra Fernández-Llatas, Carlos Bayo-Monton, Jose-Luis Di Federico, Gemma |
| author_facet | Shirali, Mohsen Ahmadi, Zahra Fernández-Llatas, Carlos Bayo-Monton, Jose-Luis Di Federico, Gemma |
| contents | Internet of Things (IoT) systems are vulnerable to data collection errors and these errors can significantly degrade the quality of collected data, impact data analysis and lead to inaccurate or distorted results. This article emphasizes the importance of evaluating data quality and errors before proceeding with analysis and considering the effectiveness of error correction methods for a smart home use case. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13091 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A process mining-based error correction approach to improve data quality of an IoT-sourced event log Shirali, Mohsen Ahmadi, Zahra Fernández-Llatas, Carlos Bayo-Monton, Jose-Luis Di Federico, Gemma Databases Emerging Technologies E.0; H.4.0; J.3; J.0 Internet of Things (IoT) systems are vulnerable to data collection errors and these errors can significantly degrade the quality of collected data, impact data analysis and lead to inaccurate or distorted results. This article emphasizes the importance of evaluating data quality and errors before proceeding with analysis and considering the effectiveness of error correction methods for a smart home use case. |
| title | A process mining-based error correction approach to improve data quality of an IoT-sourced event log |
| topic | Databases Emerging Technologies E.0; H.4.0; J.3; J.0 |
| url | https://arxiv.org/abs/2404.13091 |