Abstract
In industry, data quality is crucial for data analysis. The credibility of data analysis relies on the low-quality sensor data in multi-sensor scenarios. Current methods assess data currency quality for each sensor separately, lacking a holistic multi-sensor analysis. To this end, this paper proposes a data quality assessment method based on association currency (DQA-AC). DQA-AC first establishes the relationship between the sampling times of sensors by a directed delay graph. Then it uses the specially designed currency score function and currency fusion algorithm to evaluate the associated currency score of each record. Finally, it computes the data currency quality of sensors based on the results from previous processes. We performed experiments to compare DQA-AC with a baseline, which demonstrates its efficacy and capacity to reflect the influence of sampling delays on data currency quality results. Additionally, we explored the effect of influence factors on data currency quality assessments. Results indicate that with increasing influence factors, results of DQA-AC tend to stabilize.
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Disclosure of Interests
The authors have no competing interests to declare.
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Acknowledgments
This work was supported in part by the National Key R&D Program of China (Grant No. 2023YFB3308300); the National Natural Science Foundation of China (Grant No. 62262074, U2268204 and 62172061); the Science and Technology Project of Sichuan Province (Grant No. 2022YFG0159, 2022YFG0155, 2022YFG0157).
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Zhu, Z. et al. (2024). Data Currency Quality Assessment Based on Multi-sensor. In: Huang, DS., Chen, W., Zhang, Q. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14874. Springer, Singapore. https://doi.org/10.1007/978-981-97-5618-6_30
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DOI: https://doi.org/10.1007/978-981-97-5618-6_30
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