Method for Data Quality Assessment of Synthetic Industrial Data
Sometimes it is difficult, or even impossible, to acquire real data from sensors and machines that must be used in research. Such examples are the modern industrial platforms that frequently are reticent to share data. In such situations, the only option is to work with synthetic data obtained by si...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/4/1608 |
| _version_ | 1797476716854116352 |
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| author | László Barna Iantovics Călin Enăchescu |
| author_facet | László Barna Iantovics Călin Enăchescu |
| author_sort | László Barna Iantovics |
| collection | DOAJ |
| description | Sometimes it is difficult, or even impossible, to acquire real data from sensors and machines that must be used in research. Such examples are the modern industrial platforms that frequently are reticent to share data. In such situations, the only option is to work with synthetic data obtained by simulation. Regarding simulated data, a limitation could consist in the fact that the data are not appropriate for research, based on poor quality or limited quantity. In such cases, the design of algorithms that are tested on that data does not give credible results. For avoiding such situations, we consider that mathematically grounded data-quality assessments should be designed according to the specific type of problem that must be solved. In this paper, we approach a multivariate type of prediction whose results finally can be used for binary classification. We propose the use of a mathematically grounded data-quality assessment, which includes, among other things, the analysis of predictive power of independent variables used for prediction. We present the assumptions that should be passed by the synthetic data. Different threshold values are established by a human assessor. In the case of research data, if all the assumptions pass, then we can consider that the data are appropriate for research and can be applied by even using other methods for solving the same type of problem. The applied method finally delivers a classification table on which can be applied any indicators of performed classification quality, such as sensitivity, specificity, accuracy, F1 score, area under curve (AUC), receiver operating characteristics (ROC), true skill statistics (TSS) and Kappa coefficient. These indicators’ values offer the possibility of comparison of the results obtained by applying the considered method with results of any other method applied for solving the same type of problem. For evaluation and validation purposes, we performed an experimental case study on a novel synthetic dataset provided by the well-known UCI data repository. |
| first_indexed | 2024-03-09T21:05:37Z |
| format | Article |
| id | doaj.art-dfa5b1b961f64b5a93181629284fa9e9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:05:37Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-dfa5b1b961f64b5a93181629284fa9e92023-11-23T22:02:16ZengMDPI AGSensors1424-82202022-02-01224160810.3390/s22041608Method for Data Quality Assessment of Synthetic Industrial DataLászló Barna Iantovics0Călin Enăchescu1Department of Electrical Engineering and Information Technology, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540142 Targu Mures, RomaniaDepartment of Electrical Engineering and Information Technology, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540142 Targu Mures, RomaniaSometimes it is difficult, or even impossible, to acquire real data from sensors and machines that must be used in research. Such examples are the modern industrial platforms that frequently are reticent to share data. In such situations, the only option is to work with synthetic data obtained by simulation. Regarding simulated data, a limitation could consist in the fact that the data are not appropriate for research, based on poor quality or limited quantity. In such cases, the design of algorithms that are tested on that data does not give credible results. For avoiding such situations, we consider that mathematically grounded data-quality assessments should be designed according to the specific type of problem that must be solved. In this paper, we approach a multivariate type of prediction whose results finally can be used for binary classification. We propose the use of a mathematically grounded data-quality assessment, which includes, among other things, the analysis of predictive power of independent variables used for prediction. We present the assumptions that should be passed by the synthetic data. Different threshold values are established by a human assessor. In the case of research data, if all the assumptions pass, then we can consider that the data are appropriate for research and can be applied by even using other methods for solving the same type of problem. The applied method finally delivers a classification table on which can be applied any indicators of performed classification quality, such as sensitivity, specificity, accuracy, F1 score, area under curve (AUC), receiver operating characteristics (ROC), true skill statistics (TSS) and Kappa coefficient. These indicators’ values offer the possibility of comparison of the results obtained by applying the considered method with results of any other method applied for solving the same type of problem. For evaluation and validation purposes, we performed an experimental case study on a novel synthetic dataset provided by the well-known UCI data repository.https://www.mdpi.com/1424-8220/22/4/1608smart sensorsensor datasmart factoryIndustry 4.0data-quality assessmentprediction problem |
| spellingShingle | László Barna Iantovics Călin Enăchescu Method for Data Quality Assessment of Synthetic Industrial Data Sensors smart sensor sensor data smart factory Industry 4.0 data-quality assessment prediction problem |
| title | Method for Data Quality Assessment of Synthetic Industrial Data |
| title_full | Method for Data Quality Assessment of Synthetic Industrial Data |
| title_fullStr | Method for Data Quality Assessment of Synthetic Industrial Data |
| title_full_unstemmed | Method for Data Quality Assessment of Synthetic Industrial Data |
| title_short | Method for Data Quality Assessment of Synthetic Industrial Data |
| title_sort | method for data quality assessment of synthetic industrial data |
| topic | smart sensor sensor data smart factory Industry 4.0 data-quality assessment prediction problem |
| url | https://www.mdpi.com/1424-8220/22/4/1608 |
| work_keys_str_mv | AT laszlobarnaiantovics methodfordataqualityassessmentofsyntheticindustrialdata AT calinenachescu methodfordataqualityassessmentofsyntheticindustrialdata |