Research on the Quality Evaluation Method of Mobile Emergency Big Data Based on the Measure of Medium Truth Degree
Mobile emergency services are better able to meet the needs of frequent public emergencies; however, their data quality problems seriously affect decision-making. In order to reduce the interference of low-quality data and solve the problem of data quality ambiguity, this paper first summarizes the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/16/9072 |
| _version_ | 1797585726598021120 |
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| author | Jianxun Li Qing Li Haoxin Fu Kin Keung Lai |
| author_facet | Jianxun Li Qing Li Haoxin Fu Kin Keung Lai |
| author_sort | Jianxun Li |
| collection | DOAJ |
| description | Mobile emergency services are better able to meet the needs of frequent public emergencies; however, their data quality problems seriously affect decision-making. In order to reduce the interference of low-quality data and solve the problem of data quality ambiguity, this paper first summarizes the five characteristics of mobile emergency big data. Second, based on the characteristics of mobile emergency big data, four data quality dimensions are defined with reference to existing research and national standards and combined with the measure of medium truth degree to give single-dimension and multi-dimension data quality truth degree measure models. Finally, a subjective-objective, qualitative-quantitative mobile emergency big data quality evaluation method based on the measure of medium truth degree is formed. The validity and practicality of the method are also verified by examples of algorithmic analysis of fire text datasets from Australian mountain fire data and the Chinese Emergency Incident Corpus. The experiments show that the method can realize quantitative mobile emergency big data quality assessment, solve the problem of data quality ambiguity, and reduce the interference of low-quality data, so as to save resources and improve the analysis and decision-making ability. |
| first_indexed | 2024-03-11T00:10:07Z |
| format | Article |
| id | doaj.art-fce5f7e4e3514406beb041bc27f663fa |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T00:10:07Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-fce5f7e4e3514406beb041bc27f663fa2023-11-19T00:03:47ZengMDPI AGApplied Sciences2076-34172023-08-011316907210.3390/app13169072Research on the Quality Evaluation Method of Mobile Emergency Big Data Based on the Measure of Medium Truth DegreeJianxun Li0Qing Li1Haoxin Fu2Kin Keung Lai3School of Economics and Management, Xi’an University of Technology, Xi’an 710054, ChinaSchool of Economics and Management, Xi’an University of Technology, Xi’an 710054, ChinaSchool of Economics and Management, Xi’an University of Technology, Xi’an 710054, ChinaInternational Business School, Shaanxi Normal University, Xi’an 710048, ChinaMobile emergency services are better able to meet the needs of frequent public emergencies; however, their data quality problems seriously affect decision-making. In order to reduce the interference of low-quality data and solve the problem of data quality ambiguity, this paper first summarizes the five characteristics of mobile emergency big data. Second, based on the characteristics of mobile emergency big data, four data quality dimensions are defined with reference to existing research and national standards and combined with the measure of medium truth degree to give single-dimension and multi-dimension data quality truth degree measure models. Finally, a subjective-objective, qualitative-quantitative mobile emergency big data quality evaluation method based on the measure of medium truth degree is formed. The validity and practicality of the method are also verified by examples of algorithmic analysis of fire text datasets from Australian mountain fire data and the Chinese Emergency Incident Corpus. The experiments show that the method can realize quantitative mobile emergency big data quality assessment, solve the problem of data quality ambiguity, and reduce the interference of low-quality data, so as to save resources and improve the analysis and decision-making ability.https://www.mdpi.com/2076-3417/13/16/9072mobile emergencyMMTDbig data quality evaluationmobile emergency big data |
| spellingShingle | Jianxun Li Qing Li Haoxin Fu Kin Keung Lai Research on the Quality Evaluation Method of Mobile Emergency Big Data Based on the Measure of Medium Truth Degree Applied Sciences mobile emergency MMTD big data quality evaluation mobile emergency big data |
| title | Research on the Quality Evaluation Method of Mobile Emergency Big Data Based on the Measure of Medium Truth Degree |
| title_full | Research on the Quality Evaluation Method of Mobile Emergency Big Data Based on the Measure of Medium Truth Degree |
| title_fullStr | Research on the Quality Evaluation Method of Mobile Emergency Big Data Based on the Measure of Medium Truth Degree |
| title_full_unstemmed | Research on the Quality Evaluation Method of Mobile Emergency Big Data Based on the Measure of Medium Truth Degree |
| title_short | Research on the Quality Evaluation Method of Mobile Emergency Big Data Based on the Measure of Medium Truth Degree |
| title_sort | research on the quality evaluation method of mobile emergency big data based on the measure of medium truth degree |
| topic | mobile emergency MMTD big data quality evaluation mobile emergency big data |
| url | https://www.mdpi.com/2076-3417/13/16/9072 |
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