Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open-Pit Mine Slope
With the diversification of pit mine slope monitoring and the development of new technologies such as multisource data flow monitoring, normal alert log processing system cannot fulfil the log analysis expectation at the scale of big data. In order to make up this disadvantage, this research will pr...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/1048756 |
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| _version_ | 1832548688119988224 |
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| author | Song Jiang Minjie Lian Caiwu Lu Qinghua Gu Shunling Ruan Xuecai Xie |
| author_facet | Song Jiang Minjie Lian Caiwu Lu Qinghua Gu Shunling Ruan Xuecai Xie |
| author_sort | Song Jiang |
| collection | DOAJ |
| description | With the diversification of pit mine slope monitoring and the development of new technologies such as multisource data flow monitoring, normal alert log processing system cannot fulfil the log analysis expectation at the scale of big data. In order to make up this disadvantage, this research will provide an ensemble prediction algorithm of anomalous system data based on time series and an evaluation system for the algorithm. This algorithm integrates multiple classifier prediction algorithms and proceeds classified forecast for data collected, which can optimize the accuracy in predicting the anomaly data in the system. The algorithm and evaluation system is tested by using the microseismic monitoring data of an open-pit mine slope over 6 months. Testing results illustrate prediction algorithm provided by this research can successfully integrate the advantage of multiple algorithms to increase the accuracy of prediction. In addition, the evaluation system greatly supports the algorithm, which enhances the stability of log analysis platform. |
| format | Article |
| id | doaj-art-8b809be459644a36838316301ff8edb9 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-8b809be459644a36838316301ff8edb92025-02-03T06:13:20ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/10487561048756Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open-Pit Mine SlopeSong Jiang0Minjie Lian1Caiwu Lu2Qinghua Gu3Shunling Ruan4Xuecai Xie5School of Management, Xi’an University of Architecture and Technology, Shaanxi 710055, ChinaSchool of Management, Xi’an University of Architecture and Technology, Shaanxi 710055, ChinaSchool of Management, Xi’an University of Architecture and Technology, Shaanxi 710055, ChinaSchool of Management, Xi’an University of Architecture and Technology, Shaanxi 710055, ChinaSchool of Management, Xi’an University of Architecture and Technology, Shaanxi 710055, ChinaCollege of Resource and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, ChinaWith the diversification of pit mine slope monitoring and the development of new technologies such as multisource data flow monitoring, normal alert log processing system cannot fulfil the log analysis expectation at the scale of big data. In order to make up this disadvantage, this research will provide an ensemble prediction algorithm of anomalous system data based on time series and an evaluation system for the algorithm. This algorithm integrates multiple classifier prediction algorithms and proceeds classified forecast for data collected, which can optimize the accuracy in predicting the anomaly data in the system. The algorithm and evaluation system is tested by using the microseismic monitoring data of an open-pit mine slope over 6 months. Testing results illustrate prediction algorithm provided by this research can successfully integrate the advantage of multiple algorithms to increase the accuracy of prediction. In addition, the evaluation system greatly supports the algorithm, which enhances the stability of log analysis platform.http://dx.doi.org/10.1155/2018/1048756 |
| spellingShingle | Song Jiang Minjie Lian Caiwu Lu Qinghua Gu Shunling Ruan Xuecai Xie Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open-Pit Mine Slope Complexity |
| title | Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open-Pit Mine Slope |
| title_full | Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open-Pit Mine Slope |
| title_fullStr | Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open-Pit Mine Slope |
| title_full_unstemmed | Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open-Pit Mine Slope |
| title_short | Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open-Pit Mine Slope |
| title_sort | ensemble prediction algorithm of anomaly monitoring based on big data analysis platform of open pit mine slope |
| url | http://dx.doi.org/10.1155/2018/1048756 |
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