A Study of Pattern Prediction in the Monitoring Data of Earthen Ruins with the Internet of Things
An understanding of the changes of the rammed earth temperature of earthen ruins is important for protection of such ruins. To predict the rammed earth temperature pattern using the air temperature pattern of the monitoring data of earthen ruins, a pattern prediction method based on interesting patt...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/17/5/1076 |
| _version_ | 1811183818031235072 |
|---|---|
| author | Yun Xiao Xin Wang Faezeh Eshragh Xuanhong Wang Xiaojiang Chen Dingyi Fang |
| author_facet | Yun Xiao Xin Wang Faezeh Eshragh Xuanhong Wang Xiaojiang Chen Dingyi Fang |
| author_sort | Yun Xiao |
| collection | DOAJ |
| description | An understanding of the changes of the rammed earth temperature of earthen ruins is important for protection of such ruins. To predict the rammed earth temperature pattern using the air temperature pattern of the monitoring data of earthen ruins, a pattern prediction method based on interesting pattern mining and correlation, called PPER, is proposed in this paper. PPER first finds the interesting patterns in the air temperature sequence and the rammed earth temperature sequence. To reduce the processing time, two pruning rules and a new data structure based on an R-tree are also proposed. Correlation rules between the air temperature patterns and the rammed earth temperature patterns are then mined. The correlation rules are merged into predictive rules for the rammed earth temperature pattern. Experiments were conducted to show the accuracy of the presented method and the power of the pruning rules. Moreover, the Ming Dynasty Great Wall dataset was used to examine the algorithm, and six predictive rules from the air temperature to rammed earth temperature based on the interesting patterns were obtained, with the average hit rate reaching 89.8%. The PPER and predictive rules will be useful for rammed earth temperature prediction in protection of earthen ruins. |
| first_indexed | 2024-04-11T13:03:01Z |
| format | Article |
| id | doaj.art-b003d6dd9cb548fdba8b7a70b80f3b00 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:03:01Z |
| publishDate | 2017-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b003d6dd9cb548fdba8b7a70b80f3b002022-12-22T04:22:52ZengMDPI AGSensors1424-82202017-05-01175107610.3390/s17051076s17051076A Study of Pattern Prediction in the Monitoring Data of Earthen Ruins with the Internet of ThingsYun Xiao0Xin Wang1Faezeh Eshragh2Xuanhong Wang3Xiaojiang Chen4Dingyi Fang5School of Information Science and Technology, Northwest University, Xi’an 710021, ChinaSchool of Information Science and Technology, Northwest University, Xi’an 710021, ChinaDepartment of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaDepartment of Communication, Xi’an University of Posts & Telecommunications, Xi’an 710121, ChinaSchool of Information Science and Technology, Northwest University, Xi’an 710021, ChinaSchool of Information Science and Technology, Northwest University, Xi’an 710021, ChinaAn understanding of the changes of the rammed earth temperature of earthen ruins is important for protection of such ruins. To predict the rammed earth temperature pattern using the air temperature pattern of the monitoring data of earthen ruins, a pattern prediction method based on interesting pattern mining and correlation, called PPER, is proposed in this paper. PPER first finds the interesting patterns in the air temperature sequence and the rammed earth temperature sequence. To reduce the processing time, two pruning rules and a new data structure based on an R-tree are also proposed. Correlation rules between the air temperature patterns and the rammed earth temperature patterns are then mined. The correlation rules are merged into predictive rules for the rammed earth temperature pattern. Experiments were conducted to show the accuracy of the presented method and the power of the pruning rules. Moreover, the Ming Dynasty Great Wall dataset was used to examine the algorithm, and six predictive rules from the air temperature to rammed earth temperature based on the interesting patterns were obtained, with the average hit rate reaching 89.8%. The PPER and predictive rules will be useful for rammed earth temperature prediction in protection of earthen ruins.http://www.mdpi.com/1424-8220/17/5/1076pattern predictionmultivariate sequential dataearthen ruin |
| spellingShingle | Yun Xiao Xin Wang Faezeh Eshragh Xuanhong Wang Xiaojiang Chen Dingyi Fang A Study of Pattern Prediction in the Monitoring Data of Earthen Ruins with the Internet of Things Sensors pattern prediction multivariate sequential data earthen ruin |
| title | A Study of Pattern Prediction in the Monitoring Data of Earthen Ruins with the Internet of Things |
| title_full | A Study of Pattern Prediction in the Monitoring Data of Earthen Ruins with the Internet of Things |
| title_fullStr | A Study of Pattern Prediction in the Monitoring Data of Earthen Ruins with the Internet of Things |
| title_full_unstemmed | A Study of Pattern Prediction in the Monitoring Data of Earthen Ruins with the Internet of Things |
| title_short | A Study of Pattern Prediction in the Monitoring Data of Earthen Ruins with the Internet of Things |
| title_sort | study of pattern prediction in the monitoring data of earthen ruins with the internet of things |
| topic | pattern prediction multivariate sequential data earthen ruin |
| url | http://www.mdpi.com/1424-8220/17/5/1076 |
| work_keys_str_mv | AT yunxiao astudyofpatternpredictioninthemonitoringdataofearthenruinswiththeinternetofthings AT xinwang astudyofpatternpredictioninthemonitoringdataofearthenruinswiththeinternetofthings AT faezeheshragh astudyofpatternpredictioninthemonitoringdataofearthenruinswiththeinternetofthings AT xuanhongwang astudyofpatternpredictioninthemonitoringdataofearthenruinswiththeinternetofthings AT xiaojiangchen astudyofpatternpredictioninthemonitoringdataofearthenruinswiththeinternetofthings AT dingyifang astudyofpatternpredictioninthemonitoringdataofearthenruinswiththeinternetofthings AT yunxiao studyofpatternpredictioninthemonitoringdataofearthenruinswiththeinternetofthings AT xinwang studyofpatternpredictioninthemonitoringdataofearthenruinswiththeinternetofthings AT faezeheshragh studyofpatternpredictioninthemonitoringdataofearthenruinswiththeinternetofthings AT xuanhongwang studyofpatternpredictioninthemonitoringdataofearthenruinswiththeinternetofthings AT xiaojiangchen studyofpatternpredictioninthemonitoringdataofearthenruinswiththeinternetofthings AT dingyifang studyofpatternpredictioninthemonitoringdataofearthenruinswiththeinternetofthings |