A Multianalyzer Machine Learning Model for Marine Heterogeneous Data Schema Mapping
The main challenges that marine heterogeneous data integration faces are the problem of accurate schema mapping between heterogeneous data sources. In order to improve the schema mapping efficiency and get more accurate learning results, this paper proposes a heterogeneous data schema mapping method...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/248467 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832553240323948544 |
|---|---|
| author | Wang Yan Le Jiajin Zhang Yun |
| author_facet | Wang Yan Le Jiajin Zhang Yun |
| author_sort | Wang Yan |
| collection | DOAJ |
| description | The main challenges that marine heterogeneous data integration faces are the problem of accurate schema mapping between heterogeneous data sources. In order to improve the schema mapping efficiency and get more accurate learning results, this paper proposes a heterogeneous data schema mapping method basing on multianalyzer machine learning model. The multianalyzer analysis the learning results comprehensively, and a fuzzy comprehensive evaluation system is introduced for output results’ evaluation and multi factor quantitative judging. Finally, the data mapping comparison experiment on the East China Sea observing data confirms the effectiveness of the model and shows multianalyzer’s obvious improvement of mapping error rate. |
| format | Article |
| id | doaj-art-100104ca59544539b4a5c561a2257ad1 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-100104ca59544539b4a5c561a2257ad12025-02-03T05:54:28ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/248467248467A Multianalyzer Machine Learning Model for Marine Heterogeneous Data Schema MappingWang Yan0Le Jiajin1Zhang Yun2Glorious Sun School of Business and Management, Donghua University, Shanghai, ChinaSchool of Computer Science and Technology, Donghua University, Shanghai, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai, ChinaThe main challenges that marine heterogeneous data integration faces are the problem of accurate schema mapping between heterogeneous data sources. In order to improve the schema mapping efficiency and get more accurate learning results, this paper proposes a heterogeneous data schema mapping method basing on multianalyzer machine learning model. The multianalyzer analysis the learning results comprehensively, and a fuzzy comprehensive evaluation system is introduced for output results’ evaluation and multi factor quantitative judging. Finally, the data mapping comparison experiment on the East China Sea observing data confirms the effectiveness of the model and shows multianalyzer’s obvious improvement of mapping error rate.http://dx.doi.org/10.1155/2014/248467 |
| spellingShingle | Wang Yan Le Jiajin Zhang Yun A Multianalyzer Machine Learning Model for Marine Heterogeneous Data Schema Mapping The Scientific World Journal |
| title | A Multianalyzer Machine Learning Model for Marine Heterogeneous Data Schema Mapping |
| title_full | A Multianalyzer Machine Learning Model for Marine Heterogeneous Data Schema Mapping |
| title_fullStr | A Multianalyzer Machine Learning Model for Marine Heterogeneous Data Schema Mapping |
| title_full_unstemmed | A Multianalyzer Machine Learning Model for Marine Heterogeneous Data Schema Mapping |
| title_short | A Multianalyzer Machine Learning Model for Marine Heterogeneous Data Schema Mapping |
| title_sort | multianalyzer machine learning model for marine heterogeneous data schema mapping |
| url | http://dx.doi.org/10.1155/2014/248467 |
| work_keys_str_mv | AT wangyan amultianalyzermachinelearningmodelformarineheterogeneousdataschemamapping AT lejiajin amultianalyzermachinelearningmodelformarineheterogeneousdataschemamapping AT zhangyun amultianalyzermachinelearningmodelformarineheterogeneousdataschemamapping AT wangyan multianalyzermachinelearningmodelformarineheterogeneousdataschemamapping AT lejiajin multianalyzermachinelearningmodelformarineheterogeneousdataschemamapping AT zhangyun multianalyzermachinelearningmodelformarineheterogeneousdataschemamapping |