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The Scientific World Journal
Volume 2014, Article ID 248467, 8 pages
Research Article

A Multianalyzer Machine Learning Model for Marine Heterogeneous Data Schema Mapping

1Glorious Sun School of Business and Management, Donghua University, Shanghai, China
2College of Information Technology, Shanghai Ocean University, Shanghai, China
3School of Computer Science and Technology, Donghua University, Shanghai, China

Received 16 April 2014; Revised 14 July 2014; Accepted 28 July 2014; Published 28 August 2014

Academic Editor: Giandomenico Spezzano

Copyright © 2014 Wang Yan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.