title: A multianalyzer machine learning model for marine heterogeneous data schema mapping.
abstract: 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.
authors.affiliation: Glorious Sun School of Business and Management, Donghua University, Shanghai, China ; College of Information Technology, Shanghai Ocean University, Shanghai, China.
authors.forename: Wang
authors.initials: W
authors.lastname: Yan
authors.affiliation: School of Computer Science and Technology, Donghua University, Shanghai, China.
authors.forename: Le
authors.initials: L
authors.lastname: Jiajin
authors.affiliation: College of Information Technology, Shanghai Ocean University, Shanghai, China.
authors.forename: Zhang
authors.initials: Z
authors.lastname: Yun
mesh_terms: D001185:Artificial Intelligence; D016208:Databases, Factual / Q000706:statistics & numerical data; D008386:Marine Biology / Q000706:statistics & numerical data; D008962:Models, Theoretical