Ai- Driven Mapping In Hierarchical Heterogeneous Data For Customer Management System
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 11, Pages 1555-1568
Abstract
The very fast growth of the business world insists on the demand for integration of heterogeneous data process. The mapping of data in different heterogeneous hierarchical data is complex in the business aspect and XML is considered a hierarchical structure. In the existing literature, the mapping process is done using a synonyms table in a hierarchical structure. This approach becomes complex when retrieving the data in a hierarchical structure and uses more space for the mapping process. This work explores the mapping of different heterogeneous data using AI-MKMT (Artificial Intelligence-Multiple Key feature Mapping Technique) which uses less space. First, the standard data format is generated from the user-defined hierarchical data with SAX being used for standardization. Then, the mapping process is done among heterogeneous hierarchical structures based on the AI-MKMT technique by predefined rules. The heterogeneity of the hierarchical data structure is analyzed with an enhanced ID3 machine learning approach which generates precise and consistent data that is used in the AI mapping process. This work is applied in the marketing industry for predicting the behavior of the customer.
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