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Thus, we are using AIS data for the data integration process.
Designing the data integration processes involves creating stage-related conceptual and logical data integration process models and designing stage-related physical data integration process models, stage-related source to target mappings and the overall data integration workflow.
The data integration process for a heterogeneous legacy system is a key aspect of the development of a computerized system and in the integration of a design framework.
Design specifications include conceptual, logical, and physical data integration process models; logical and physical data models for sources and targets; and source to target mappings.
To cope with such complexity, in this paper, we present the Big Data Integration ontology, the core construct to govern the data integration process under schema evolution by systematically annotating it with information regarding the schema of the sources.
Data integration process is performed by data filtering and classifying.
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A database table connector (DTC) wrapper program is implemented based on the use of data integration processes and keyword-based searching.
Big-data integration processes and saves the data for use in plant growth classification.
A fundamental component to achieve this goal, is data integration, the process of combining and unifying data from multiple sources.
It entails IT organizations to think about various aspects while choosing an organized approach for their application integration, data integration and process integration.
The goal of a new data integration and process system is to design the KSTAR data scheduling on top of the Pulse Automation and Scheduling System (PASS) according to KSTAR events.
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