Abstract :

The Rapid Expansion Of Heterogeneous Big Data Has Created Significant Challenges For Organizations Attempting To Integrate Information Generated From Diverse Data Sources. Modern Enterprise Environments Continuously Produce Structured, Semi-structured, And Unstructured Data Through Relational Databases, Cloud Platforms, Internet Of Things (IoT) Devices, Enterprise Applications, Web Services, And Social Media Networks. Traditional Data Integration Techniques Primarily Rely On Manually Defined Transformation Rules And Static Schema Mappings, Making Them Insufficient For Handling The Complexity And Dynamic Nature Of Heterogeneous Big Data. This Paper Presents A Conceptual Intelligent Data Integration Approach That Incorporates Data Preprocessing, Schema Matching, Entity Resolution, Semantic Integration, And Metadata Management To Improve Data Quality, Interoperability, And Integration Efficiency. The Proposed Approach Emphasizes Intelligent Automation To Reduce Manual Intervention While Enhancing Consistency Among Heterogeneous Datasets. Data Preprocessing Improves Information Quality By Eliminating Duplicate Records, Correcting Inconsistencies, Handling Missing Values, And Standardizing Data Formats. Schema Matching And Entity Resolution Establish Relationships Among Heterogeneous Datasets And Identify Duplicate Entities, Whereas Semantic Integration And Metadata Management Resolve Conceptual Conflicts And Maintain Consistent Descriptions Of Integrated Information. The Proposed Approach Provides A Comprehensive Conceptual Methodology Capable Of Supporting Business Intelligence, Predictive Analytics, Decision Support, Healthcare, Finance, Scientific Research, And Other Data-intensive Applications. The Conceptual Analysis Indicates That Integrating Intelligent Techniques Throughout The Data Integration Lifecycle Can Significantly Improve The Quality, Reliability, And Usability Of Heterogeneous Data In Modern Big Data Environments.

Published:

28-7-2024

Issue:

Vol. 24 No. 7 (2024)

Page Nos:

416-424

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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