Challenges in Data Integration: Heterogeneity and Solutions

Data integration faces challenges such as value heterogeneity, instance heterogeneity, and structure heterogeneity. Existing solutions assume independence of data sources and utilize methods like data fusion, truth discovery, string matching, object matching, schema matching, and model management. This involves handling various types of heterogeneity and managing queries for effective information extraction.

• Data integration
• Heterogeneity
• Solutions
• Data fusion
• Schema matching

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Uploaded on Sep 12, 2024 | 0 Views

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1. Data Integration Faces 3 Challenges Value Heterogeneity Instance Heterogeneity Structure Heterogeneity

2. Data Integration Faces 3 Challenges Value Heterogeneity Instance Heterogeneity Structure Heterogeneity

3. Data Integration Faces 3 Challenges Scissors Value Heterogeneity Paper Scissors Instance Heterogeneity Structure Heterogeneity

4. Data Integration Faces 3 Challenges Scissors Value Heterogeneity Glue Instance Heterogeneity Structure Heterogeneity

5. Existing Solutions Assume Independence of Data Sources Data fusion Truth discovery Value Heterogeneity String matching (edit distance, token-based, etc.) Object matching (aka. record linkage, reference reconciliation, ) Instance Heterogeneity Structure Heterogeneity Schema matching Model management Query answering using views Information extraction