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.
Download Presentation
Please find below an Image/Link to download the presentation.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author. Download presentation by click this link. If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.
E N D
Presentation Transcript
Data Integration Faces 3 Challenges Value Heterogeneity Instance Heterogeneity Structure Heterogeneity
Data Integration Faces 3 Challenges Value Heterogeneity Instance Heterogeneity Structure Heterogeneity
Data Integration Faces 3 Challenges Scissors Value Heterogeneity Paper Scissors Instance Heterogeneity Structure Heterogeneity
Data Integration Faces 3 Challenges Scissors Value Heterogeneity Glue Instance Heterogeneity Structure Heterogeneity
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