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Data Integration

Data integration (also called information integration) is the process of consolidating data from a set of heterogeneous data sources into a single uniform data set (materialized integration) or view on the data (virtual integration). Data integration pipelines involve subtasks such as schema matching, table annotation, entity resolution, value normalization, data cleansing, and data fusion. Application domains of data integration include data warehousing, data lakes, and knowledge base consolidation. Surveys on Data integration:

Papers

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Showing 181–190 of 431 papers

TitleStatusHype
A Pseudo-Likelihood Approach to Linear Regression with Partially Shuffled Data—0
Deep Learning and NLP in Cryptocurrency Forecasting: Integrating Financial, Blockchain, and Social Media Data—0
Community-Based Data Integration of Course and Job Data in Support of Personalized Career-Education Recommendations—0
Federated Multi-View Learning for Private Medical Data Integration and Analysis—0
Federated Learning over Harmonized Data Silos—0
Common Foundations for SHACL, ShEx, and PG-Schema—0
A Primer on the Data Cleaning Pipeline—0
Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data—0
Federated Learning for Coronary Artery Plaque Detection in Atherosclerosis Using IVUS Imaging: A Multi-Hospital Collaboration—0
Federated Learning: A new frontier in the exploration of multi-institutional medical imaging data—0
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