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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 421–430 of 431 papers

TitleStatusHype
Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets—0
FadMan: Federated Anomaly Detection across Multiple Attributed Networks—0
Causal Feature Selection for Algorithmic Fairness—0
Fast Record Linkage for Company Entities—0
Feature Selection for Data Integration with Mixed Multi-view Data—0
Federated Learning: A new frontier in the exploration of multi-institutional medical imaging data—0
Federated Learning for Coronary Artery Plaque Detection in Atherosclerosis Using IVUS Imaging: A Multi-Hospital Collaboration—0
Federated Learning over Harmonized Data Silos—0
Federated Multi-View Learning for Private Medical Data Integration and Analysis—0
Deep Learning and NLP in Cryptocurrency Forecasting: Integrating Financial, Blockchain, and Social Media Data—0
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