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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 251–260 of 431 papers

TitleStatusHype
Understanding Reflection Needs for Personal Health Data in Diabetes—0
Unified Representation of Genomic and Biomedical Concepts through Multi-Task, Multi-Source Contrastive Learning—0
Unlocking Historical Clinical Trial Data with ALIGN: A Compositional Large Language Model System for Medical Coding—0
Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models—0
Urban Representation Learning for Fine-grained Economic Mapping: A Semi-supervised Graph-based Approach—0
Reasoning about disclosure in data integration in the presence of source constraints—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
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