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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

Showing 291300 of 431 papers

TitleStatusHype
TweetsKB: A Public and Large-Scale RDF Corpus of Annotated Tweets0
Uncertainty in Automated Ontology Matching: Lessons Learned from an Empirical Experimentation0
Understanding Reflection Needs for Personal Health Data in Diabetes0
Unified Representation of Genomic and Biomedical Concepts through Multi-Task, Multi-Source Contrastive Learning0
Unlocking Historical Clinical Trial Data with ALIGN: A Compositional Large Language Model System for Medical Coding0
Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models0
Urban Representation Learning for Fine-grained Economic Mapping: A Semi-supervised Graph-based Approach0
Reasoning about disclosure in data integration in the presence of source constraints0
When Geoscience Meets Foundation Models: Towards General Geoscience Artificial Intelligence System0
VITAL: Interactive Few-Shot Imitation Learning via Visual Human-in-the-Loop Corrections0
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