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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 351–360 of 431 papers

TitleStatusHype
Bi-Encoders based Species Normalization -- Pairwise Sentence Learning to Rank—0
BIM-to-BRICK: Using graph modeling for IoT/BMS and spatial semantic data interoperability within digital data models of buildings—0
Biodiversity data standards for the organization and dissemination of complex research projects and digital twins: a guide—0
Brain Imaging Foundation Models, Are We There Yet? A Systematic Review of Foundation Models for Brain Imaging and Biomedical Research—0
Business Entity Matching with Siamese Graph Convolutional Networks—0
Can Large Language Models Unveil the Mysteries? An Exploration of Their Ability to Unlock Information in Complex Scenarios—0
Capturing and Anticipating User Intents in Data Analytics via Knowledge Graphs—0
CDE-Mapper: Using Retrieval-Augmented Language Models for Linking Clinical Data Elements to Controlled Vocabularies—0
CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data—0
Clinical Trials Ontology Engineering with Large Language Models—0
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