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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 111120 of 431 papers

TitleStatusHype
Evaluating Blocking Biases in Entity MatchingCode0
Building Flexible, Scalable, and Machine Learning-ready Multimodal Oncology DatasetsCode0
A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep LearningCode0
PoWareMatch: a Quality-aware Deep Learning Approach to Improve Human Schema MatchingCode0
DALL-M: Context-Aware Clinical Data Augmentation with LLMsCode0
DANAE: a denoising autoencoder for underwater attitude estimationCode0
An attention model to analyse the risk of agitation and urinary tract infections in people with dementiaCode0
ReMatch: Retrieval Enhanced Schema Matching with LLMsCode0
A deep learning pipeline for cross-sectional and longitudinal multiview data integrationCode0
Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional DatasetsCode0
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