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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 101110 of 431 papers

TitleStatusHype
Mining the contribution of intensive care clinical course to outcome after traumatic brain injuryCode0
Leveraging Legacy Data to Accelerate Materials Design via Preference LearningCode0
Enhancing Glucose Level Prediction of ICU Patients through Hierarchical Modeling of Irregular Time-SeriesCode0
An Empirical Meta-analysis of the Life Sciences (Linked?) Open Data on the WebCode0
Building Flexible, Scalable, and Machine Learning-ready Multimodal Oncology DatasetsCode0
Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMsCode0
A Survey of Pipeline Tools for Data EngineeringCode0
An attention model to analyse the risk of agitation and urinary tract infections in people with dementiaCode0
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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