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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 171–180 of 431 papers

TitleStatusHype
Contextual Data Integration for Bike-sharing Demand Prediction with Graph Neural Networks in Degraded Weather Conditions—0
Context-Aware Analytics in MOM Applications—0
A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery—0
Conditionally Invariant Representation Learning for Disentangling Cellular Heterogeneity—0
A review of machine learning approaches, challenges and prospects for computational tumor pathology—0
Adverse Childhood Experiences Ontology for Mental Health Surveillance, Research, and Evaluation: Advanced Knowledge Representation and Semantic Web Techniques—0
Accu-Help: A Machine Learning based Smart Healthcare Framework for Accurate Detection of Obsessive Compulsive Disorder—0
A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference—0
Computer-Assisted Analysis of Biomedical Images—0
A Pseudo-Likelihood Approach to Linear Regression with Partially Shuffled Data—0
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