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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 331–340 of 431 papers

TitleStatusHype
An Overview of Healthcare Data Analytics With Applications to the COVID-19 Pandemic—0
Application of Artificial Intelligence in Schizophrenia Rehabilitation Management: A Systematic Scoping Review—0
A Primer on the Data Cleaning Pipeline—0
A Pseudo-Likelihood Approach to Linear Regression with Partially Shuffled Data—0
A review of machine learning approaches, challenges and prospects for computational tumor pathology—0
A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery—0
A Semantic Analyzer for the Comprehension of the Spontaneous Arabic Speech—0
Assessing the Reproducibility of Machine-learning-based Biomarker Discovery in Parkinson's Disease—0
Assumption-Lean Post-Integrated Inference with Negative Control Outcomes—0
A Survey of Data Quality Measurement and Monitoring Tools—0
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