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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 191–200 of 431 papers

TitleStatusHype
CARTE: Pretraining and Transfer for Tabular LearningCode2
Statistical Agnostic Regression: a machine learning method to validate regression models—0
Patient-Centric Knowledge Graphs: A Survey of Current Methods, Challenges, and Applications—0
An Adaptive System Architecture for Multimodal Intelligent Transportation Systems—0
P3LS: Partial Least Squares under Privacy Preservation—0
eipy: An Open-Source Python Package for Multi-modal Data Integration using Heterogeneous EnsemblesCode1
Integrate Any Omics: Towards genome-wide data integration for patient stratificationCode2
TemporalAugmenter: An Ensemble Recurrent Based Deep Learning Approach for Signal Classification—0
Analyses and Concerns in Precision Medicine: A Statistical Perspective—0
Data Integration Framework for Virtual Reality Enabled Digital Twins—0
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