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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 341–350 of 431 papers

TitleStatusHype
A survey on deep learning approaches for data integration in autonomous driving system—0
A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior—0
A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches—0
A Theoretical Framework for Graph-based Digital Twins for Supply Chain Management and Optimization—0
A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design—0
AtomAgents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence—0
Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey—0
A Vision for Health Informatics: Introducing the SKED Framework.An Extensible Architecture for Scientific Knowledge Extraction from Data—0
Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise—0
Beyond designer's knowledge: Generating materials design hypotheses via large language models—0
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