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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 111–120 of 431 papers

TitleStatusHype
The S2 Hierarchical Discrete Global Grid as a Nexus for Data Representation, Integration, and Querying Across Geospatial Knowledge Graphs—0
KcMF: A Knowledge-compliant Framework for Schema and Entity Matching with Fine-tuning-free LLMs—0
Unified Representation of Genomic and Biomedical Concepts through Multi-Task, Multi-Source Contrastive Learning—0
InstructBioMol: Advancing Biomolecule Understanding and Design Following Human Instructions—0
Empowering Cognitive Digital Twins with Generative Foundation Models: Developing a Low-Carbon Integrated Freight Transportation System—0
Multilayer network approaches to omics data integration in Digital Twins for cancer research—0
Deep learning-based Visual Measurement Extraction within an Adaptive Digital Twin Framework from Limited Data Using Transfer Learning—0
Assumption-Lean Post-Integrated Inference with Negative Control Outcomes—0
Comparative Analysis of Multi-Omics Integration Using Advanced Graph Neural Networks for Cancer ClassificationCode0
Nested Deep Learning Model Towards A Foundation Model for Brain Signal Data—0
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