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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
Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review—0
Multimodal Data Integration for Precision Oncology: Challenges and Future Directions—0
Multimodal Data Integration for Sustainable Indoor Gardening: Tracking Anyplant with Time Series Foundation Model—0
Multi-Modal Dataset Creation for Federated Learning with DICOM Structured Reports—0
Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer—0
Multimodal Generative AI for Story Point Estimation in Software Development—0
Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects—0
Multi-object Data Integration in the Study of Primary Progressive Aphasia—0
Multi-omics data integration for early diagnosis of hepatocellular carcinoma (HCC) using machine learning—0
Semantic interoperability based on the European Materials and Modelling Ontology and its ontological paradigm: Mereosemiotics—0
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