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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 126–150 of 431 papers

TitleStatusHype
Beyond designer's knowledge: Generating materials design hypotheses via large language models—0
AnyMatch -- Efficient Zero-Shot Entity Matching with a Small Language ModelCode1
Multi-faceted Neuroimaging Data Integration via Analysis of Subspaces—0
Prompt-Matcher: Leveraging Large Models to Reduce Uncertainty in Schema Matching Results—0
EUR-USD Exchange Rate Forecasting Based on Information Fusion with Large Language Models and Deep Learning Methods—0
Exploring the Feasibility of Automated Data Standardization using Large Language Models for Seamless PositioningCode3
Cross-Species Data Integration for Enhanced Layer Segmentation in Kidney PathologyCode0
Personalized graph feature-based multi-omics data integration for cancer subtype identification—0
EraW-Net: Enhance-Refine-Align W-Net for Scene-Associated Driver Attention Estimation—0
MMREC: LLM Based Multi-Modal Recommender System—0
Enhancing Medical Learning and Reasoning Systems: A Boxology-Based Comparative Analysis of Design Patterns—0
Conformal Trajectory Prediction with Multi-View Data Integration in Cooperative DrivingCode1
MSMA: Multi-agent Trajectory Prediction in Connected and Autonomous Vehicle Environment with Multi-source Data IntegrationCode1
VITAL: Interactive Few-Shot Imitation Learning via Visual Human-in-the-Loop Corrections—0
Intelligent Cross-Organizational Process Mining: A Survey and New Perspectives—0
AtomAgents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence—0
Multi-Modal Dataset Creation for Federated Learning with DICOM Structured Reports—0
DALL-M: Context-Aware Clinical Data Augmentation with LLMsCode0
TCKAN:A Novel Integrated Network Model for Predicting Mortality Risk in Sepsis Patients—0
AI-driven multi-omics integration for multi-scale predictive modeling of causal genotype-environment-phenotype relationships—0
Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directions—0
Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional DatasetsCode0
LLM See, LLM Do: Guiding Data Generation to Target Non-Differentiable ObjectivesCode1
Multimodal Data Integration for Precision Oncology: Challenges and Future Directions—0
Optimal Transport for Latent Integration with An Application to Heterogeneous Neuronal Activity Data—0
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