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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 81–90 of 431 papers

TitleStatusHype
MISFEAT: Feature Selection for Subgroups with Systematic Missing Data—0
Multi-Layer Privacy-Preserving Record Linkage with Clerical Review based on gradual information disclosure—0
Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data—0
Contextual Data Integration for Bike-sharing Demand Prediction with Graph Neural Networks in Degraded Weather Conditions—0
Enhancing Function-Calling Capabilities in LLMs: Strategies for Prompt Formats, Data Integration, and Multilingual Translation—0
Towards Unified Molecule-Enhanced Pathology Image Representation Learning via Integrating Spatial TranscriptomicsCode1
Skeleton Detection Using Dual Radars with Integration of Dual-View CNN Models and mmPose—0
Optimal Estimation of Shared Singular Subspaces across Multiple Noisy Matrices—0
Multimodal Alignment and Fusion: A Survey—0
Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data SourcesCode0
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