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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 51–60 of 431 papers

TitleStatusHype
Towards Unified Molecule-Enhanced Pathology Image Representation Learning via Integrating Spatial TranscriptomicsCode1
eipy: An Open-Source Python Package for Multi-modal Data Integration using Heterogeneous EnsemblesCode1
AdapterEM: Pre-trained Language Model Adaptation for Generalized Entity Matching using Adapter-tuningCode0
From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer ResearchCode0
From Swath to Full-Disc: Advancing Precipitation Retrieval with Multimodal Knowledge ExpansionCode0
Evaluating Blocking Biases in Entity MatchingCode0
Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data SourcesCode0
A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep LearningCode0
Alternative Telescopic Displacement: An Efficient Multimodal Alignment MethodCode0
Evaluating approaches for supervised semantic labelingCode0
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