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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 121–130 of 431 papers

TitleStatusHype
Enhancing End Stage Renal Disease Outcome Prediction: A Multi-Sourced Data-Driven Approach—0
Evaluating Blocking Biases in Entity MatchingCode0
Design and Evaluation of a CDSS for Drug Allergy Management Using LLMs and Pharmaceutical Data Integration—0
Multi-omics data integration for early diagnosis of hepatocellular carcinoma (HCC) using machine learning—0
Fine-tuning Large Language Models for Entity MatchingCode1
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
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