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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 311–320 of 431 papers

TitleStatusHype
Local Embeddings for Relational Data Integration—0
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities—0
Making Table Understanding Work in Practice—0
Mapping-equivalence and oid-equivalence of single-function object-creating conjunctive queries—0
Matchmaker: Self-Improving Large Language Model Programs for Schema Matching—0
Maximum Temperature Prediction Using Remote Sensing Data Via Convolutional Neural Network—0
metasnf: Meta Clustering with Similarity Network Fusion in R—0
Mind the Data Gap: Bridging LLMs to Enterprise Data Integration—0
MISFEAT: Feature Selection for Subgroups with Systematic Missing Data—0
MMREC: LLM Based Multi-Modal Recommender System—0
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