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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 251–260 of 431 papers

TitleStatusHype
Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs—0
Scalable Similarity Joins of Tokenized Strings—0
Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case—0
scICML: Information-theoretic Co-clustering-based Multi-view Learning for the Integrative Analysis of Single-cell Multi-omics data—0
Secure and Differentially Private Bayesian Learning on Distributed Data—0
Segment-based fusion of multi-sensor multi-scale satellite soil moisture retrievals—0
Semantic Annotation for Tabular Data—0
Semantic Data Management in Data Lakes—0
Siamese Graph Neural Networks for Data Integration—0
Simplifying Data Integration: SLM-Driven Systems for Unified Semantic Queries Across Heterogeneous Databases—0
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