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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

Showing 231240 of 431 papers

TitleStatusHype
Is your data alignable? Principled and interpretable alignability testing and integration of single-cell dataCode1
Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case0
Crowd Safety Manager: Towards Data-Driven Active Decision Support for Planning and Control of Crowd Events0
BIM-to-BRICK: Using graph modeling for IoT/BMS and spatial semantic data interoperability within digital data models of buildings0
A Primer on the Data Cleaning Pipeline0
Conditionally Invariant Representation Learning for Disentangling Cellular Heterogeneity0
Alternative Telescopic Displacement: An Efficient Multimodal Alignment MethodCode0
A Comparison of Neuroelectrophysiology Databases0
A survey on deep learning approaches for data integration in autonomous driving system0
Cross Modal Data Discovery over Structured and Unstructured Data LakesCode0
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