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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 101–125 of 431 papers

TitleStatusHype
Ontology Population using LLMs—0
Neural decoding from stereotactic EEG: accounting for electrode variability across subjects—0
Capturing and Anticipating User Intents in Data Analytics via Knowledge Graphs—0
Novel Architecture for Distributed Travel Data Integration and Service Provision Using Microservices—0
Matchmaker: Self-Improving Large Language Model Programs for Schema Matching—0
Graph Integration for Diffusion-Based Manifold AlignmentCode0
Multimodal Quantum Natural Language Processing: A Novel Framework for using Quantum Methods to Analyse Real DataCode0
Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction—0
metasnf: Meta Clustering with Similarity Network Fusion in R—0
Development of CODO: A Comprehensive Tool for COVID-19 Data Representation, Analysis, and Visualization—0
The S2 Hierarchical Discrete Global Grid as a Nexus for Data Representation, Integration, and Querying Across Geospatial Knowledge Graphs—0
KcMF: A Knowledge-compliant Framework for Schema and Entity Matching with Fine-tuning-free LLMs—0
Unified Representation of Genomic and Biomedical Concepts through Multi-Task, Multi-Source Contrastive Learning—0
InstructBioMol: Advancing Biomolecule Understanding and Design Following Human Instructions—0
Empowering Cognitive Digital Twins with Generative Foundation Models: Developing a Low-Carbon Integrated Freight Transportation System—0
Multilayer network approaches to omics data integration in Digital Twins for cancer research—0
Deep learning-based Visual Measurement Extraction within an Adaptive Digital Twin Framework from Limited Data Using Transfer Learning—0
Assumption-Lean Post-Integrated Inference with Negative Control Outcomes—0
Comparative Analysis of Multi-Omics Integration Using Advanced Graph Neural Networks for Cancer ClassificationCode0
Nested Deep Learning Model Towards A Foundation Model for Brain Signal Data—0
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
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