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Data Integration
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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:
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Empowering Digital Agriculture: A Privacy-Preserving Framework for Data Sharing and Collaborative Research
Intelligent Operation and Maintenance and Prediction Model Optimization for Improving Wind Power Generation Efficiency
An introduction to Causal Modelling
Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs
Brain Imaging Foundation Models, Are We There Yet? A Systematic Review of Foundation Models for Brain Imaging and Biomedical Research
Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises
Pushing the Limits of Extreme Weather: Constructing Extreme Heatwave Storylines with Differentiable Climate Models
NoLoCo: No-all-reduce Low Communication Training Method for Large Models
Enhancing Bagging Ensemble Regression with Data Integration for Time Series-Based Diabetes Prediction
scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data
The Cell Ontology in the age of single-cell omics
From Swath to Full-Disc: Advancing Precipitation Retrieval with Multimodal Knowledge Expansion
KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes
Matching Markets Meet LLMs: Algorithmic Reasoning with Ranked Preferences
Challenges in Automated Processing of Speech from Child Wearables: The Case of Voice Type Classifier
The Coupling Effect of Sensing Targets on the Environment for 3GPP ISAC Channels: Observation, Modeling, and Validation
Multi-task Learning for Heterogeneous Data via Integrating Shared and Task-Specific Encodings
Decoding Knowledge Attribution in Mixture-of-Experts: A Framework of Basic-Refinement Collaboration and Efficiency Analysis
Towards Scalable Schema Mapping using Large Language Models
Open CaptchaWorld: A Comprehensive Web-based Platform for Testing and Benchmarking Multimodal LLM Agents
Towards Unified Neural Decoding with Brain Functional Network Modeling
Sensitivity of DC Network Representation for GIC Analysis
Evaluating AI capabilities in detecting conspiracy theories on YouTube
ChatPD: An LLM-driven Paper-Dataset Networking System
Derailing Non-Answers via Logit Suppression at Output Subspace Boundaries in RLHF-Aligned Language Models
Streamlining Knowledge Graph Creation with PyRML
Streamlining Resilient Kubernetes Autoscaling with Multi-Agent Systems via an Automated Online Design Framework
Towards a Spatiotemporal Fusion Approach to Precipitation Nowcasting
Generative RLHF-V: Learning Principles from Multi-modal Human Preference
Multimodal Generative AI for Story Point Estimation in Software Development
Control of Renewable Energy Communities using AI and Real-World Data
AI-empowered Channel Estimation for Block-based Active IRS-enhanced Hybrid-field IoT Network
A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
Urban Representation Learning for Fine-grained Economic Mapping: A Semi-supervised Graph-based Approach
ELIS: Efficient LLM Iterative Scheduling System with Response Length Predictor
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