Data Integration

65 papers with code • 0 benchmarks • 7 datasets

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:

Most implemented papers

MIMIC-III, a freely accessible critical care database

mit-lcp/mimic-iii-paper Nature 2016

MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital.

Bayesian Hybrid Matrix Factorisation for Data Integration

ThomasBrouwer/HMF 17 Apr 2017

We introduce a novel Bayesian hybrid matrix factorisation model (HMF) for data integration, based on combining multiple matrix factorisation methods, that can be used for in- and out-of-matrix prediction of missing values.

COMO: A Pipeline for Multi-Omics Data Integration in Metabolic Modeling and Drug Discovery

helikarlab/como 4 Nov 2020

Identifying potential drug targets using metabolic modeling requires integrating multiple modeling methods and heterogenous biological datasets, which can be challenging without sophisticated tools.

Scalable Randomized Kernel Methods for Multiview Data Integration and Prediction

lasandrall/randmvlearn 10 Apr 2023

We develop scalable randomized kernel methods for jointly associating data from multiple sources and simultaneously predicting an outcome or classifying a unit into one of two or more classes.

Heter-LP: A heterogeneous label propagation algorithm and its application in drug repositioning

dkrlab/Heter-LP-code 8 Nov 2016

As a result, it is necessary for drug development studies to conduct an investigation into the interrelationships of drugs, protein targets, and diseases.

Neuro-symbolic representation learning on biological knowledge graphs

bio-ontology-research-group/walking-rdf-and-owl 13 Dec 2016

Motivation: Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries.

A Unified Joint Matrix Factorization Framework for Data Integration

dugzzuli/jmf 25 Jul 2017

In this paper, we introduce a sparse multiple relationship data regularized joint matrix factorization (JMF) framework and two adapted prediction models for pattern recognition and data integration.

Evaluating approaches for supervised semantic labeling

NICTA/serene-benchmark 29 Jan 2018

Relational data sources are still one of the most popular ways to store enterprise or Web data, however, the issue with relational schema is the lack of a well-defined semantic description.

Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models

GeorgeMichailidis/JMMLE_code 9 Mar 2018

Following this, we develop a debiasing technique and asymptotic distributions of inter-layer directed edge weights that utilize already computed neighborhood selection coefficients for nodes in the upper layer.

Leveraging Legacy Data to Accelerate Materials Design via Preference Learning

tsudalab/PrefInt 25 Oct 2019

Machine learning applications in materials science are often hampered by shortage of experimental data.