124 citations · 125 across the 4 of their papers we have counts for
5 papers
OmniMatch: Effective Self-Supervised Any-Join Discovery in Tabular Data Repositories
Christos Koutras, Jiani Zhang, Xiao Qin +5
How can we discover join relationships among columns of tabular data in a data repository? Can this be done effectively when metadata is missing? Traditional column matching works…
SiMa: Effective and Efficient Matching Across Data Silos Using Graph Neural Networks
Christos Koutras, Rihan Hai, Kyriakos Psarakis +2
How can we leverage existing column relationships within silos, to predict similar ones across silos? Can we do this efficiently and effectively? Existing matching approaches do no…
Amalur: Data Integration Meets Machine Learning
Rihan Hai, Christos Koutras, Andra Ionescu +5
The data needed for machine learning (ML) model training, can reside in different separate sites often termed data silos. For data-intensive ML applications, data silos pose a majo…
Data Lakes: A Survey of Functions and Systems
Rihan Hai, Christos Koutras, Christoph Quix +1
Data lakes are becoming increasingly prevalent for big data management and data analytics. In contrast to traditional 'schema-on-write' approaches such as data warehouses, data lak…
Valentine: Evaluating Matching Techniques for Dataset Discovery
Christos Koutras, George Siachamis, Andra Ionescu +6
Data scientists today search large data lakes to discover and integrate datasets. In order to bring together disparate data sources, dataset discovery methods rely on some form of…