Posts

Tutorial 5: Quantum data management: from theory to opportunities Full paper on Arxiv Slides Abstract: Quantum computing has emerged as a transformative tool for future data management. Classical problems in database domains, including query optimization, data integration, and transaction management, have recently been addressed using techniques from quantum computing.

Extended from our vision paper at ICDE 2024, the full version of Amalur is now available at TKDE! Amalur: The Convergence of Data Integration and Machine Learning By Ziyu Li, Wenbo Sun, Danning Zhan, Yan Kang, Lydia Chen, Alessandro Bozzon, and Rihan Hai

Model Selection with Model Zoo via Graph Learning By Ziyu Li, Hilco van der Wilk, Danning Zhan, Megha Khosla, Alessandro Bozzon, Rihan Hai Abstract—Pre-trained deep learning (DL) models are increasingly accessible in public repositories, i.

Join us at the TKDE poster session at ICDE24 Data Lakes: A Survey of Functions and Systems (Extended abstract) Rihan Hai (TU Delft); Christos Koutras (TU Delft); Christoph Quix (Hochule Niederrhein University of Applied Sciences); Matthias Jarke (RWTH Aachen University) Full paper

SiloFuse: Cross-silo Synthetic Data Generation with Latent Tabular Diffusion Models by Aditya Shankar, Hans Brouwer, Rihan Hai, Lydia Chen Abstract—Synthetic tabular data is crucial for sharing and augmenting data across silos, especially for enterprises with proprietary data.

Cross-Source ML Model Training By Wenbo Sun, Rihan Hai Machine learning (ML) training data is often fragmented across various sources. Typically, ML models over disparate sources are trained using one of two paradigms: distributed or centralized.

AutoFeat: Transitive Feature Discovery over Join Paths by Andra Ionescu, Kiril Vasilev, Florena Buse, Rihan Hai, Asterios Katsifodimos Abstract—Can we automatically discover machine learning (ML) features in a large data lake in order to increase the accuracy of a given ML model?

Will Sharing Metadata Leak Privacy? By Danning Zhan and Rihan Hai Abstract—In the dynamic field of data management and machine learning, achieving a balance between effective data use and privacy preservation is increasingly crucial.

Are you interested in the past, present, and future of data lakes? Check out our survey accepted at IEEE Transactions on Knowledge and Data Engineering (TKDE). Data Lakes: A Survey of Functions and Systems

Our vision paper “Amalur: Data Integration Meets Machine Learning” is accepted at ICDE’23 (https://icde2023.ics.uci.edu/) Abstract: The data needed for machine learning (ML) model training, can reside in different separate sites often termed data silos.