Amalur: Data Integration Meets Machine Learning

The increasing need for data trading across businesses nowadays has created a demand for data marketplaces. However, despite the intentions of both data providers and consumers, today’s data marketplaces remain mere data catalogs. We believe that marketplaces of the future require a set of value-added services, such as advanced search and discovery, that have been proposed in the database research community for years, but are not yet put to practice. With this paper, we report on the effort to engineer and develop an open-source modular data market platform to enable both entrepreneurs and researchers to setup and experiment with data marketplaces. To this end, we implemented and extended existing methods for data profiling, dataset search & discovery, and data recommendation.

• Paper [pdf]

• Bibtex:

  @INPROCEEDINGS{10184649,
  author={Hai, Rihan and Koutras, Christos and Ionescu, Andra and Li, Ziyu and Sun, Wenbo and van Schijndel, Jessie and Kang, Yan and Katsifodimos, Asterios},
  booktitle={2023 IEEE 39th International Conference on Data Engineering (ICDE)}, 
  title={Amalur: Data Integration Meets Machine Learning}, 
  year={2023},
  volume={},
  number={},
  pages={3729-3739},
  keywords={Training;Data privacy;Federated learning;Computational modeling;Data integration;Training data;Manuals},
  doi={10.1109/ICDE55515.2023.00301}
  }