@inproceedings{a867aa53bde74647b0794c8dde61bca5,
title = "Demonstrating MATE and COCOA for Data Discovery",
abstract = "One of the common use cases for data discovery is to enrich a given table with additional columns from related tables inside a data lake. We have recently introduced MATE and COCOA, two systems for joinability discovery and correlation calculation, respectively. By leveraging two novel index structures, a hash-based Super Key Index, and an Order Index, our system is capable of efficiently identifying tables that join on multiple columns and contain relevant features. We show how the data exploration and enrichment process benefits from our index structures by demonstrating MaCo, a unified system on top of open web and large table corpora.",
keywords = "data discovery for ML, data integration, index structures",
author = "Jannis Becktepe and Mahdi Esmailoghli and Maximilian Koch and Ziawasch Abedjan",
note = "Funding Information: This project has been supported by the German Research Foundation (DFG) under grant agreement 387872445.; 2023 ACM/SIGMOD International Conference on Management of Data, SIGMOD 2023 ; Conference date: 18-06-2023 Through 23-06-2023",
year = "2023",
month = jun,
day = "5",
doi = "10.1145/3555041.3589716",
language = "English",
series = "Proceedings of the ACM SIGMOD International Conference on Management of Data",
publisher = "Association for Computing Machinery (ACM)",
pages = "119--122",
booktitle = "SIGMOD '23",
address = "United States",
}