{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T03:13:19Z","timestamp":1758078799823,"version":"3.44.0"},"reference-count":11,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:p>Real-world datasets are often fragmented across multiple heterogeneous tables, managed by different teams or organizations. Ensuring data quality in such environments is challenging, as traditional error detection tools typically operate on isolated tables and overlook cross-table relationships. To address this gap, we investigate how cleaning multiple tables simultaneously, combined with structured user collaboration, can reduce annotation effort and enhance the effectiveness and efficiency of error detection.<\/jats:p>\n          <jats:p>We present Matelda, an interactive system for multi-table error detection that combines automated error detection with human-in-the-loop refinement. Matelda guides users through Inspection &amp; Action, allowing them to explore system-generated insights, refine decisions, and annotate data with contextual support. It organizes tables using domain-based and quality-based folding and leverages semi-supervised learning to propagate labels across related tables efficiently. Our demonstration showcases Matelda's capabilities for collaborative error detection and resolution by leveraging shared knowledge, contextual similarity, and structured user interactions across multiple tables.<\/jats:p>","DOI":"10.14778\/3750601.3750676","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:38:05Z","timestamp":1758029885000},"page":"5379-5382","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Demonstrating Matelda for Multi-Table Error Detection"],"prefix":"10.14778","volume":"18","author":[{"given":"Fatemeh","family":"Ahmadi","sequence":"first","affiliation":[{"name":"TU Berlin &amp; BIFOLD, Berlin, Germany"}]},{"given":"Julian","family":"Paulu\u00dfen","sequence":"additional","affiliation":[{"name":"TU Berlin &amp; BIFOLD, Berlin, Germany"}]},{"given":"Ziawasch","family":"Abedjan","sequence":"additional","affiliation":[{"name":"TU Berlin &amp; BIFOLD, Berlin, Germany"}]}],"member":"320","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the VLDB Endowment (PVLDB)","author":"Abedjan Ziawasch","year":"2016","unstructured":"Ziawasch Abedjan, Xu Chu, Dong Deng, Raul Castro Fernandez, Ihab F. Ilyas, Mourad Ouzzani, Paolo Papotti, Michael Stonebraker, and Nan Tang. 2016. Detecting Data Errors: Where are we and what needs to be done? Proceedings of the VLDB Endowment (PVLDB) (2016)."},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the International Conference on Extending Database Technology (EDBT).","author":"Ahmadi Fatemeh","year":"2025","unstructured":"Fatemeh Ahmadi, Marc Speckmann, Malte F. Kuhlmann, and Ziawasch Abedjan. 2025. MaTElDa: Multi-Table Error Detection. In Proceedings of the International Conference on Extending Database Technology (EDBT)."},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the VLDB Endowment (PVLDB)","author":"Chu Xu","year":"2015","unstructured":"Xu Chu, Mourad Ouzzani, John Morcos, Ihab F. Ilyas, Paolo Papotti, Nan Tang, and Yin Ye. 2015. KATARA: Reliable Data Cleaning with Knowledge Bases and Crowdsourcing. Proceedings of the VLDB Endowment (PVLDB) (2015)."},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the VLDB Endowment (PVLDB)","author":"Ebaid Amr","year":"2013","unstructured":"Amr Ebaid, Ahmed K. Elmagarmid, Ihab F. Ilyas, Mourad Ouzzani, Jorge-Arnulfo Quian\u00e9-Ruiz, Nan Tang, and Si Yin. 2013. NADEEF: A Generalized Data Cleaning System. Proceedings of the VLDB Endowment (PVLDB) (2013)."},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the International Conference on Management of Data (SIGMOD).","author":"Heidari Alireza","year":"2019","unstructured":"Alireza Heidari, Joshua McGrath, Ihab F. Ilyas, and Theodoros Rekatsinas. 2019. HoloDetect: Few-Shot Learning for Error Detection. In Proceedings of the International Conference on Management of Data (SIGMOD)."},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the VLDB Endowment (PVLDB)","author":"Mahdavi Mohammad","year":"2020","unstructured":"Mohammad Mahdavi and Ziawasch Abedjan. 2020. Baran: Effective Error Correction via a Unified Context Representation and Transfer Learning. Proceedings of the VLDB Endowment (PVLDB) (2020)."},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the Conference on Innovative Data Systems Research (CIDR).","author":"Mahdavi Mohammad","year":"2021","unstructured":"Mohammad Mahdavi and Ziawasch Abedjan. 2021. Semi-Supervised Data Cleaning with Raha and Baran. In Proceedings of the Conference on Innovative Data Systems Research (CIDR)."},{"key":"e_1_2_1_8_1","volume-title":"Proceedings of the International Conference on Management of Data (SIGMOD).","author":"Mahdavi Mohammad","year":"2019","unstructured":"Mohammad Mahdavi, Ziawasch Abedjan, Raul Castro Fernandez, Samuel Madden, Mourad Ouzzani, Michael Stonebraker, and Nan Tang. 2019. Raha: A Configuration-Free Error Detection System. In Proceedings of the International Conference on Management of Data (SIGMOD)."},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the International Conference on Management of Data (SIGMOD).","author":"Schelter Sebastian","year":"2019","unstructured":"Sebastian Schelter, Felix Bie\u00dfmann, Dustin Lange, Tammo Rukat, Philipp Schmidt, Stephan Seufert, Pierre Brunelle, and Andrey Taptunov. 2019. Unit Testing Data with Deequ. In Proceedings of the International Conference on Management of Data (SIGMOD)."},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the International Conference on Management of Data (SIGMOD).","author":"Wang Pei","year":"2019","unstructured":"Pei Wang and Yeye He. 2019. Uni-Detect: A Unified Approach to Automated Error Detection in Tables. In Proceedings of the International Conference on Management of Data (SIGMOD)."},{"key":"e_1_2_1_11_1","volume-title":"Proceedings of the International Conference on Management of Data (SIGMOD).","author":"Yakout Mohamed","year":"2010","unstructured":"Mohamed Yakout, Ahmed K. Elmagarmid, Jennifer Neville, and Mourad Ouzzani. 2010. GDR: a system for guided data repair. In Proceedings of the International Conference on Management of Data (SIGMOD)."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3750601.3750676","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:42:44Z","timestamp":1758030164000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3750601.3750676"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8]]},"references-count":11,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["10.14778\/3750601.3750676"],"URL":"https:\/\/doi.org\/10.14778\/3750601.3750676","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2025,8]]},"assertion":[{"value":"2025-09-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}