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Query-Guided Resolution in Uncertain Databases

Authors:

Matanya Freiman,

Antoine Amarilli,

Yael AmsterdamerAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 1, Issue 2

Article No.: 180, Pages 1 - 27

https://doi.org/10.1145/3589325

Published: 20 June 2023 Publication History

Abstract

We present a novel framework for uncertain data management. We start with a database whose tuple correctness is uncertain and an oracle that can resolve the uncertainty, i.e., decide if a tuple is correct or not. Such an oracle may correspond, e.g., to a data expert or to a crowdsourcing platform. We wish to use the oracle to clean the database with the goal of ensuring the correct answer for specific mission-critical queries. To avoid the prohibitive cost of cleaning the entire database and to minimize the expected number of calls to the oracle, we must carefully select tuples whose resolution would suffice to resolve the uncertainty in query results. In other words, we need a query-guided process for the resolution of uncertain data.

We develop an end-to-end solution to this problem, based on the derivation of query answers and on correctness probabilities for the uncertain data. At a high level, we first track Boolean provenance to identify which input tuples contribute to the derivation of each output tuple, and in what ways. We then design an active learning solution for iteratively choosing tuples to resolve, based on the provenance structure and on an evolving estimation of tuple correctness probabilities. We conduct an extensive experimental study to validate our framework in different use cases.

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We present a novel framework for an end-to-end solution for uncertain mission-critical data management. We start with a database whose tuple correctness is uncertain and an oracle (e.g., to a data expert or to a crowdsourcing platform) that can resolve the uncertainty, i.e., decide if a tuple is correct or not. We wish to use the oracle to clean the database with the goal of ensuring the correct answer for specific mission-critical queries. Our goal is to compute the precise ground truth query result while performing a minimal number of sequential oracles probes.

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References

[1]

Serge Abiteboul, Richard Hull, and Victor Vianu. 1995. Foundations of Databases. Addison-Wesley.

Digital Library

[2]

Foto N. Afrati and Phokion G. Kolaitis. 2009. Repair Checking in Inconsistent Databases: Algorithms and Complexity. In ICDT. 31--41.

[3]

Parag Agrawal, Omar Benjelloun, Anish Das Sarma, Chris Hayworth, Shubha U. Nabar, Tomoe Sugihara, and Jennifer Widom. 2006. Trio: A System for Data, Uncertainty, and Lineage. In PVLDB. 1151--1154.

Digital Library

[4]

Sarah R. Allen, Lisa Hellerstein, Devorah Kletenik, and Tonguç Ünlüyurt. 2017. Evaluation of Monotone DNF Formulas. Algorithmica 77, 3 (2017), 661--685.

Digital Library

[5]

Yael Amsterdamer, Susan B. Davidson, Daniel Deutch, Tova Milo, Julia Stoyanovich, and Val Tannen. 2011. Putting Lipstick on Pig: Enabling Database-style Workflow Provenance. PVLDB 5, 4 (2011), 346--357.

Digital Library

[6]

Yael Amsterdamer, Daniel Deutch, and Val Tannen. 2011. Provenance for Aggregate Queries. In PODS. 153--164.

[7]

Marcelo Arenas, Leopoldo E. Bertossi, and Jan Chomicki. 2003. Answer Sets for Consistent Query Answering in Inconsistent Databases. Theory Pract. Log. Program. 3, 4--5 (2003), 393--424.

Digital Library

[8]

Ahmad Assadi, Tova Milo, and Slava Novgorodov. 2018. Cleaning Data with Constraints and Experts. In WebDB. 1:1--1:6.

[9]

Adam Bates, Benjamin Mood, Masoud Valafar, and Kevin R. B. Butler. 2013. Towards Secure Provenance-Based Access Control in Cloud Environments. In CODASPY. 277--284.

[10]

Omar Benjelloun, Anish Das Sarma, Alon Y. Halevy, Martin Theobald, and Jennifer Widom. 2008. Databases With Uncertainty and Lineage. VLDB J. 17, 2 (2008), 243--264.

Digital Library

[11]

Moria Bergman, Tova Milo, Slava Novgorodov, and Wang Chiew Tan. 2015. Query-Oriented Data Cleaning with Oracles. In SIGMOD. 1199--1214.

[12]

Leopoldo E. Bertossi, Solmaz Kolahi, and Laks V. S. Lakshmanan. 2013. Data Cleaning and Query Answering with Matching Dependencies and Matching Functions. Theory Comput. Syst. 52, 3 (2013), 441--482.

Digital Library

[13]

Michael Bloodgood. 2018. Support Vector Machine Active Learning Algorithms with Query-by-Committee Versus Closest-to-Hyperplane Selection. In ICSC. 148--155.

[14]

Piero A. Bonatti, Luca Ioffredo, Iliana M. Petrova, Luigi Sauro, and Ida Sri Rejeki Siahaan. 2020. Real-Time Reasoning in OWL2 for GDPR Compliance. Artif. Intell. 289 (2020), 103389.

[15]

Endre Boros and Tonguç Ünlüyurt. 2000. Sequential Testing of Series-Parallel Systems of Small Depth. In Computing tools for modeling, optimization and simulation. Springer, 39--73.

[16]

Leo Breiman. 2001. Random Forests. Machine Learning 45, 1 (2001), 5--32.

Digital Library

[17]

Peter Buneman, Sanjeev Khanna, and Wang Chiew Tan. 2001. Why and Where: A Characterization of Data Provenance. In ICDT. 316--330.

[18]

James Cheney, Laura Chiticariu, and Wang Chiew Tan. 2009. Provenance in Databases: Why, How, and Where. Foundations and Trends in Databases 1, 4 (2009), 379--474.

Digital Library

[19]

Reynold Cheng, Jinchuan Chen, and Xike Xie. 2008. Cleaning Uncertain Data with Quality Guarantees. PVLDB 1, 1 (2008), 722--735.

Digital Library

[20]

Reynold Cheng, Eric Lo, Xuan S. Yang, Ming-Hay Luk, Xiang Li, and Xike Xie. 2010. Explore or Exploit? Effective Strategies for Disambiguating Large Databases. PVLDB 3, 1 (2010), 815--825.

Digital Library

[21]

Xu Chu, Ihab F. Ilyas, and Paolo Papotti. 2013. Holistic Data Cleaning: Putting Violations Into Context. In ICDE. 458--469.

[22]

Xu Chu, John Morcos, Ihab F. Ilyas, Mourad Ouzzani, Paolo Papotti, Nan Tang, and Yin Ye. 2015. KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing. In SIGMOD. 1247--1261.

Digital Library

[23]

Ferdinando Cicalese, Eduardo Sany Laber, and Aline Medeiros Saettler. 2014. Diagnosis determination: decision trees optimizing simultaneously worst and expected testing cost. In ICML. 414--422.

[24]

Nilesh N. Dalvi and Dan Suciu. 2007. Efficient Query Evaluation on Probabilistic Databases. VLDB J. 16, 4 (2007), 523--544.

Digital Library

[25]

Dave DeBarr and H. Wechsler. 2009. Spam Detection using Clustering, Random Forests, and Active Learning. In CEAS.

[26]

Ofer Dekel, Claudio Gentile, and Karthik Sridharan. 2012. Selective Sampling and Active Learning from Single and Multiple Teachers. J. Mach. Learn. Res. 13 (2012), 2655--2697.

Digital Library

[27]

Guy Van den Broeck and Dan Suciu. 2017. Query Processing on Probabilistic Data: A Survey. Found. Trends DBs 7, 3--4 (2017), 197--341.

[28]

Amol Deshpande, Lisa Hellerstein, and Devorah Kletenik. 2014. Approximation Algorithms for Stochastic Boolean Function Evaluation and Stochastic Submodular Set Cover. In SODA. 1453--1466.

[29]

Daniel Deutch, Nave Frost, and Amir Gilad. 2017. Provenance for Natural Language Queries. PVLDB 10, 5 (2017), 577--588.

Digital Library

[30]

Daniel Deutch, Tova Milo, Sudeepa Roy, and Val Tannen. 2014. Circuits for Datalog Provenance. In ICDT. 201--212.

[31]

Osnat Drien, Antoine Amarilli, and Yael Amsterdamer. 2021. Managing Consent for Data Access in Shared Databases. In ICDE. 2012--2015.

[32]

Anna Fariha, Ashish Tiwari, Alexandra Meliou, Arjun Radhakrishna, and Sumit Gulwani. 2021. CoCo: Interactive Exploration of Conformance Constraints for Data Understanding and Data Cleaning. In SIGMOD. 2706--2710.

[33]

Kaniz Fatema, Ensar Hadziselimovic, Harshvardhan J. Pandit, Christophe Debruyne, Dave Lewis, and Declan O'Sullivan. 2017. Compliance through Informed Consent: Semantic Based Consent Permission and Data Management Model. In PrivOn at ISWC.

[34]

Amos Fiat and Dmitry Pechyony. 2004. Decision Trees: More Theoretical Justification for Practical Algorithms. In ALT. 156--170.

[35]

J. Nathan Foster, Todd J. Green, and Val Tannen. 2008. Annotated XML: Queries and Provenance. In PODS. 271--280.

[36]

Michael J. Franklin, Donald Kossmann, Tim Kraska, Sukriti Ramesh, and Reynold Xin. 2011. CrowdDB: Answering Queries with Crowdsourcing. In SIGMOD. 61--72.

[37]

Floris Geerts, Giansalvatore Mecca, Paolo Papotti, and Donatello Santoro. 2020. Cleaning Data with Llunatic. VLDB J. 29, 4 (2020), 867--892.

[38]

Shima Gerani, Mark James Carman, and F. Crestani. 2010. Proximity-Based Opinion Retrieval. SIGIR (2010).

[39]

Amir Gilad, Daniel Deutch, and Sudeepa Roy. 2020. On Multiple Semantics for Declarative Database Repairs. In SIGMOD. 817--831.

[40]

Boris Glavic and Gustavo Alonso. 2009. Perm: Processing Provenance and Data on the Same Data Model through Query Rewriting. In ICDE. 174--185.

[41]

Boris Glavic and Gustavo Alonso. 2009. Provenance for Nested Subqueries. In EDBT. 982--993.

[42]

Abigail Goldsteen, Shelly Garion, Sima Nadler, Natalia Razinkov, Yosef Moatti, and Paula Ta-Shma. 2017. Brief Announcement: A Consent Management Solution for Enterprises. In CSCML. 189--192.

[43]

Daniel Golovin and Andreas Krause. 2011. Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization. JAIR 42 (2011), 427--486.

Digital Library

[44]

Daniel Golovin, Andreas Krause, and Debajyoti Ray. 2010. Near-Optimal Bayesian Active Learning with Noisy Observations. In NeurIPS. Curran Associates, Inc., 766--774.

[45]

Sergio Greco, Cristian Molinaro, and Irina Trubitsyna. 2017. Computing Approximate Certain Answers over Incomplete Databases. In AMW, Vol. 1912.

[46]

Todd J. Green, Grigoris Karvounarakis, Zachary G. Ives, and Val Tannen. 2010. Provenance in ORCHESTRA. IEEE Data Eng. Bull. 33, 3 (2010), 9--16.

[47]

Todd J. Green, Gregory Karvounarakis, and Val Tannen. 2007. Provenance Semirings. In PODS. 31--40.

[48]

Husheng Guo and Wenjian Wang. 2015. An Active Learning-Based SVM Multi-Class Classification Model. Pattern Recognit. 48, 5 (2015), 1577--1597.

Digital Library

[49]

Naeemul Hassan, Gensheng Zhang, Fatma Arslan, Josue Caraballo, Damian Jimenez, Siddhant Gawsane, Shohedul Hasan, Minumol Joseph, Aaditya Kulkarni, Anil Kumar Nayak, Vikas Sable, Chengkai Li, and Mark Tremayne. 2017. ClaimBuster: The First-ever End-to-end Fact-checking System. PVLDB 10, 12 (2017), 1945--1948.

Digital Library

[50]

Melanie Herschel, Ralf Diestelkämper, and Houssem Ben Lahmar. 2017. A Survey on Provenance: What for? What Form? What from? VLDB J. 26, 6 (2017), 881--906.

Digital Library

[51]

Ihab F. Ilyas and Xu Chu. 2019. Data Cleaning. ACM.

Digital Library

[52]

Tomasz Imielinski and Witold Lipski Jr. 1984. Incomplete Information in Relational Databases. JACM 31, 4 (1984), 761--791.

Digital Library

[53]

Haim Kaplan, Eyal Kushilevitz, and Yishay Mansour. 2005. Learning with Attribute Costs. In STOC. 356--365.

[54]

Grigoris Karvounarakis, Zachary G. Ives, and Val Tannen. 2010. Querying Data Provenance. In SIGMOD. 951--962.

[55]

O. Keren. 2008. Reduction of Average Path Length in Binary Decision Diagrams by Spectral Methods. IEEE TOCS 57 (2008), 520--531.

Digital Library

[56]

Christoph Koch and Dan Olteanu. 2008. Conditioning Probabilistic Databases. PVLDB 1, 1 (2008), 313--325.

Digital Library

[57]

Daphne Koller. 1999. Probabilistic Relational Models. In ILP. 3--13.

[58]

George Konstantinidis, Jet Holt, and Adriane Chapman. 2021. Enabling Personal Consent in Databases. PVLDB 15, 2 (2021), 375--387.

Digital Library

[59]

Ksenia Konyushkova, Raphael Sznitman, and Pascal Fua. 2017. Learning Active Learning from Data. In NIPS. 4225--4235.

[60]

Sanjay Krishnan, Jiannan Wang, Eugene Wu, Michael J. Franklin, and Ken Goldberg. 2016. ActiveClean: Interactive Data Cleaning For Statistical Modeling. PVLDB 9, 12 (2016), 948--959.

Digital Library

[61]

Jasper Kuperus, Cor J. Veenman, and Maurice van Keulen. 2013. Increasing NER Recall with Minimal Precision Loss. In EISIC. 106--111.

[62]

Guoliang Li, Chengliang Chai, Ju Fan, Xueping Weng, Jian Li, Yudian Zheng, Yuanbing Li, Xiang Yu, Xiaohang Zhang, and Haitao Yuan. 2018. CDB: A Crowd-Powered Database System. PVLDB 11, 12 (2018), 1926--1929.

[63]

Xiang Lian, Lei Chen, and Shaoxu Song. 2010. Consistent Query Answers in Inconsistent Probabilistic Databases. In SIGMOD. 303--314.

[64]

Xin Lin, Yun Peng, Jianliang Xu, and Byron Choi. 2018. Human-Powered Data Cleaning for Probabilistic Reachability Queries on Uncertain Graphs. In ICDE. 1755--1756.

[65]

Ye-Zheng Liu, Zhe Li, Chong Zhou, Yuanchun Jiang, Jianshan Sun, Meng Wang, and Xiangnan He. 2020. Generative Adversarial Active Learning for Unsupervised Outlier Detection. IEEE TKDE 32, 8 (2020), 1517--1528.

[66]

Ester Livshits, Benny Kimelfeld, and Sudeepa Roy. 2018. Computing Optimal Repairs for Functional Dependencies. In PODS. 225--237.

[67]

A. Marcus, E. Wu, D.R. Karger, S. Madden, and R.C. Miller. 2011. Crowdsourced Databases: Query Processing with People. In CIDR.

[68]

Alexandra Meliou, Wolfgang Gatterbauer, and Dan Suciu. 2011. Bringing Provenance to Its Full Potential Using Causal Reasoning. In TaPP.

[69]

Tom M. Mitchell, William W. Cohen, Estevam R. Hruschka Jr., Partha P. Talukdar, Bo Yang, Justin Betteridge, Andrew Carlson, Bhavana Dalvi Mishra, Matt Gardner, Bryan Kisiel, Jayant Krishnamurthy, Ni Lao, Kathryn Mazaitis, Thahir Mohamed, Ndapandula Nakashole, Emmanouil A. Platanios, Alan Ritter, Mehdi Samadi, Burr Settles, Richard C. Wang, Derry Wijaya, Abhinav Gupta, Xinlei Chen, Abulhair Saparov, Malcolm Greaves, and Joel Welling. 2018. Never-Ending Learning. Commun. ACM 61, 5 (2018), 103--115.

Digital Library

[70]

Luyi Mo, Reynold Cheng, Xiang Li, David W. Cheung, and Xuan S. Yang. 2013. Cleaning Uncertain Data for Top-k Queries. In ICDE. 134--145.

[71]

Felix Neutatz, Binger Chen, Ziawasch Abedjan, and Eugene Wu. 2021. From Cleaning before ML to Cleaning for ML. IEEE Data Eng. Bull. 44, 1 (2021), 24--41.

[72]

Federico Olmedo, Friedrich Gretz, Nils Jansen, Benjamin Lucien Kaminski, Joost-Pieter Katoen, and Annabelle McIver. 2018. Conditioning in Probabilistic Programming. TOPLAS 40, 1 (2018), 1--50.

Digital Library

[73]

Natalia Ostapuk, Jie Yang, and Philippe Cudré-Mauroux. 2019. ActiveLink: Deep Active Learning for Link Prediction in Knowledge Graphs. In WWW. 1398--1408.

[74]

Harshvardhan J. Pandit, Declan O'Sullivan, and Dave Lewis. 2018. Queryable Provenance Metadata For GDPR Compliance. In SEMANTICS. 262--268.

[75]

Katerina Papaioannou, Martin Theobald, and Michael H. Böhlen. 2018. Supporting Set Operations in Temporal- Probabilistic Databases. In ICDE. 1180--1191.

[76]

Aditya G. Parameswaran, Hyunjung Park, Hector Garcia-Molina, Neoklis Polyzotis, and Jennifer Widom. 2012. Deco: Declarative Crowdsourcing. In CIKM.

[77]

Jaehong Park, Dang Nguyen, and Ravi S. Sandhu. 2012. A Provenance-Based Access Control Model. In PST. 137--144.

[78]

Eugenia A. Politou, Efthimios Alepis, and Constantinos Patsakis. 2018. Forgetting Personal Data and Revoking Consent under the GDPR: Challenges and Proposed Solutions. J. Cybersecur. 4, 1 (2018), tyy001.

[79]

Christopher Re, Nilesh Dalvi, and Dan Suciu. 2007. Efficient Top-k Query Evaluation on Probabilistic Data. In ICDE. 886--895.

[80]

Theodoros Rekatsinas, Xu Chu, Ihab F. Ilyas, and Christopher Ré. 2017. HoloClean: Holistic Data Repairs with Probabilistic Inference. PVLDB 10, 11 (2017), 1190--1201.

Digital Library

[81]

El Kindi Rezig, Mourad Ouzzani, Walid G. Aref, Ahmed K. Elmagarmid, Ahmed R. Mahmood, and Michael Stonebraker. 2021. Horizon: Scalable Dependency-driven Data Cleaning. PVLDB 14, 11 (2021), 2546--2554.

Digital Library

[82]

Sudeepa Roy, Vittorio Perduca, and Val Tannen. 2011. Faster Query Answering in Probabilistic Databases Using Read-Once Functions. In ICDT. 232--243.

[83]

Rodrygo L. T. Santos, Ben He, Craig Macdonald, and Iadh Ounis. 2009. Integrating Proximity to Subjective Sentences for Blog Opinion Retrieval. In ECIR. 325--336.

[84]

Pierre Senellart, Louis Jachiet, Silviu Maniu, and Yann Ramusat. 2018. ProvSQL: Provenance and Probability Management in PostgreSQL. PVLDB 11, 12 (2018), 2034--2037.

Digital Library

[85]

Yanyao Shen, Hyokun Yun, Zachary C. Lipton, Yakov Kronrod, and Animashree Anandkumar. 2018. Deep Active Learning for Named Entity Recognition. In ICLR.

[86]

Yasuhiro Sogawa, Tsuyoshi Ueno, Yoshinobu Kawahara, and Takashi Washio. 2013. Active Learning for Noisy Oracle via Density Power Divergence. Neural Networks 46 (2013), 133--143.

Digital Library

[87]

sourcecode 2022. Source code repository. https://github.com/osnatairy/Active-Probabilistic-Databases.git.

[88]

Dan Suciu, Dan Olteanu, Christopher Ré, and Christoph Koch. 2011. Probabilistic Databases. Morgan & Claypool.

[89]

Ruiming Tang, Reynold Cheng, Huayu Wu, and Stéphane Bressan. 2012. A Framework for Conditioning Uncertain Relational Data. In DEXA, Vol. 7447. 71--87.

[90]

TPC-H 2019. TPC-H Benchmark. http://www.tpc.org/tpch/.

[91]

Tonguç Ünlüyurt. 2004. Sequential Testing of Complex Systems: A Review. Discrete Applied Mathematics 142, 1--3 (2004), 189--205.

[92]

Maurice van Keulen, Benjamin Lucien Kaminski, Christoph Matheja, and Joost-Pieter Katoen. 2018. Rule-Based Conditioning of Probabilistic Data. In SUM, Vol. 11142. 290--305.

[93]

Joannès Vermorel and M. Mohri. 2005. Multi-armed Bandit Algorithms and Empirical Evaluation. In ECML.

[94]

Jiannan Wang, Sanjay Krishnan, Michael J. Franklin, Ken Goldberg, Tim Kraska, and Tova Milo. 2014. A Sample-and- Clean Framework for Fast and Accurate Query Processing on Dirty Data. In SIGMOD. 469--480.

[95]

Geoffrey I Webb, Eamonn Keogh, and Risto Miikkulainen. 2010. Naïve Bayes. Encyclopedia of Machine Learning 15 (2010), 713--714.

[96]

Jef Wijsen. 2019. Foundations of Query Answering on Inconsistent Databases. SIGMOD Rec. 48, 3 (2019), 6--16.

Digital Library

[97]

Jie Xu, Dmitri V. Kalashnikov, and Sharad Mehrotra. 2015. Query Aware Determinization of Uncertain Objects. IEEE TKDE 27, 1 (2015), 207--221.

[98]

Mohamed Yakout, Laure Berti-Équille, and Ahmed K. Elmagarmid. 2013. Don't be SCAREd: use SCalable Automatic REpairing with maximal likelihood and bounded changes. In SIGMOD. 553--564.

[99]

Wei Zhang, Clement Yu, and Weiyi Meng. 2007. Opinion Retrieval from Blogs. In CIKM. 831.

[100]

Nan Zheng and Zack Ives. 2020. Compact, Tamper-Resistant Archival of Fine-Grained Provenance. PVLDB 14, 4 (2020), 485--497.

Digital Library

[101]

Hong Zhu, Caicai Zhang, Zhongsheng Cao, and Ruiming Tang. 2016. On Efficient Conditioning of Probabilistic Relational Databases. Knowledge-Based Systems 92 (2016), 112--126.

Digital Library

Cited By

Scharf CDomshlak CGal ARoitman H(2025)A Rank-Based Approach to Recommender System's Top-K Queries with Uncertain ScoresProceedings of the ACM on Management of Data10.1145/37096553:1(1-26)Online publication date: 11-Feb-2025
https://dl.acm.org/doi/10.1145/3709655
Schreiber RAmsterdamer Y(2024)CleanEr: Interactive, Query-Guided Error Mitigation for Data Cleaning Systems2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00418(5421-5424)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00418

Index Terms

Query-Guided Resolution in Uncertain Databases

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 1, Issue 2

PACMMOD

June 2023

2310 pages

EISSN:2836-6573

DOI:10.1145/3605748

Editor:
Divyakant Agrawal
UC Santa Barbara, United States

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Published: 20 June 2023

Published in PACMMOD Volume 1, Issue 2

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Scharf CDomshlak CGal ARoitman H(2025)A Rank-Based Approach to Recommender System's Top-K Queries with Uncertain ScoresProceedings of the ACM on Management of Data10.1145/37096553:1(1-26)Online publication date: 11-Feb-2025
https://dl.acm.org/doi/10.1145/3709655
Schreiber RAmsterdamer Y(2024)CleanEr: Interactive, Query-Guided Error Mitigation for Data Cleaning Systems2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00418(5421-5424)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00418

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