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  • Li Y, Wu X, Jin Y, Li J, Li G and Feng J. (2021). Adaptive algorithms for crowd-aided categorization. The VLDB Journal — The International Journal on Very Large Data Bases. 31:6. (1311-1337). Online publication date: 1-Nov-2022.

    https://doi.org/10.1007/s00778-021-00685-2

  • Cong Q, Tang J, Han K, Huang Y, Chen L and Chee Y. Noisy Interactive Graph Search. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (231-240).

    https://doi.org/10.1145/3534678.3539267

  • Galhotra S, Firmani D, Saha B and Srivastava D. Hierarchical Entity Resolution using an Oracle. Proceedings of the 2022 International Conference on Management of Data. (414-428).

    https://doi.org/10.1145/3514221.3526147

  • Christophides V, Efthymiou V, Palpanas T, Papadakis G and Stefanidis K. (2020). An Overview of End-to-End Entity Resolution for Big Data. ACM Computing Surveys. 53:6. (1-42). Online publication date: 30-Nov-2021.

    https://doi.org/10.1145/3418896

  • Barlaug N and Gulla J. (2021). Neural Networks for Entity Matching: A Survey. ACM Transactions on Knowledge Discovery from Data. 15:3. (1-37). Online publication date: 30-Jun-2021.

    https://doi.org/10.1145/3442200

  • Chen R, Shen Y and Zhang D. GNEM: A Generic One-to-Set Neural Entity Matching Framework. Proceedings of the Web Conference 2021. (1686-1694).

    https://doi.org/10.1145/3442381.3450119

  • Zhu X, Huang X, Choi B, Jiang J, Zou Z and Xu J. (2021). Budget constrained interactive search for multiple targets. Proceedings of the VLDB Endowment. 14:6. (890-902). Online publication date: 1-Feb-2021.

    https://doi.org/10.14778/3447689.3447694

  • Yang J, Fan J, Wei Z, Li G, Liu T and Du X. (2020). A game-based framework for crowdsourced data labeling. The VLDB Journal — The International Journal on Very Large Data Bases. 29:6. (1311-1336). Online publication date: 1-Nov-2020.

    https://doi.org/10.1007/s00778-020-00613-w

  • Jiang N, Zhuang Y and Chiu D. (2020). Effective and efficient crowd-assisted similarity retrieval of medical images in resource-constraint Mobile telemedicine systems. Multimedia Tools and Applications. 79:27-28. (19893-19923). Online publication date: 1-Jul-2020.

    https://doi.org/10.1007/s11042-020-08755-3

  • Chen Z, Chen Q, Hou B, Li Z and Li G. Towards Interpretable and Learnable Risk Analysis for Entity Resolution. Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. (1165-1180).

    https://doi.org/10.1145/3318464.3380572

  • Li Y, Wu X, Jin Y, Li J and Li G. (2020). Efficient algorithms for crowd-aided categorization. Proceedings of the VLDB Endowment. 13:8. (1221-1233). Online publication date: 1-Apr-2020.

    https://doi.org/10.14778/3389133.3389139

  • Draisbach U, Christen P and Naumann F. (2019). Transforming Pairwise Duplicates to Entity Clusters for High-quality Duplicate Detection. Journal of Data and Information Quality. 12:1. (1-30). Online publication date: 31-Mar-2020.

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  • Salam M, Koone M, Thirumuruganathan S, Das G and Basu Roy S. A Human-in-the-loop Attribute Design Framework for Classification. The World Wide Web Conference. (1612-1622).

    https://doi.org/10.1145/3308558.3313547

  • Wang X and Meliou A. (2019). Explain3D. Proceedings of the VLDB Endowment. 12:7. (779-792). Online publication date: 1-Mar-2019.

    https://doi.org/10.14778/3317315.3317320

  • Dolatshah M, Teoh M, Wang J and Pei J. (2018). Cleaning crowdsourced labels using oracles for statistical classification. Proceedings of the VLDB Endowment. 12:4. (376-389). Online publication date: 1-Dec-2018.

    https://doi.org/10.14778/3297753.3297758

  • Chai C, Li G, Li J, Deng D and Feng J. (2018). A partial-order-based framework for cost-effective crowdsourced entity resolution. The VLDB Journal — The International Journal on Very Large Data Bases. 27:6. (745-770). Online publication date: 1-Dec-2018.

    https://doi.org/10.1007/s00778-018-0509-6

  • Yang J, Fan J, Wei Z, Li G, Liu T and Du X. (2018). Cost-effective data annotation using game-based crowdsourcing. Proceedings of the VLDB Endowment. 12:1. (57-70). Online publication date: 1-Sep-2018.

    https://doi.org/10.14778/3275536.3275541

  • Ke X, Teo M, Khan A and Yalavarthi V. (2018). A demonstration of PERC. Proceedings of the VLDB Endowment. 11:12. (1922-1925). Online publication date: 1-Aug-2018.

    https://doi.org/10.14778/3229863.3236225

  • Brackenbury W, Liu R, Mondal M, Elmore A, Ur B, Chard K and Franklin M. Draining the Data Swamp. Proceedings of the Workshop on Human-In-the-Loop Data Analytics. (1-7).

    https://doi.org/10.1145/3209900.3209911

  • Assadi A, Milo T and Novgorodov S. Cleaning Data with Constraints and Experts. Proceedings of the 21st International Workshop on the Web and Databases. (1-6).

    https://doi.org/10.1145/3201463.3201464

  • Galhotra S, Firmani D, Saha B and Srivastava D. Robust Entity Resolution using Random Graphs. Proceedings of the 2018 International Conference on Management of Data. (3-18).

    https://doi.org/10.1145/3183713.3183755

  • Altowim Y, Kalashnikov D and Mehrotra S. (2018). ProgressER. ACM Transactions on Knowledge Discovery from Data. 12:3. (1-45). Online publication date: 27-Apr-2018.

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  • Qian K, Popa L and Sen P. Active Learning for Large-Scale Entity Resolution. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. (1379-1388).

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  • Zhuang Y, Li G, Zhong Z and Feng J. Hike. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. (1917-1926).

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  • Yalavarthi V, Ke X and Khan A. Select Your Questions Wisely. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. (317-326).

    https://doi.org/10.1145/3132847.3132876

  • Zhao Y, Liu G, Zheng K, Liu A, Li Z and Zhou X. (2017). A context-aware approach for trustworthy worker selection in social crowd. World Wide Web. 20:6. (1211-1235). Online publication date: 1-Nov-2017.

    https://doi.org/10.1007/s11280-016-0429-6

  • Zhuang Y, Li G and Feng J. Crowdsourced Entity Alignment: A Decision Theory Based Approach. Web Information Systems Engineering – WISE 2017. (19-36).

    https://doi.org/10.1007/978-3-319-68786-5_2

  • Crescenzi V, Fernandes A, Merialdo P and Paton N. (2017). Crowdsourcing for data management. Knowledge and Information Systems. 53:1. (1-41). Online publication date: 1-Oct-2017.

    https://doi.org/10.1007/s10115-017-1057-x

  • Saberi M, Hussain O and Chang E. An online statistical quality control framework for performance management in crowdsourcing. Proceedings of the International Conference on Web Intelligence. (476-482).

    https://doi.org/10.1145/3106426.3106436

  • Li G. (2017). Human-in-the-loop data integration. Proceedings of the VLDB Endowment. 10:12. (2006-2017). Online publication date: 1-Aug-2017.

    https://doi.org/10.14778/3137765.3137833

  • Doan A, Ardalan A, Ballard J, Das S, Govind Y, Konda P, Li H, Mudgal S, Paulson E, Suganthan G and Zhang H. Human-in-the-Loop Challenges for Entity Matching. Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. (1-6).

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  • Li G, Chai C, Fan J, Weng X, Li J, Zheng Y, Li Y, Yu X, Zhang X and Yuan H. CDB. Proceedings of the 2017 ACM International Conference on Management of Data. (1463-1478).

    https://doi.org/10.1145/3035918.3064036

  • Li G, Zheng Y, Fan J, Wang J and Cheng R. Crowdsourced Data Management. Proceedings of the 2017 ACM International Conference on Management of Data. (1711-1716).

    https://doi.org/10.1145/3035918.3054776

  • Das S, G.C. P, Doan A, Naughton J, Krishnan G, Deep R, Arcaute E, Raghavendra V and Park Y. Falcon. Proceedings of the 2017 ACM International Conference on Management of Data. (1431-1446).

    https://doi.org/10.1145/3035918.3035960

  • Pradhan R, Bykau S and Prabhakar S. Staging User Feedback toward Rapid Conflict Resolution in Data Fusion. Proceedings of the 2017 ACM International Conference on Management of Data. (603-618).

    https://doi.org/10.1145/3035918.3035941

  • Verroios V, Garcia-Molina H and Papakonstantinou Y. Waldo. Proceedings of the 2017 ACM International Conference on Management of Data. (1133-1148).

    https://doi.org/10.1145/3035918.3035931

  • Mazumdar A and Saha B. A theoretical analysis of first heuristics of crowdsourced entity resolution. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. (970-976).

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  • Zheng Y, Li G, Li Y, Shan C and Cheng R. (2017). Truth inference in crowdsourcing. Proceedings of the VLDB Endowment. 10:5. (541-552). Online publication date: 1-Jan-2017.

    https://doi.org/10.14778/3055540.3055547

  • Zheng Y, Li G and Cheng R. (2016). DOCS. Proceedings of the VLDB Endowment. 10:4. (361-372). Online publication date: 1-Nov-2016.

    https://doi.org/10.14778/3025111.3025118

  • Khan A and Garcia-Molina H. Attribute-based Crowd Entity Resolution. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. (549-558).

    https://doi.org/10.1145/2983323.2983831

  • Maskat R, Paton N and Embury S. Pay-as-you-go Configuration of Entity Resolution. Transactions on Large-Scale Data- and Knowledge-Centered Systems XXIX - Volume 10120. (40-65).

    https://doi.org/10.1007/978-3-662-54037-4_2

  • Ríos J, Paton N, Fernandes A and Belhajjame K. Efficient Feedback Collection for Pay-as-you-go Source Selection. Proceedings of the 28th International Conference on Scientific and Statistical Database Management. (1-12).

    https://doi.org/10.1145/2949689.2949690

  • Chai C, Li G, Li J, Deng D and Feng J. Cost-Effective Crowdsourced Entity Resolution. Proceedings of the 2016 International Conference on Management of Data. (969-984).

    https://doi.org/10.1145/2882903.2915252

  • Chu X, Ilyas I, Krishnan S and Wang J. Data Cleaning. Proceedings of the 2016 International Conference on Management of Data. (2201-2206).

    https://doi.org/10.1145/2882903.2912574

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    https://doi.org/10.1145/2872427.2883030

  • Paton N, Belhajjame K, Embury S, Fernandes A and Maskat R. Pay-as-you-go Data Integration. Proceedings of the 42nd International Conference on SOFSEM 2016: Theory and Practice of Computer Science - Volume 9587. (81-92).

    https://doi.org/10.1007/978-3-662-49192-8_7

  • Firmani D, Saha B and Srivastava D. (2016). Online entity resolution using an Oracle. Proceedings of the VLDB Endowment. 9:5. (384-395). Online publication date: 1-Jan-2016.

    https://doi.org/10.14778/2876473.2876474

  • Marcus A and Parameswaran A. (2015). Crowdsourced Data Management. Foundations and Trends in Databases. 6:1-2. (1-161). Online publication date: 1-Dec-2015.

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  • (2015). C3D+P. Web Semantics: Science, Services and Agents on the World Wide Web. 35:P4. (203-213). Online publication date: 1-Dec-2015.

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    https://doi.org/10.1016/j.comnet.2015.05.018

  • Zhang C, Meng R, Chen L and Zhu F. CrowdLink. Proceedings of the Second International Workshop on Exploratory Search in Databases and the Web. (15-20).

    https://doi.org/10.1145/2795218.2795222

  • Zheng Y, Wang J, Li G, Cheng R and Feng J. QASCA. Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. (1031-1046).

    https://doi.org/10.1145/2723372.2749430

  • El-Roby A and Aboulnaga A. ALEX. Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. (1839-1853).

    https://doi.org/10.1145/2723372.2749428

  • Bergman M, Milo T, Novgorodov S and Tan W. Query-Oriented Data Cleaning with Oracles. Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. (1199-1214).

    https://doi.org/10.1145/2723372.2737786

  • Wang S, Xiao X and Lee C. Crowd-Based Deduplication. Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. (1263-1277).

    https://doi.org/10.1145/2723372.2723739

  • Wu T, Chen L, Hui P, Zhang C and Li W. (2015). Hear the whole story. Proceedings of the VLDB Endowment. 8:5. (485-496). Online publication date: 1-Jan-2015.

    https://doi.org/10.14778/2735479.2735482

  • Davidson S, Khanna S, Milo T and Roy S. (2014). Top-k and Clustering with Noisy Comparisons. ACM Transactions on Database Systems. 39:4. (1-39). Online publication date: 30-Dec-2015.

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  • Guo L, Sun H and Liu X. Using clustering and transitivity to reduce the costs of crowdsourced entity resolution. Proceedings of the 1st International Workshop on Crowd-based Software Development Methods and Technologies. (13-18).

    https://doi.org/10.1145/2666539.2666568

  • Zhang C, Tong Y and Chen L. (2014). Where to. Proceedings of the VLDB Endowment. 7:14. (2005-2016). Online publication date: 1-Oct-2014.

    https://doi.org/10.14778/2733085.2733105

  • Vesdapunt N, Bellare K and Dalvi N. (2014). Crowdsourcing algorithms for entity resolution. Proceedings of the VLDB Endowment. 7:12. (1071-1082). Online publication date: 1-Aug-2014.

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  • Gokhale C, Das S, Doan A, Naughton J, Rampalli N, Shavlik J and Zhu X. Corleone. Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. (601-612).

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