Publications

  1. 2021

    1. Gazzarri, L., & Herschel, M. (2021). End-to-end Task Based Parallelization for Entity Resolution on Dynamic Data. Proceedings of the IEEE International Conference on Data Engineering (ICDE).
    2. Diestelkämper, R., Lee, S., Herschel, M., & Glavic, B. (2021). To not miss the forest for the trees - A holistic approach for explaining missing answers over nested data. In Proceedins of the ACM SIG Conference on the Management of Data (SIGMOD).
    3. Zheng, K., Chen, G., Herschel, M., Ngiam, K. Y., Ooi, B. C., & Gao, J. (2021). PACE: Learning Effective Task Decomposition for Human-in-the-loop Healthcare Delivery. In Proceedings of the ACM SIG Conference on the Management of Data (SIGMOD).
    4. Lässig, N., Oppold, S., & Herschel, M. (2021). Using FALCES against bias in automated decisions by integrating fairness in dynamic model ensembles (to appear). In Proceedings of Database Systems for Business, Technology, and Web (BTW).
    5. Ben Lahmar, H., & Herschel, M. (2021). Collaborative filtering over evolution provenance data for interactive visual data exploration. Information Systems, 95, 101620. https://doi.org/10.1016/j.is.2020.101620
    6. Ellwein, C., Reichle, A., Herschel, M., & Verl, A. (2021). Integrative data processing for cyber-physical off-site and on-site construction promoting co-design. Procedia CIRP, 100, 451–456. https://doi.org/10.1016/j.procir.2021.05.103
  2. 2020

    1. Oppold, S., & Herschel, M. (2020). A System Framework for Personalized and Transparent Data-Driven Decisions. Proceedings of the International Conference on Advanced Information Systems Engineering (CAiSE), 153–168. https://doi.org/10.1007/978-3-030-49435-3_10
    2. Gazzarri, L., & Herschel, M. (2020). Boosting Blocking Performance in Entity Resolution Pipelines: Comparison Cleaning using Bloom Filters. Proceedings of the International Conference on Extending Database Technology (EDBT), 419--422. https://doi.org/10.5441/002/edbt.2020.47
    3. Diestelkämper, R., & Herschel, M. (2020). Tracing nested data with structural provenance for big data analytics. Proceedings of the International Conference on Extending Database Technology (EDBT), 253–264. https://doi.org/10.5441/002/edbt.2020.23
    4. Diestelkämper, R., & Herschel, M. (2020). Distributed Tree-Pattern Matching in Big Data Analytics Systems. In Proceedings of the Conference on Advances in Databases and Information Systems (ADBIS), 171–186. https://doi.org/10.1007/978-3-030-54832-2_14
    5. Oppold, S., & Herschel, M. (2020). Accountable Data Analytics Start with Accountable Data: The LiQuID Metadata Model. ER Forum, Demo and Posters 2020 Co-Located with International Conference on Conceptual Modeling (ER), 59--72. http://ceur-ws.org/Vol-2716/paper5.pdf
    6. Gazzarri, L., & Herschel, M. (2020). Towards task-based parallelization for entity resolution. SICS Software-Intensive Cyber-Physical Systems, 35(1), 31–38. https://doi.org/10.1007/s00450-019-00409-6
  3. 2019

    1. Ben Lahmar, H., & Herschel, M. (2019). Structural summaries for visual provenance analysis. Proceedings of the International Workshop on Theory and Practice of Provenance (TaPP). https://www.usenix.org/conference/tapp2019/presentation/lahmar
    2. Diestelkämper, R., Glavic, B., Herschel, M., & Lee, S. (2019). Query-based Why-not Explanations for Nested Data. Proceedings of the International Workshop on Theory and Practice of Provenance (TaPP). https://www.usenix.org/conference/tapp2019/presentation/diestelkamper
    3. Diestelkämper, R., & Herschel, M. (2019). Capturing and Querying Structural Provenance in Spark with Pebble. In ACM International Conference on Management of Data (SIGMOD), 1893–1896. https://doi.org/10.1145/3299869.3320225
    4. Oppold, S., & Herschel, M. (2019). LuPe: A System for Personalized and Transparent Data-driven Decisions. Proceedings of the International Conference on Information and Knowledge Management (CIKM), 2905–2908. https://doi.org/10.1145/3357384.3357857
    5. Bruder, V., Ben Lahmar, H., Hlawatsch, M., Frey, S., Burch, M., Weiskopf, D., Herschel, M., & Ertl, T. (2019). Volume-based large dynamic graph analysis supported by evolution provenance. Multimedia Tools and Applications, 78(23), 32939–32965. https://doi.org/10.1007/s11042-019-07878-6
    6. Ben Lahmar, H., & Herschel, M. (2019). Towards Integrating Collaborative Filtering in Visual Data Exploration Systems. New Trends in Databases and Information Systems, (ADBIS) 2019 Short Papers, Workshops BBIGAP, QAUCA, SemBDM, SIMPDA, M2P, MADEISD, and Doctoral Consortium, 153--160. https://doi.org/10.1007/978-3-030-30278-8\_19
  4. 2018

    1. Oppold, S., & Herschel, M. (2018). Provenance for Entity Resolution. In K. Belhajjame, A. Gehani, & P. Alper (Eds.), IPAW (Vol. 11017, pp. 226–230). Springer. http://dblp.uni-trier.de/db/conf/ipaw/ipaw2018.html#OppoldH18
    2. Schulz, C., Zeyfang, A., van Garderen, M., Ben Lahmar, H., Herschel, M., & Weiskopf, D. (2018). Simultaneous Visual Analysis of Multiple Software Hierarchies. Proceedings of the IEEE Working Conference on Software Visualization (VISSOFT), 87–95. https://doi.org/10.1109/VISSOFT.2018.00017
    3. Ben Lahmar, H., Herschel, M., Blumenschein, M., & Keim, D. A. (2018). Provenance-Based Visual Data Exploration with EVLIN. Proceedings of International Conference on Extending Database Technology (EDBT), 686--689. https://doi.org/10.5441/002/edbt.2018.85
  5. 2017

    1. Ben Lahmar, H., & Herschel, M. (2017). Provenance-based Recommendations for Visual Data Exploration. International Workshop on the Theory and Practice of Provenance (TAPP). https://www.usenix.org/conference/tapp17/workshop-program/presentation/lahmar
    2. Herschel, M., Diestelkämper, R., & Ben Lahmar, H. (2017). A survey on provenance: What for? What form? What from? The VLDB Journal, 26(6), 881–906. https://doi.org/10.1007/s00778-017-0486-1
    3. Diestelkämper, R., Herschel, M., & Jadhav, P. (2017). Provenance in DISC Systems: Reducing Space Overhead at Runtime. Proceedings of the USENIX Conference on Theory and Practice of Provenance (TAPP), 1–13. https://dl.acm.org/doi/abs/10.5555/3183865.3183883
  6. 2016

    1. Camacho-Rodríguez, J., Colazzo, D., Herschel, M., Manolescu, I., & Chowdhury, S. R. (2016). Reuse-based Optimization for Pig Latin. Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM), 2215--2220. https://doi.org/10.1145/2983323.2983669
    2. Bidoit, N., Herschel, M., & Tzompanaki, K. (2016). Refining SQL Queries based on Why-Not Polynomials. 8th USENIX Workshop on the Theory and Practice of Provenance, TaPP 2016, Washington, D.C., USA, June 8-9, 2016. https://www.usenix.org/conference/tapp16/workshop-program/presentation/bidoit
    3. Herschel, M., & Hlawatsch, M. (2016). Provenance: On and Behind the Screens. Proceedings of the ACM International Conference on the Management of Data (SIGMOD), 2213–2217. https://doi.org/10.1145/2882903.2912568
  7. 2015

    1. Bidoit, N., Herschel, M., & Tzompanaki, K. (2015). Immutably answering Why-Not questions for equivalent conjunctive queries. Ingénierie Des Systèmes d’information, 20(5), 27–52. https://doi.org/10.3166/isi.20.5.27-52
    2. Saveta, T., Daskalaki, E., Flouris, G., Fundulaki, I., Herschel, M., & Ngomo, A.-C. N. (2015). LANCE: Piercing to the Heart of Instance Matching Tools. International Semantic Web Conference (ISWC), 375–391. https://doi.org/10.1007/978-3-319-25007-6\_22
    3. Bidoit, N., Herschel, M., & Tzompanaki, K. (2015). EFQ: why-not answer polynomials in action. Proceedings of the VLDB Endowment (PVLDB), 8(12), 1980–1983. http://dblp.uni-trier.de/db/journals/pvldb/pvldb8.html#BidoitHT15
    4. Herschel, M. (2015). A hybrid approach to answering why-not questions on relational query results. Journal of Data and Information Quality, 5(3), 10:1-10:29. http://dblp.uni-trier.de/db/journals/jdiq/jdiq5.html#Herschel15
    5. Saveta, T., Daskalaki, E., Flouris, G., Fundulaki, I., Herschel, M., & Ngomo, A.-C. N. (2015). Pushing the Limits of Instance Matching Systems: A Semantics-Aware Benchmark for Linked Data. Proceedings of the International Conference on World Wide Web (WW), Companion Volume, 105–106. https://doi.org/10.1145/2740908.2742729
    6. Bidoit, N., Herschel, M., & Tzompanaki, A. (2015). Efficient computation of polynomial explanations of why-not questions. In J. Bailey, A. Moffat, C. C. Aggarwal, M. de Rijke, R. Kumar, V. Murdock, T. K. Sellis, & J. X. Yu (Eds.), CIKM’15 (pp. 713–722). Association for Computing Machinery. https://doi.org/10.1145/2806416.2806426
To the top of the page