Ludwig Zellner

Research Interests:

• Process Mining
• Sequential Rule Mining
• Recommender Systems
• Anomaly Detection
• Concept Drift Detection

Teaching

• Softwareentwicklungspraktikum (SoSe 2023)
• Softwareentwicklungspraktikum (WiSe 2022/2023)
• Oberseminar (SoSe 2022)
• Softwareentwicklungspraktikum (SoSe 2022)
• Softwareentwicklungspraktikum (WiSe 2021/2022)
• Oberseminar (SoSe 2021)
• Softwareentwicklungspraktikum (SoSe 2021)
• Softwareentwicklungspraktikum (WiSe 2020/2021)
• Master Seminar "Recent Developments in Process Mining" (SoSe 2020)
• Algorithmen und Datenstrukturen (SoSe 2020)
• Einführung in die Programmierung (WiSe 2019/2020)
• Softwareentwicklungspraktikum (SoSe 2019)

Supervised Theses:

• Examining Filter Bubbles over Time: A Sequential Rule Mining Approach
• Comparing Recommender Systems on a Successive-Item-Recommendation Task
• Dynamic Ranking of Sequential Rules Based on Discrete Gaps
• Dynamic Ranking of Sequential Rules Based on Continuous Gaps
• Analysing Parameter Impact and Quality Measures for Designated Rule Mining Algorithms
• On the Optimization of Partially-Ordered Sequential Rule Mining
• Utilising Sequential Rule Mining for Conformance Checking
• Comparing Types of Sequential Rule Mining for Recommendation Purposes
• Using Graph Similarity For Concept Drift Detection
• Discovering Frequent Subtraces with Gaps on Unstructured Processes
• k-Lines Clustering: Extending k-Means to Linearly Correlated Data
• Coalition-Based Ranking on Process Graphs

Possible Phases of a Thesis:

Publications:

• Maldonado, A.*, Zellner, L.*, Strickroth, S., & Seidl, T. (2023, October). Process Mining Techniques for Collusion Detection in Online Exams. In 2023 International Conference on Process Mining (EduPM@ICPM). (*Equal contribution)
• Zellner, L., Sontheim, J., Richter, F., Lindner, G., & Seidl, T. (2021, December). SCORER-Gap: Sequentially Correlated Rules for Event Recommendation Considering Gap Size. In 2021 International Conference on Data Mining Workshops (ICDMW) (pp. 925-934). IEEE.
• Zellner, L., Richter, F., Sontheim, J., Maldonado, A., & Seidl, T. (2020, October). Concept drift detection on streaming data with dynamic outlier aggregation. In International Conference on Process Mining (ICPMW) (pp. 206-217). Springer, Cham.
• Richter, F., Maldonado, A., Zellner, L., & Seidl, T. (2020, October). OTOSO: Online trace ordering for structural overviews. In International Conference on Process Mining (ICPMW) (pp. 218-229). Springer, Cham.
• Richter, F., Lu, Y., Zellner, L., Sontheim, J., & Seidl, T. (2020, October). TOAD: Trace Ordering for Anomaly Detection. In 2020 2nd International Conference on Process Mining (ICPM) (pp. 169-176). IEEE.
• Richter, F., Sontheim, J., Zellner, L., & Seidl, T. (2020, September). TADE: Stochastic Conformance Checking Using Temporal Activity Density Estimation. In International Conference on Business Process Management (pp. 220-236). Springer, Cham.
• Richter, F., Zellner, L., Sontheim, J., & Seidl, T. (2019, October). Model-Aware Clustering of Non-conforming Traces. In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems" (pp. 193-200). Springer, Cham.
• Richter, F., Zellner, L., Azaiz, I., Winkel, D., & Seidl, T. (2019, September). LIProMa: label-independent process matching. In International Conference on Business Process Management (pp. 186-198). Springer, Cham.
• Jossé, Gregor, Matthias Schubert, and Ludwig Zellner. "Easyev: Monitoring and querying system for electric vehicle fleets using smart car data." International Symposium on Spatial and Temporal Databases. Springer, Cham, 2015.

Academic Service:

Reviewing for DSAA (DSAA'19), 2 x DAMI (DAMI'20), EDBT (EDBT'23), BTW (BTW'23), 2 x Data & Knowledge Engineering Journal (DATAK'23), ECAI (ECAI'23)