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Florian Richter
Florian Richter
Verified email at dbs.ifi.lmu.de - Homepage
Title
Cited by
Cited by
Year
Efficient process discovery from event streams using sequential pattern mining
M Hassani, S Siccha, F Richter, T Seidl
2015 IEEE symposium series on computational intelligence, 1366-1373, 2015
552015
TESSERACT: time-drifts in event streams using series of evolving rolling averages of completion times
F Richter, T Seidl
International Conference on Business Process Management, 289-305, 2017
122017
Efficient infrequent itemset mining using depth-first and top-down lattice traversal
Y Lu, F Richter, T Seidl
International Conference on Database Systems for Advanced Applications, 908-915, 2018
62018
Looking into the TESSERACT: Time-drifts in event streams using series of evolving rolling averages of completion times
F Richter, T Seidl
Information Systems 84, 265-282, 2019
52019
Efficient infrequent pattern mining using negative itemset tree
Y Lu, F Richter, T Seidl
Complex Pattern Mining, 1-16, 2020
42020
LIProMa: label-independent process matching
F Richter, L Zellner, I Azaiz, D Winkel, T Seidl
International Conference on Business Process Management, 186-198, 2019
42019
OTOSO: online trace ordering for structural overviews
F Richter, A Maldonado, L Zellner, T Seidl
International Conference on Process Mining, 218-229, 2020
32020
Concept drift detection on streaming data with dynamic outlier aggregation
L Zellner, F Richter, J Sontheim, A Maldonado, T Seidl
International Conference on Process Mining, 206-217, 2020
32020
Model-aware clustering of non-conforming traces
F Richter, L Zellner, J Sontheim, T Seidl
OTM Confederated International Conferences" On the Move to Meaningful …, 2019
32019
Interdisciplinary knowledge cohesion through distributed information management systems
D Kaltenthaler, JY Lohrer, F Richter, P Kröger
Journal of Information, Communication and Ethics in Society, 2018
32018
“Show Me the Crowds!” Revealing Cluster Structures Through AMTICS
F Richter, Y Lu, D Kazempour, T Seidl
Data Science and Engineering 5 (4), 360-374, 2020
22020
k-Nearest Neighbor based Clustering with Shape Alternation Adaptivity
Y Lu, Y Zhang, F Richter, T Seidl
2020 International Joint Conference on Neural Networks (IJCNN), 1-8, 2020
22020
PErrCas: Process Error Cascade Mining in Trace Streams
A Wimbauer, F Richter, T Seidl
International Conference on Process Mining, 224-236, 2022
12022
TOAD: trace ordering for anomaly detection
F Richter, Y Lu, L Zellner, J Sontheim, T Seidl
2020 2nd International Conference on Process Mining (ICPM), 169-176, 2020
12020
TADE: Stochastic conformance checking using temporal activity density estimation
F Richter, J Sontheim, L Zellner, T Seidl
International Conference on Business Process Management, 220-236, 2020
12020
Lscminer: Efficient low support closed itemsets mining
Y Lu, F Richter, T Seidl
International Conference on Web Information Systems Engineering, 293-309, 2020
12020
Temporal Deviations on Event Sequences.
J Sontheim, F Richter, T Seidl
LWDA, 173-177, 2019
12019
k-process: Model-Conformance-based Clustering of Process Instances.
F Richter, F Wahl, A Sydorova, T Seidl
LWDA, 161-172, 2019
12019
Retrieval of heterogeneous data from dynamic and anonymous sources
JY Lohrer, D Kaltenthaler, F Richter, T Sizova, P Kröger, ...
2018 8th International Conference on Cloud Computing, Data Science …, 2018
12018
The data science lab at LMU Munich: leveraging knowledge transfer, implementing collaborative projects, and promoting future data science talents
T Seidl, P Kröger, T Emrich, M Schubert, G Jossé, F Richter
Digital Marketplaces Unleashed, 549-556, 2018
12018
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Articles 1–20