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ORCIDJochen De Weerdt
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- affiliation: KU Leuven, Belgium
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2020 – today
- 2024
[j48]Margot Geerts
, Seppe vanden Broucke, Jochen De Weerdt:
GeoRF: a geospatial random forest. Data Min. Knowl. Discov. 38(6): 3414-3448 (2024)- [j47]Johannes De Smedt, Jochen De Weerdt:
Predictive process model monitoring using long short-term memory networks. Eng. Appl. Artif. Intell. 133: 108295 (2024) - [j46]Philipp Borchert, Kristof Coussement, Jochen De Weerdt, Arno De Caigny:
Industry-sensitive language modeling for business. Eur. J. Oper. Res. 315(2): 691-702 (2024) - [j45]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Validation set sampling strategies for predictive process monitoring. Inf. Syst. 121: 102330 (2024) - [j44]Björn Rafn Gunnarsson, Seppe vanden Broucke, Jochen De Weerdt:
LS-ICE: A Load State Intercase Encoding framework for improved predictive monitoring of business processes. Inf. Syst. 125: 102432 (2024) - [j43]Carlos Ortega Vázquez, Seppe vanden Broucke, Jochen De Weerdt:
Hellinger distance decision trees for PU learning in imbalanced data sets. Mach. Learn. 113(7): 4547-4578 (2024) - [j42]Rafaël Van Belle, Jochen De Weerdt:
SHINE: A Scalable Heterogeneous Inductive Graph Neural Network for Large Imbalanced Datasets. IEEE Trans. Knowl. Data Eng. 36(9): 4904-4915 (2024) - [c69]Zahra Ahmadi, Jochen De Weerdt, Estefanía Serral Asensio:
A Novel Contextualization Method for Process Discovery Using Activity Specialization Hierarchies. BPMDS/EMMSAD@CAiSE 2024: 143-155 - [c68]Brecht Wuyts, Seppe K. L. M. vanden Broucke, Jochen De Weerdt:
SuTraN: an Encoder-Decoder Transformer for Full-Context-Aware Suffix Prediction of Business Processes. ICPM 2024: 17-24 - [c67]Philipp Borchert, Jochen De Weerdt, Marie-Francine Moens:
Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning. NAACL (Short Papers) 2024: 638-646 - [c66]Margot Geerts, Seppe vanden Broucke, Jochen De Weerdt:
A Spatial Loss Function for Gradient Boosted Trees. STRL@IJCAI 2024 - [e5]Jochen De Weerdt, Luise Pufahl:
Business Process Management Workshops - BPM 2023 International Workshops, Utrecht, The Netherlands, September 11-15, 2023, Revised Selected Papers. Lecture Notes in Business Information Processing 492, Springer 2024, ISBN 978-3-031-50973-5 [contents] - [e4]Jochen De Weerdt, Giovanni Meroni, Han van der Aa, Karolin Winter:
Doctoral Consortium and Demo Track 2024 at the International Conference on Process Mining 2024 co-located with the 6th International Conference on Process Mining (ICPM 2024), Copenhagen, Denmark, October 15, 2024. CEUR Workshop Proceedings 3783, CEUR-WS.org 2024 [contents] - [i18]Philipp Borchert, Jochen De Weerdt, Marie-Francine Moens:
Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning. CoRR abs/2403.16543 (2024) - [i17]Manon Reusens, Philipp Borchert, Jochen De Weerdt, Bart Baesens:
Native Design Bias: Studying the Impact of English Nativeness on Language Model Performance. CoRR abs/2406.17385 (2024) - [i16]Lien Bosmans, Jari Peeperkorn, Alexandre Goossens, Giovanni Lugaresi, Johannes De Smedt, Jochen De Weerdt:
Dynamic and Scalable Data Preparation for Object-Centric Process Mining. CoRR abs/2410.00596 (2024) - 2023
- [j41]Carlos Ortega Vázquez, Seppe vanden Broucke, Jochen De Weerdt:
A two-step anomaly detection based method for PU classification in imbalanced data sets. Data Min. Knowl. Discov. 37(3): 1301-1325 (2023) - [j40]Johannes De Smedt, Anton Yeshchenko, Artem Polyvyanyy, Jochen De Weerdt, Jan Mendling:
Process model forecasting and change exploration using time series analysis of event sequence data. Data Knowl. Eng. 145: 102145 (2023) - [j39]Rafaël Van Belle, Bart Baesens, Jochen De Weerdt:
CATCHM: A novel network-based credit card fraud detection method using node representation learning. Decis. Support Syst. 164: 113866 (2023) - [j38]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Global conformance checking measures using shallow representation and deep learning. Eng. Appl. Artif. Intell. 123(Part): 106393 (2023) - [j37]Philipp Borchert, Kristof Coussement, Arno De Caigny, Jochen De Weerdt:
Extending business failure prediction models with textual website content using deep learning. Eur. J. Oper. Res. 306(1): 348-357 (2023) - [j36]Margot Geerts, Seppe vanden Broucke, Jochen De Weerdt:
A Survey of Methods and Input Data Types for House Price Prediction. ISPRS Int. J. Geo Inf. 12(5): 200 (2023) - [j35]Arthur H. M. ter Hofstede, Agnes Koschmider, Andrea Marrella, Robert Andrews, Dominik Andreas Fischer, Sareh Sadeghianasl, Moe Thandar Wynn, Marco Comuzzi, Jochen De Weerdt, Kanika Goel, Niels Martin, Pnina Soffer:
Process-Data Quality: The True Frontier of Process Mining. ACM J. Data Inf. Qual. 15(3): 29:1-29:21 (2023) - [j34]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Can recurrent neural networks learn process model structure? J. Intell. Inf. Syst. 61(1): 27-51 (2023) - [j33]Monique Snoeck, Charlotte Verbruggen, Johannes De Smedt, Jochen De Weerdt:
Supporting data-aware processes with MERODE. Softw. Syst. Model. 22(6): 1779-1802 (2023) - [j32]Björn Rafn Gunnarsson, Seppe vanden Broucke, Jochen De Weerdt:
A Direct Data Aware LSTM Neural Network Architecture for Complete Remaining Trace and Runtime Prediction. IEEE Trans. Serv. Comput. 16(4): 2330-2342 (2023) - [c65]Brecht Wuyts, Hans Weytjens, Seppe vanden Broucke, Jochen De Weerdt:
DyLoPro: Profiling the Dynamics of Event Logs. BPM 2023: 146-162 - [c64]Hans Weytjens, Wouter Verbeke, Jochen De Weerdt:
Timed Process Interventions: Causal Inference vs. Reinforcement Learning. Business Process Management Workshops 2023: 245-258 - [c63]Yannis Bertrand, Jochen De Weerdt, Estefanía Serral:
A Novel Multi-perspective Trace Clustering Technique for IoT-Enhanced Processes: A Case Study in Smart Manufacturing. BPM 2023: 395-412 - [c62]Manon Reusens, Philipp Borchert, Margot Mieskes, Jochen De Weerdt, Bart Baesens:
Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques. EMNLP 2023: 2887-2896 - [c61]Philipp Borchert, Jochen De Weerdt, Kristof Coussement, Arno De Caigny, Marie-Francine Moens:
CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation. EMNLP 2023: 11792-11806 - [c60]Jan Niklas Adams, Jari Peeperkorn, Tobias Brockhoff, Isabelle Terrier, Heiko Göhner, Merih Seran Uysal, Seppe vanden Broucke, Jochen De Weerdt, Wil M. P. van der Aalst:
Discovering high-quality process models despite data scarcity. ER (Companion) 2023 - [c59]Thais Rodrigues Neubauer, Jari Peeperkorn, Sarajane Marques Peres, Jochen De Weerdt, Marcelo Fantinato:
Vector Representation for Business Process: Graph Embedding for Domain Knowledge Integration. ICMLA 2023: 588-594 - [c58]Alexander Stevens, Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt:
Manifold Learning for Adversarial Robustness in Predictive Process Monitoring. ICPM 2023: 17-24 - [c57]Margot Geerts, Seppe vanden Broucke, Jochen De Weerdt:
An Evolutionary Geospatial Regression Tree. STRL@IJCAI 2023 - [i15]Hans Weytjens, Wouter Verbeke, Jochen De Weerdt:
Timing Process Interventions with Causal Inference and Reinforcement Learning. CoRR abs/2306.04299 (2023) - [i14]Manon Reusens, Philipp Borchert, Margot Mieskes, Jochen De Weerdt, Bart Baesens:
Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques. CoRR abs/2310.10310 (2023) - [i13]Jan Niklas Adams, Jari Peeperkorn, Tobias Brockhoff, Isabelle Terrier, Heiko Göhner, Merih Seran Uysal, Seppe vanden Broucke, Jochen De Weerdt, Wil M. P. van der Aalst:
Discovering High-Quality Process Models Despite Data Scarcity. CoRR abs/2310.11332 (2023) - [i12]Philipp Borchert, Jochen De Weerdt, Kristof Coussement, Arno De Caigny, Marie-Francine Moens:
CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation. CoRR abs/2310.12024 (2023) - 2022
- [j31]Hans Weytjens, Jochen De Weerdt:
Learning uncertainty with artificial neural networks for predictive process monitoring. Appl. Soft Comput. 125: 109134 (2022) - [j30]Rafaël Van Belle, Charles Van Damme, Hendrik Tytgat, Jochen De Weerdt:
Inductive Graph Representation Learning for fraud detection. Expert Syst. Appl. 193: 116463 (2022) - [j29]Galina Deeva, Johannes De Smedt, Cecilia Saint-Pierre, Richard Weber, Jochen De Weerdt:
Predicting student performance using sequence classification with time-based windows. Expert Syst. Appl. 209: 118182 (2022) - [j28]Galina Deeva, Johannes De Smedt, Jochen De Weerdt:
Educational Sequence Mining for Dropout Prediction in MOOCs: Model Building, Evaluation, and Benchmarking. IEEE Trans. Learn. Technol. 15(6): 720-735 (2022) - [c56]Jarne Vandenabeele, Gilles Vermaut, Jari Peeperkorn, Jochen De Weerdt:
Enhancing Stochastic Petri Net-based Remaining Time Prediction using k-Nearest Neighbors. ATAED@Petri Nets 2022: 9-24 - [c55]Simon Hiel, Lore Nicolaers, Carlos Ortega Vázquez, Sandra Mitrovic, Bart Baesens, Jochen De Weerdt:
Evaluation of Joint Modeling Techniques for Node Embedding and Community Detection on Graphs. ASONAM 2022: 403-410 - [c54]Yannis Bertrand, Jochen De Weerdt, Estefanía Serral:
Assessing the Suitability of Traditional Event Log Standards for IoT-Enhanced Event Logs. Business Process Management Workshops 2022: 63-75 - [c53]Alexander Stevens, Johannes De Smedt, Jari Peeperkorn, Jochen De Weerdt:
Assessing the Robustness in Predictive Process Monitoring through Adversarial Attacks. ICPM 2022: 56-63 - [c52]Jari Peeperkorn, Carlos Ortega Vázquez, Alexander Stevens, Johannes De Smedt, Seppe vanden Broucke, Jochen De Weerdt:
Outcome-Oriented Predictive Process Monitoring on Positive and Unlabelled Event Logs. ICPM Workshops 2022: 255-268 - [c51]Yannis Bertrand, Rafaël Van Belle, Jochen De Weerdt, Estefanía Serral:
Defining Data Quality Issues in Process Mining with IoT Data. ICPM Workshops 2022: 422-434 - [c50]Björn Rafn Gunnarsson, Jochen De Weerdt, Seppe vanden Broucke:
A framework for encoding the multi-location load state of a business process. PMAI@IJCAI 2022: 13-24 - [c49]Carlos Ortega Vázquez, Jochen De Weerdt, Seppe vanden Broucke:
The Hidden Cost of Fraud: An Instance-Dependent Cost-Sensitive Approach for Positive and Unlabeled Learning. LIDTA 2022: 53-67 - [c48]Margot Geerts, Kiran Shaikh, Jochen De Weerdt, Seppe vanden Broucke:
Predicting the State of a House Using Google Street View - An Analysis of Deep Binary Classification Models for the Assessment of the Quality of Flemish Houses. RCIS 2022: 703-710 - [p1]Jochen De Weerdt, Moe Thandar Wynn:
Foundations of Process Event Data. Process Mining Handbook 2022: 193-211 - [e3]Jochen De Weerdt, Artem Polyvyanyy:
Intelligent Information Systems - CAiSE Forum 2022, Leuven, Belgium, June 6-10, 2022, Proceedings. Lecture Notes in Business Information Processing 452, Springer 2022, ISBN 978-3-031-07480-6 [contents] - [i11]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Can deep neural networks learn process model structure? An assessment framework and analysis. CoRR abs/2202.11985 (2022) - [i10]Hans Weytjens, Jochen De Weerdt:
Learning Uncertainty with Artificial Neural Networks for Improved Predictive Process Monitoring. CoRR abs/2206.06317 (2022) - [i9]Jarne Vandenabeele, Gilles Vermaut, Jari Peeperkorn, Jochen De Weerdt:
Enhancing Stochastic Petri Net-based Remaining Time Prediction using k-Nearest Neighbors. CoRR abs/2206.13109 (2022) - [i8]Galina Deeva, Johannes De Smedt, Cecilia Saint-Pierre, Richard Weber, Jochen De Weerdt:
Predicting student performance using sequence classification with time-based windows. CoRR abs/2208.07749 (2022) - [i7]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Can recurrent neural networks learn process model structure? CoRR abs/2212.06430 (2022) - 2021
- [j27]Galina Deeva, Daria Bogdanova, Estefanía Serral, Monique Snoeck, Jochen De Weerdt:
A review of automated feedback systems for learners: Classification framework, challenges and opportunities. Comput. Educ. 162: 104094 (2021) - [j26]Andrea Burattin, Jochen De Weerdt, Boudewijn F. van Dongen, Jan Claes, Wil M. P. van der Aalst:
Special issue on business process intelligence. Computing 103(1): 1-2 (2021) - [j25]Mieke Jans, Jochen De Weerdt, Benoît Depaire, Marlon Dumas, Gert Janssenswillen:
Conformance Checking in Process Mining. Inf. Syst. 102: 101851 (2021) - [j24]Sandra Mitrovic, Bart Baesens, Wilfried Lemahieu, Jochen De Weerdt:
tcc2vec: RFM-informed representation learning on call graphs for churn prediction. Inf. Sci. 557: 270-285 (2021) - [j23]Pieter De Koninck, Klaas Nelissen, Seppe vanden Broucke, Bart Baesens, Monique Snoeck, Jochen De Weerdt:
Expert-driven trace clustering with instance-level constraints. Knowl. Inf. Syst. 63(5): 1197-1220 (2021) - [c47]Hans Weytjens, Jochen De Weerdt:
Creating Unbiased Public Benchmark Datasets with Data Leakage Prevention for Predictive Process Monitoring. Business Process Management Workshops 2021: 18-29 - [c46]Hans Weytjens, Jochen De Weerdt:
Learning Uncertainty with Artificial Neural Networks for Improved Remaining Time Prediction of Business Processes. BPM 2021: 141-157 - [c45]Monique Snoeck, Johannes De Smedt, Jochen De Weerdt:
Supporting Data-Aware Processes with MERODE. BPMDS/EMMSAD@CAiSE 2021: 131-146 - [c44]Johannes De Smedt, Anton Yeshchenko, Artem Polyvyanyy, Jochen De Weerdt, Jan Mendling:
Process Model Forecasting Using Time Series Analysis of Event Sequence Data. ER 2021: 47-61 - [c43]Joppe Geluykens, Sandra Mitrovic, Carlos Ortega Vázquez, Teodoro Laino, Alain C. Vaucher, Jochen De Weerdt:
Neural Machine Translation for Conditional Generation of Novel Procedures. HICSS 2021: 1-10 - [c42]Yannis Bertrand, Jochen De Weerdt, Estefanía Serral:
A Bridging Model for Process Mining and IoT. ICPM Workshops 2021: 98-110 - [c41]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Can Deep Neural Networks Learn Process Model Structure? An Assessment Framework and Analysis. ICPM Workshops 2021: 127-139 - [i6]Hans Weytjens, Jochen De Weerdt:
Process Outcome Prediction: CNN vs. LSTM (with Attention). CoRR abs/2104.06934 (2021) - [i5]Johannes De Smedt, Anton Yeshchenko, Artem Polyvyanyy, Jochen De Weerdt, Jan Mendling:
Process Model Forecasting Using Time Series Analysis of Event Sequence Data. CoRR abs/2105.01092 (2021) - [i4]Hans Weytjens, Jochen De Weerdt:
Learning Uncertainty with Artificial Neural Networks for Improved Remaining Time Prediction of Business Processes. CoRR abs/2105.05559 (2021) - [i3]Hans Weytjens, Jochen De Weerdt:
Creating Unbiased Public Benchmark Datasets with Data Leakage Prevention for Predictive Process Monitoring. CoRR abs/2107.01905 (2021) - [i2]Pieter De Koninck, Klaas Nelissen, Seppe vanden Broucke, Bart Baesens, Monique Snoeck, Jochen De Weerdt:
Expert-driven Trace Clustering with Instance-level Constraints. CoRR abs/2110.06703 (2021) - 2020
- [j22]Niels Martin, Jochen De Weerdt, Carlos Fernández-Llatas, Avigdor Gal, Roberto Gatta, Gema Ibáñez, Owen A. Johnson, Felix Mannhardt, Luis Marco-Ruiz, Steven Mertens, Jorge Munoz-Gama, Fernando Seoane, Jan Vanthienen, Moe Thandar Wynn, David Baltar Boilève, Jochen Bergs, Mieke Joosten-Melis, Stijn Schretlen, Bram B. Van Acker:
Recommendations for enhancing the usability and understandability of process mining in healthcare. Artif. Intell. Medicine 109: 101962 (2020) - [j21]Sandra Mitrovic, Jochen De Weerdt:
Churn modeling with probabilistic meta paths-based representation learning. Inf. Process. Manag. 57(2): 102052 (2020) - [j20]Johannes De Smedt, Galina Deeva, Jochen De Weerdt:
Mining Behavioral Sequence Constraints for Classification. IEEE Trans. Knowl. Data Eng. 32(6): 1130-1142 (2020) - [c40]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Conformance Checking Using Activity and Trace Embeddings. BPM (Forum) 2020: 105-121 - [c39]Hans Weytjens, Jochen De Weerdt:
Process Outcome Prediction: CNN vs. LSTM (with Attention). Business Process Management Workshops 2020: 321-333 - [c38]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Supervised Conformance Checking Using Recurrent Neural Network Classifiers. ICPM Workshops 2020: 175-187 - [c37]Steven Van Goidsenhoven, Daria Bogdanova, Galina Deeva, Seppe vanden Broucke, Jochen De Weerdt, Monique Snoeck:
Predicting student success in a blended learning environment. LAK 2020: 17-25 - [c36]Carlos Ortega Vázquez, Sandra Mitrovic, Jochen De Weerdt, Seppe vanden Broucke:
A Comparative Study of Representation Learning Techniques for Dynamic Networks. WorldCIST (3) 2020: 523-530 - [e2]Claudio Di Ciccio, Benoît Depaire, Jochen De Weerdt, Chiara Di Francescomarino, Jorge Munoz-Gama:
Proceedings of the ICPM Doctoral Consortium and Tool Demonstration Track 2020 co-located with the 2nd International Conference on Process Mining (ICPM 2020), Padua, Italy, October 4-9, 2020. CEUR Workshop Proceedings 2703, CEUR-WS.org 2020 [contents] - [i1]Johannes De Smedt, Jochen De Weerdt, Junichiro Mori, Masanao Ochi:
Predictive Process Model Monitoring using Recurrent Neural Networks. CoRR abs/2011.02819 (2020)
2010 – 2019
- 2019
- [c35]Sandra Mitrovic, Laurent Lecoutere, Jochen De Weerdt:
A comparison of methods for link sign prediction with signed network embeddings. ASONAM 2019: 1089-1096 - [c34]Björn Rafn Gunnarsson, Seppe K. L. M. vanden Broucke, Jochen De Weerdt:
Predictive Process Monitoring in Operational Logistics: A Case Study in Aviation. Business Process Management Workshops 2019: 250-262 - [c33]Galina Deeva, Johannes De Smedt, Jochen De Weerdt, María Óskarsdóttir:
Mining Behavioural Patterns in Urban Mobility Sequences Using Foursquare Check-in Data from Tokyo. COMPLEX NETWORKS (2) 2019: 931-943 - [c32]Björn Rafn Gunnarsson, Seppe vanden Broucke, Jochen De Weerdt:
Optimizing Marketing Campaign Targeting Using Uncertainty-Based Predictive Modelling. ICDM Workshops 2019: 326-332 - [c31]Pieter De Koninck, Jochen De Weerdt:
Scalable Mixed-Paradigm Trace Clustering using Super-Instances. ICPM 2019: 17-24 - [c30]Rafaël Van Belle, Sandra Mitrovic, Jochen De Weerdt:
Representation Learning in Graphs for Credit Card Fraud Detection. MIDAS@PKDD 2019: 32-46 - [c29]Lauranne Coppens, Jonathan De Venter, Sandra Mitrovic, Jochen De Weerdt:
A Comparative Study of Community Detection Techniques for Large Evolving Graphs. PKDD/ECML Workshops (1) 2019: 368-384 - [r1]Jochen De Weerdt:
Trace Clustering. Encyclopedia of Big Data Technologies 2019 - 2018
- [j19]Sandra Mitrovic, Bart Baesens, Wilfried Lemahieu, Jochen De Weerdt:
On the operational efficiency of different feature types for telco Churn prediction. Eur. J. Oper. Res. 267(3): 1141-1155 (2018) - [j18]Johannes De Smedt, Jochen De Weerdt, Estefanía Serral, Jan Vanthienen:
Discovering hidden dependencies in constraint-based declarative process models for improving understandability. Inf. Syst. 74(Part): 40-52 (2018) - [c28]Sam De Winter, Tim Decuypere, Sandra Mitrovic, Bart Baesens, Jochen De Weerdt:
Combining Temporal Aspects of Dynamic Networks with Node2Vec for a more Efficient Dynamic Link Prediction. ASONAM 2018: 1234-1241 - [c27]Galina Deeva, Jochen De Weerdt:
Understanding Automated Feedback in Learning Processes by Mining Local Patterns. Business Process Management Workshops 2018: 56-68 - [c26]Pieter De Koninck, Seppe vanden Broucke, Jochen De Weerdt:
act2vec, trace2vec, log2vec, and model2vec: Representation Learning for Business Processes. BPM 2018: 305-321 - [c25]Galina Deeva, Monique Snoeck, Jochen De Weerdt:
Discovering the Impact of Students' Modeling Behavior on their Final Performance. PoEM 2018: 335-350 - 2017
- [j17]Pieter De Koninck, Jochen De Weerdt, Seppe K. L. M. vanden Broucke:
Explaining clusterings of process instances. Data Min. Knowl. Discov. 31(3): 774-808 (2017) - [j16]Seppe K. L. M. vanden Broucke, Jochen De Weerdt:
Fodina: A robust and flexible heuristic process discovery technique. Decis. Support Syst. 100: 109-118 (2017) - [j15]Wei Zhe Low, Wil M. P. van der Aalst, Arthur H. M. ter Hofstede, Moe Thandar Wynn, Jochen De Weerdt:
Change visualisation: Analysing the resource and timing differences between two event logs. Inf. Syst. 65: 106-123 (2017) - [j14]Pieter De Koninck, Jochen De Weerdt:
Similarity-Based Approaches for Determining the Number of Trace Clusters in Process Discovery. Trans. Petri Nets Other Model. Concurr. 12: 19-42 (2017) - [c24]Galina Deeva, Johannes De Smedt, Pieter De Koninck, Jochen De Weerdt:
Dropout Prediction in MOOCs: A Comparison Between Process and Sequence Mining. Business Process Management Workshops 2017: 243-255 - [c23]Pieter De Koninck, Klaas Nelissen, Bart Baesens, Seppe vanden Broucke, Monique Snoeck, Jochen De Weerdt:
An Approach for Incorporating Expert Knowledge in Trace Clustering. CAiSE 2017: 561-576 - [c22]Sandra Mitrovic, Gaurav Singh, Bart Baesens, Wilfried Lemahieu, Jochen De Weerdt:
Scalable RFM-enriched Representation Learning for Churn Prediction. DSAA 2017: 79-88 - [c21]Johannes De Smedt, Galina Deeva, Jochen De Weerdt:
Behavioral Constraint Template-Based Sequence Classification. ECML/PKDD (2) 2017: 20-36 - [c20]Sandra Mitrovic, Bart Baesens, Wilfried Lemahieu, Jochen De Weerdt:
Churn Prediction Using Dynamic RFM-Augmented Node2vec. PAP@PKDD/ECML 2017: 122-138 - 2016
- [j13]Johannes De Smedt, Jochen De Weerdt, Jan Vanthienen, Geert Poels:
Mixed-Paradigm Process Modeling with Intertwined State Spaces. Bus. Inf. Syst. Eng. 58(1): 19-29 (2016) - [j12]Johannes De Smedt, Jochen De Weerdt, Jan Vanthienen, Geert Poels:
Erratum to: Mixed-Paradigm Process Modeling with Intertwined State Spaces. Bus. Inf. Syst. Eng. 58(1): 101-104 (2016) - [j11]Gayane Sedrakyan, Jochen De Weerdt, Monique Snoeck:
Process-mining enabled feedback: "Tell me what I did wrong" vs. "tell me how to do it right". Comput. Hum. Behav. 57: 352-376 (2016) - [j10]Wei Zhe Low, Seppe K. L. M. vanden Broucke, Moe Thandar Wynn, Arthur H. M. ter Hofstede, Jochen De Weerdt, Wil M. P. van der Aalst:
Revising history for cost-informed process improvement. Computing 98(9): 895-921 (2016) - [c19]Pieter De Koninck, Jochen De Weerdt:
Determining the Number of Trace Clusters: a Stability-based Approach. ATAED@Petri Nets/ACSD 2016: 1-15 - [c18]Pieter De Koninck, Jochen De Weerdt:
Multi-objective Trace Clustering: Finding More Balanced Solutions. Business Process Management Workshops 2016: 49-60 - [c17]Pieter De Koninck, Jochen De Weerdt:
A Stability Assessment Framework for Process Discovery Techniques. BPM 2016: 57-72 - [c16]Johannes De Smedt, Jochen De Weerdt, Estefanía Serral, Jan Vanthienen:
Improving Understandability of Declarative Process Models by Revealing Hidden Dependencies. CAiSE 2016: 83-98 - [c15]Estefanía Serral, Jochen De Weerdt, Gayane Sedrakyan, Monique Snoeck:
Automating immediate and personalized feedback taking conceptual modelling education to a next level. RCIS 2016: 1-6 - 2015
- [j9]Johannes De Smedt, Jochen De Weerdt, Jan Vanthienen:
Fusion Miner: Process discovery for mixed-paradigm models. Decis. Support Syst. 77: 123-136 (2015) - [c14]Johannes De Smedt, Jochen De Weerdt, Estefanía Serral, Jan Vanthienen:
Gamification of Declarative Process Models for Learning and Model Verification. Business Process Management Workshops 2015: 432-443 - 2014
- [j8]Filip Caron, Jan Vanthienen, Kris Vanhaecht, Erik van Limbergen, Jochen De Weerdt, Bart Baesens:
Monitoring care processes in the gynecologic oncology department. Comput. Biol. Medicine 44: 88-96 (2014) - [j7]Gayane Sedrakyan, Monique Snoeck, Jochen De Weerdt:
Process mining analysis of conceptual modeling behavior of novices - empirical study using JMermaid modeling and experimental logging environment. Comput. Hum. Behav. 41: 486-503 (2014) - [j6]Luciano García-Bañuelos, Marlon Dumas, Marcello La Rosa, Jochen De Weerdt, Chathura C. Ekanayake:
Controlled automated discovery of collections of business process models. Inf. Syst. 46: 85-101 (2014) - [j5]Seppe K. L. M. vanden Broucke, Jochen De Weerdt, Jan Vanthienen, Bart Baesens:
Determining Process Model Precision and Generalization with Weighted Artificial Negative Events. IEEE Trans. Knowl. Data Eng. 26(8): 1877-1889 (2014) - [c13]Jochen De Weerdt, Seppe K. L. M. vanden Broucke:
SECPI: Searching for Explanations for Clustered Process Instances. BPM 2014: 408-415 - [c12]Jochen De Weerdt, Seppe K. L. M. vanden Broucke, Filip Caron:
Bidimensional Process Discovery for Mining BPMN Models. Business Process Management Workshops 2014: 529-540 - [c11]Wei Zhe Low, Jochen De Weerdt, Moe Thandar Wynn, Arthur H. M. ter Hofstede, Wil M. P. van der Aalst, Seppe K. L. M. vanden Broucke:
Perturbing event logs to identify cost reduction opportunities: A genetic algorithm-based approach. IEEE Congress on Evolutionary Computation 2014: 2428-2435 - [c10]Johannes De Smedt, Jochen De Weerdt, Jan Vanthienen:
Multi-paradigm Process Mining: Retrieving Better Models by Combining Rules and Sequences - (Short Paper). OTM Conferences 2014: 446-453 - 2013
- [j4]Jochen De Weerdt, Annelies Schupp, An Vanderloock, Bart Baesens:
Process Mining for the multi-faceted analysis of business processes - A case study in a financial services organization. Comput. Ind. 64(1): 57-67 (2013) - [j3]Jochen De Weerdt, Seppe K. L. M. vanden Broucke, Jan Vanthienen, Bart Baesens:
Active Trace Clustering for Improved Process Discovery. IEEE Trans. Knowl. Data Eng. 25(12): 2708-2720 (2013) - [c9]Moe Wynn, Jochen De Weerdt, Arthur H. M. ter Hofstede, Wil M. P. van der Aalst, Hajo A. Reijers, Michael Adams, Chun Ouyang, Michael Rosemann, Wei Zhe Low:
Cost-Aware Business Process Management: A Research Agenda. ACIS 2013: 110 - [c8]Boudewijn F. van Dongen, Barbara Weber, Diogo R. Ferreira, Jochen De Weerdt:
Report: Business Process Intelligence Challenge 2013. Business Process Management Workshops 2013: 79-87 - [c7]Seppe K. L. M. vanden Broucke, Jochen De Weerdt, Jan Vanthienen, Bart Baesens:
A comprehensive benchmarking framework (CoBeFra) for conformance analysis between procedural process models and event logs in ProM. CIDM 2013: 254-261 - [e1]Boudewijn F. van Dongen, Barbara Weber, Diogo R. Ferreira, Jochen De Weerdt:
Proceedings of the 3rd Business Process Intelligence Challenge co-located with 9th International Business Process Intelligence Workshop (BPI 2013), Beijing, China, August 26, 2013. CEUR Workshop Proceedings 1052, CEUR-WS.org 2013 [contents] - 2012
- [b1]Jochen De Weerdt:
Business process discovery: new techniques and applications. Katholieke Universiteit Leuven, Belgium, 2012 - [j2]Jochen De Weerdt, Manu De Backer, Jan Vanthienen, Bart Baesens:
A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Inf. Syst. 37(7): 654-676 (2012) - [c6]Seppe K. L. M. vanden Broucke, Jochen De Weerdt, Bart Baesens, Jan Vanthienen:
Improved Artificial Negative Event Generation to Enhance Process Event Logs. CAiSE 2012: 254-269 - [c5]Jochen De Weerdt, Seppe K. L. M. vanden Broucke, Jan Vanthienen, Bart Baesens:
Leveraging process discovery with trace clustering and text mining for intelligent analysis of incident management processes. IEEE Congress on Evolutionary Computation 2012: 1-8 - [c4]Jochen De Weerdt, Filip Caron, Jan Vanthienen, Bart Baesens:
Getting a Grasp on Clinical Pathway Data: An Approach Based on Process Mining. PAKDD Workshops 2012: 22-35 - 2011
- [j1]Stijn Goedertier, Jochen De Weerdt, David Martens, Jan Vanthienen, Bart Baesens:
Process discovery in event logs: An application in the telecom industry. Appl. Soft Comput. 11(2): 1697-1710 (2011) - [c3]Filip Caron, Jan Vanthienen, Jochen De Weerdt, Bart Baesens:
Advanced Care-Flow Mining and Analysis. Business Process Management Workshops (1) 2011: 167-168 - [c2]Jochen De Weerdt, Manu De Backer, Jan Vanthienen, Bart Baesens:
A robust F-measure for evaluating discovered process models. CIDM 2011: 148-155 - 2010
- [c1]Jochen De Weerdt, Manu De Backer, Jan Vanthienen, Bart Baesens:
A Critical Evaluation Study of Model-Log Metrics in Process Discovery. Business Process Management Workshops 2010: 158-169
Coauthor Index
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