Alexander Seeliger

Dr.-Ing. Alexander Seeliger

+49 6151 16-23190
+49 6151 16-23202

Hochschulstraße 10
64289 Darmstadt

Office: S2|02 A112

Research Interests

  • Process Mining of Business Processes
  • Big Data Processing and Mining
  • Knowledge Extraction of Unstructured Data
  • Machine Learning Methods and Techniques

Current / Past Research Projects

  • KI.RPA: Intelligent Robotic Process Automation (> 2018)
    The goal of the project is to develop a tool that companies can use to create virtual employee pools based on robotic process automation, which support human employees in recurring routine tasks.
  • DRUP: Deep Reasoning about Business Processes (2017 – 2018)
    The goal of the project is to develop methods that extract valuable knowledge in event logs of which the structure is not known before analysis.
  • PASAP: Process Analytics: Entwicklung von Softwaretechnologien für einen neuartigen Ansatz in der Prozessanalyse (2015 – 2017)



Supervised Theses (ongoing and finished)

  • Detection Concept Drift in Processes using Change Point Detection
  • Development of a Root-Cause Analysis Framework for Process Executions
  • Prozessgraphoptimierung mittels Motiv -basierter Graphadaption
  • Business Rule Framework für Spark Streaming
  • Intelligenter Browser für Process Mining
  • Trace Clustering using Event Attributes from Event Logs
  • Reduktion von Unstrukturiertheit in Prozessmodellen im Kontext von Prozess Mining
  • Multivariante Root-Cause-Analyse in Process Mining
  • Rule Checking in Process Models using the Taint Flow Algorithm
  • Intelligent and Systematic Browsing through Process Mining Data
  • Trace Clustering mit Expertenwissen
  • Case2vec: Distributed Representations of Event Log Traces for Process Clustering
  • Visualisierung von Prozessmodellen in Augmented Reality
  • Zeitbasierte Erkennung von Ausreißern und Abweichungen im Prozess mittels Machine Learning

Please contact me if you are interested in writing a Bachelor or Master thesis.


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Gruppiere nach: Publikationsjahr | Typ des Eintrags | Keine Gruppierung
Springe zu: 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2012
Anzahl der Einträge: 17.


Seeliger, Alexander (2020):
Intelligent Computer-assisted Process Mining.
Darmstadt, Technische Universität Darmstadt, DOI: 10.25534/tuprints-00011915,

Nolle, Timo ; Seeliger, Alexander ; Thoma, Nils ; Mühlhäuser, Max Dustdar, Schahram ; Yu, Eric ; Salinesi, Camille ; Rieu, Dominique ; Pant, Vik (Hrsg.) (2020):
DeepAlign: Alignment-Based Process Anomaly Correction Using Recurrent Neural Networks.
Springer, In: 32nd International Conference on Advanced Information Systems Engineering (CAiSE 2020), virtual Conference, 08.-12.06., S. 319-333, ISSN 0302-9743, ISBN 978-3-030-49434-6,
DOI: 10.1007/978-3-030-49435-3_20,


Nolle, Timo ; Luettgen, Stefan ; Seeliger, Alexander ; Mühlhäuser, Max (2019):
BINet: Multi-perspective business process anomaly classification.
In: Information Systems, Elsevier ScienceDirect, S. 101458, ISSN 0306-4379,
DOI: 10.1016/,

Sanchez Guinea, Alejandro ; Seeliger, Alexander ; Pejovic, Usman and Scholl ; Naeem, Usman ; Scholl, Philipp ; Mihale-Wilson, Cristina ; Di Lascio, Elena ; Azam, Muhammad Awais ; Kuo, Pei-Yi (Patricia) ; Mühlhäuser, Max ; Meurisch, Christian (2019):
UPA'19: 4th International Workshop on Ubiquitous Personal Assistance.
In: UbiComp/ISWC '19 Adjunct, In: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, New York, NY, USA, ACM, S. 1099-1101, ISBN 978-1-4503-6869-8,
DOI: 10.1145/3341162.3347755,

Seeliger, Alexander ; Sánchez Guinea, Alejandro ; Nolle, Timo ; Mühlhäuser, Max (2019):
ProcessExplorer: Intelligent Process Mining Guidance.
Springer, In: 17th International Conference on Business Process Management (BPM 2019), Wien, Austria, 01.-06.09., S. 216-231, ISBN 978-3-030-26618-9,
DOI: 10.1007/978-3-030-26619-6_15,

Seeliger, Alexander ; Ratzke, Maximilian ; Nolle, Timo ; Mühlhäuser, Max Burattin, Andrea ; Polyvyanyy, Artem ; Zelst, Sebastiaan van (Hrsg.) (2019):
ProcessExplorer: Interactive Visual Exploration of Event Logs with Analysis Guidance.
In: 1st International Conference on Process Mining - Demo Track, Aachen, Germany, In: ICPMD 2019, Aachen, Germany, June 24-26, 2019, S. 24-27, [Online-Edition:],


Seeliger, Alexander ; Nolle, Timo ; Mühlhäuser, Max Weske, Mathias ; Montali, Marco ; Weber, Ingo ; vom Brocke, Jan (Hrsg.) (2018):
Finding Structure in the Unstructured: Hybrid Feature Set Clustering for Process Discovery.
In: 16, In: Business Process Management, 11080,
Cham, Springer International Publishing, Sydney, Australia, S. 288-304, DOI: 10.1007/978-3-319-98648-7_17,

Seeliger, Alexander ; Nolle, Timo ; Mühlhäuser, Max (2018):
ProcessExplorer: An Interactive Visual Recommendation System for Process Mining.
In: KDD 2018 Workshop on Interactive Data Exploration and Analytics, London, UK, [Online-Edition:],

Nolle, Timo ; Seeliger, Alexander ; Mühlhäuser, Max Weske, Mathias ; Montali, Marco ; Weber, Ingo ; vom Brocke, Jan (Hrsg.) (2018):
BINet: Multivariate Business Process Anomaly Detection Using Deep Learning.
Springer, Sydney, Australia, September 9-14, 2018, S. 271-287, ISBN 978-3-319-98647-0,
DOI: 10.1007/978-3-319-98648-7_16,

Nolle, Timo ; Luettgen, Stefan ; Seeliger, Alexander ; Mühlhäuser, Max (2018):
Analyzing business process anomalies using autoencoders.
In: Machine Learning, ISSN 0885-6125,
DOI: 10.1007/s10994-018-5702-8,


Seeliger, Alexander ; Stein, Michael ; Mühlhäuser, Max Teniente, Ernest ; Weidlich, Matthias (Hrsg.) (2017):
Can We Find Better Process Models? Process Model Improvement using Motif-based Graph Adaptation.
In: Business Process Management Workshops, 308,
Springer International Publishing, Barcelona, Spain, S. 230-242, DOI: 10.1007/978-3-319-74030-0_17,

Seeliger, Alexander ; Nolle, Timo ; Mühlhäuser, Max Mühlhäuser, Max ; Zehbold, Cornelia (Hrsg.) (2017):
Detecting Concept Drift in Processes using Graph Metrics on Process Graphs.
9In: S-BPM ONE '17, In: Proceedings of the 9th International Conference on Subject-oriented Business Process Management (S-BPM-ONE), New York, NY, USA, ACM, Darmstadt, Germany, ISBN 978-1-4503-4862-1/17/03,
DOI: 10.1145/3040565.3040566,


Seeliger, Alexander ; Nolle, Timo ; Schmidt, Benedikt ; Mühlhäuser, Max (2016):
Process Compliance Checking using Taint Flow Analysis.
37In: Proceedings of the 37th International Conference on Information Systems (ICIS), AIS, Dublin, Ireland, S. 1-18, [Konferenzveröffentlichung]

Nolle, Timo ; Seeliger, Alexander ; Mühlhäuser, Max Calders, Toon ; Ceci, Michelangelo ; Malerba, Donato (Hrsg.) (2016):
Unsupervised Anomaly Detection in Noisy Business Process Event Logs Using Denoising Autoencoders.
In: Discovery Science: 19th International Conference, DS 2016, Bari, Italy, Proceedings, Calders, Toon Ceci, Michelangelo Malerba, Donato, Bari, Italy, S. 442-456, ISBN 978-3-319-46307-0,
DOI: 10.1007/978-3-319-46307-0_28,

Seeliger, Alexander ; Schmidt, Benedikt ; Schweizer, Immanuel ; Mühlhäuser, Max Nichols, Jeffrey ; Mahmud, Jalal ; O'Donovan, John ; Conati, Cristina ; Zancanaro, Massimo (Hrsg.) (2016):
What Belongs Together Comes Together. Activity-centric Document Clustering for Information Work.
21In: Proceedings of the 21th International Conference on Intelligent User Interfaces, ACM, Sonoma, CA, USA, S. 60-70, ISBN 978-1-4503-4137-0,
DOI: 10.1145/2856767.2856777,


Meurisch, Christian ; Seeliger, Alexander ; Schmidt, Benedikt ; Schweizer, Immanuel ; Kaup, Fabian ; Mühlhäuser, Max (2015):
Upgrading Wireless Home Routers for Enabling Large-scale Deployment of Cloudlets.
162In: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, In: Mobile Computing, Applications, and Services : 7th International Conference, MobiCASE 2015, Berlin, Springer, Berlin, Germany, November 12-13, 2015, S. 12-29, ISBN 978-3-319-29002-7,
DOI: 10.1007/978-3-319-29003-4_2,


Seeliger, Alexander ; Paulheim, Heiko (2012):
A Semantic Browser for Linked Open Data.
In: Semantic Web Challenge, [Konferenzveröffentlichung]

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