Query languages are essential for exploring, working with data and directly answering questions from data. SQL is the prime example for answering questions on relational data. Behavioral data is recorded in the form of events with timestamps. Various techniques such as Process Mining use the data in the form of event logs to aggregate and Read More …
Zahra Toosinezhad, Dirk Fahland, and Wil van der Aalst have won the Best Paper award at ICPM with her paper “Detecting System-Level Behavior Leading To Dynamic Bottlenecks“. Congratulations to Zahra , Dirk, and Wil!
Xixi Lu, a former PhD student of our group, has won the Best PhD. Dissertation award with her thesis “Using behavioral context in process mining: exploration, preprocessing and analysis of event data“. Her promotor was Wil van der Aalst, and Dirk Fahland was one of her copromotors.
Summary In the context of the “Process Mining in Logistics” research project between Vanderlande Industries, we are offering multiple Master projects on process mining on event data of large-scale material handling systems. The fundamental challenges addressed are size (logistics processes are a factor 10-100 larger than business processes), reliable performance analysis and process prediction. We Read More …
Understanding and predicting behavior of people and machines in a shared setting (task, project, factory, process, organization) is central to Data Science and Artificial Intelligence. Actions of people and machines can be recorded as discrete events in event sequences (logs), event databases (tables, graphs), and real-time event streams. Learning behavioral models of discrete event data Read More …
Only for BDMA students. Links 2AMR10 / 2020Q1 @ Osiris Staff
Graafmans, T. L. F., Türetken, O., Poppelaars, J. J. G. H., & Fahland, D. (Accepted/In press). Process mining for six sigma: a guideline and tool support. Business & Information Systems Engineering. Abstract Process mining offers a set of techniques for gaining data-based insights into business processes from event logs. The literature acknowledges the potential benefits Read More …
On February 1st, Eva Klijn and Fatemeh Shafiee started working as PhD students in the PA group. Eva started as PhD-TA, and is supervised by Dirk Fahland. Fatemeh started as a PhD student on the TACTICS project, and is supervised by Natalia Sidorova. A big welcome to the both of them!
Esser, S., & Fahland, D. (2019). Storing and querying multi-dimensional process event logs using graph databases. In 15th International Workshop on Business Process Intelligence Abstract Process event data is usually stored either in a sequential process event log or in a relational database. While the sequential, single-dimensional nature of event logs aids querying for event Read More …
Denisov, V., Fahland, D., & van der Aalst, W. M. P. (2019). Predictive performance monitoring of material handling systems using the performance spectrum. In Proceedings – 2019 International Conference on Process Mining, ICPM 2019 (pp. 137-144). [8786068] Piscataway: Institute of Electrical and Electronics Engineers. DOI: 10.1109/ICPM.2019.00029 Abstract Predictive performance analysis is crucial for supporting operational Read More …
Klijn, E. L., & Fahland, D. (2019). Performance mining for batch processing using the performance spectrum. In 15th International Workshop on Business Process Intelligence Abstract Performance analysis from process event logs is a central element of business process management and improvement. Established performance analysis techniques aggregate time-stamped event data to identify bottlenecks or to visualize Read More …
Fahland, D. (2019). Describing behavior of processes with many-to-many interactions. In S. Haar, & S. Donatelli (Eds.), Application and Theory of Petri Nets and Concurrency – 40th International Conference, PETRI NETS 2019, Proceedings (pp. 3-24). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11522 LNCS). Read More …
Fahland, D., Köhler-Bußmeier, M., & Moldt, D. (2018). Preface: Workshop on Petri Nets and Modeling 2018. In MOD-WS 2018 (CEUR Workshop Proceedings; Vol. 2060). CEUR-WS.org.
Leemans, S. J. J., & Fahland, D. (2019). Information-preserving abstractions of event data in process mining. Knowledge and Information Systems. DOI: 10.1007/s10115-019-01376-9 Abstract Process mining aims at obtaining information about processes by analysing their past executions in event logs, event streams, or databases. Discovering a process model from a finite amount of event data thereby Read More …
In this meeting the student gets insight into the study program. Links 2IMC90 / 2020 @ Osiris Staff
Process Discovery is an unsupervised learning problem with the task of discovering a graph-based model from sequences (or graphs) of event data that describes the data best. Generative Adversarial Neural Networks (GANNs) are a type of neural networks used to learn structures in an unsupervised fashion. The objective of this project is to explore the Read More …
Process mining assumes event data to be stored in an event log, which is technically either a relational table (attributes as columns) or a stream of events (attribute value pairs). Recently, we developed a new technique to store event data in a Graph database such as Neo4j. This allows to do process mining over various Read More …
A recent visual analytics technique called the “Performance Spectrum” https://github.com/processmining-in-logistics/psm allows us to gain more fine-grained insights into performance behavior and changes over time. A TU/e Master student showed that it is possible to mine synchronization of cases from the performance spectrum data showing that the behavior of a case depends on the mechanisms and Read More …
for event log clustering, outlier detection, and pre-processing. We recently developed a technique to detect the context of events from an event log in an efficient way through sub-graph matching. This allows to identify events and parts of event logs which are similar or different to each other, allowing to cluster traces, detect outliers, and Read More …
The quality of process mining results highly depends on the quality of the input data where noise, infrequent behaviors, log incompleteness or many different variants undercut the assumptions of process discovery algorithms, and lead to low-quality results. ProM provides numerous event log pre-processing and filtering options, but they require expert knowledge to understand when which Read More …
Denisov, Vadim, Belkina, Elena, Fahland, Dirk & van der Aalst, Wil M.P. (2018). The performance spectrum miner : visual analytics for fine-grained performance analysis of processes. CEUR Workshop Proceedings, 2196, 96-100. Abstract We present the Performance Spectrum Miner, a ProM plugin, which implements a new technique for fine-grained performance analysis of processes. The technique uses Read More …
Burattin, Andrea, Soffer, Pnina, Fahland, Dirk, Mendling, Jan, Reijers, Hajo A., Vanderfeesten, Irene, Weidlich, Matthias & Weber, Barbara (2018). Who is behind the model? classifying modelers based on pragmatic model features. In Ingo Weber, Jan vom Brocke, Marco Montali & Mathias Weske (Eds.), Business Process Management – 16th International Conference, BPM 2018, Proceedings (pp. 322-338). Read More …
ProcessGold is a software supplier bringing together Process Mining and Business Intelligence, driven by highly skilled ICT entrepreneurs and backed by a wealth of experience. ProcessGold recently released a new Process Mining platform, the ProcessGold Enterprise Platform, that combines data extraction, process mining techniques, and visual analytics in order to produce dynamic visual reports which Read More …
Vanderlande is the global market leader for value-added logistic process automation at airports and in the parcel market. The company is also a leading supplier of process automation solutions for warehouses. Some figures: Vanderlande’s baggage handling systems move 3.7 billion pieces of luggage around the world per year. Our systems are active in 600 airports Read More …
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Denisov, V.V., Fahland, D. & van der Aalst, W.M.P. (2018). Unbiased, fine-grained description of processes performance from event data. Business Process Management – 16th International Conference, BPM 2018, Sydney, NSW, Australia, September 9-14, 2018, Proceedings. (pp. 139-157). (Lecture Notes in Computer Science, No. 11080). Springer. Abstract Performance is central to processes management and event data Read More …
Leemans, S.J.J., Fahland, D. & van der Aalst, W.M.P. (2018). Scalable process discovery and conformance checking. Software and Systems Modeling, 17(2), 599-631. Abstract Considerable amounts of data, including process events, are collected and stored by organisations nowadays. Discovering a process model from such event data and verification of the quality of discovered models are important Read More …
Dirk is Associate Professor (UHD) in the PA group. He completed his PhD with summa cum laude at Humboldt-Univeristät zu Berlin and Eindhoven University of Technology in 2010. His research interests include distributed processes and systems built from distributed components for which he investigates modeling systems (using process modeling languages, Petri nets, or scenario-based techniques), Read More …
Lu, X. (2018). Using behavioral context in process mining : exploration, preprocessing and analysis of event data. Eindhoven: Technische Universiteit Eindhoven. ((Co-)promot.: Wil van der Aalst, Dirk Fahland & Nicola Zannone)
Polderdijk, Melanie, Vanderfeesten, Irene, Erasmus, Jonnro, Traganos, Kostas, Bosch, Tim, van Rhijn, Gu & Fahland, Dirk (2018). A visualization of human physical risks in manufacturing processes using BPMN. Business Process Management Workshops – BPM 2017 International Workshops, Revised Papers (pp. 732-743). (Lecture Notes in Business Information Processing, No. 308). Springer. Abstract Process models are schematic Read More …
Fahland, Dirk, Köhler-Bußmeier, Michael & Moldt, Daniel (2018). Workshop on Petri Nets and Modeling 2018 (PeMod’2018). CEUR Workshop Proceedings, 2060
Tax, N., Lu, X., Sidorova, N., Fahland, D. & van der Aalst, W.M.P. (2018). The imprecisions of precision measures in process mining. Information Processing Letters, 135, 1-8. Abstract In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log. Although several measures have been Read More …
Fahland, Dirk & Völzer, Hagen (2018). Dynamic skipping and blocking, dead path elimination for cyclic workflows, and a local semantics for inclusive gateways. Information Systems, 78, 126-143. Abstract We propose and study dynamic versions of the classical flexibility constructs ‘skip’ and ‘block’ for workflows and motivate and define a formal semantics for them. We show Read More …
Alizadeh, M., Lu, X., Fahland, D., Zannone, N. & van der Aalst, W.M.P. (2018). Linking data and process perspectives for conformance analysis. Computers and Security, 73, 172-193. Abstract The detection of data breaches has become a major challenge for most organizations. The problem lies in the fact that organizations often lack proper mechanisms to control Read More …
In this seminar, a group of master students will get in touch with research in the area of Information Systems, where Process Mining and Process Analysis from Event Data are the central themes. We study recent publications in the area of process mining and practical applications on real-life examples, to provide a good insight into Read More …
People interested in the ‘process side’ of information systems can take the course ‘Capita selecta architecture of information systems’. This course will be organized in an ad-hoc manner taking into account the interests of the student. The focus will always be on a particular ‘hot topic’ in the information systems domain. The course can, in Read More …
This course focuses on enterprise information systems that are driven by models, i.e., instead of constructing code these systems are assembled, configured or generated using a model-driven approach. Of particular interest are so-called “process-aware” information systems. Typical examples are workflow management systems and the process engines of ERP, CRM, PDM and other enterprise information systems. Read More …
Process mining provides a new means to understand and improve processes in an objective way in a variety of application domains through the analysis of recorded event data. This advanced course on process mining teaches students the fundamental concepts and theoretical foundations of process mining along a complete process mining methodology, and exposes students to Read More …
The objective of the Data Challenge courses is to teach students how to perform large-scale data-driven analyses themselves, combining technical skills acquired earlier with insights gained in methodological courses. The focus of Data Challenge 3 is to take students through the entire life-cycle of a data analysis for public stakeholders, starting in a typical situation Read More …
Process Mining in Logistics is a joint project of the Data Science Center Eindhoven and Vanderlande industries. Description Logistics processes are notoriously difficult to design, analyze, and to improve. Where classical processes are scoped around the processing of information associated to a specific unique case, logistics deals with physical objects that are grouped and processed Read More …
