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Sander J. J. Leemans
Queensland University of Technology
Data miningProcess miningComputer scienceProcess modelingBusiness process discovery
18Publications
6H-index
279Citations
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Publications 28
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#1Alifah SyamsiyahH-Index: 4
#2Sander J. J. Leemans (QUT: Queensland University of Technology)H-Index: 6
Process executions generate event data that are typically stored in legacy information systems, such as databases. However, process discovery, which requires such event data, is performed in main memory. To bridge this gap, existing techniques must transform and extract event data, which can be expensive steps. This issue has been addressed by processing the event data directly in their origin. However, existing methods rely only on the simplest event data abstraction: the Directly Follows (DF) ...
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#1Alireza OstovarH-Index: 6
Last. Marcello La RosaH-Index: 34
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Process workers may vary the normal execution of a business process to adjust to changes in their operational environment, e.g. changes in workload, season or regulations. Changes may be simple, such as skipping an individual activity, or complex, such as replacing an entire procedure with another. Over time, these changes may negatively affect process performance; hence it is important to identify and understand them early on. As such, a number of techniques have been developed to detect proces...
3 Citations
#1Sander J. J. Leemans (QUT: Queensland University of Technology)H-Index: 6
#2Dirk Fahland (TU/e: Eindhoven University of Technology)H-Index: 25
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 has to correctly infer infinitely many unseen behaviours. Thereby, many process discovery techniques leverage abstractions on the finite event data to infer and preserve behavioural information of the underlying process. However, the fundamental information-preserving properties of these ...
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#1Syed Rehan (QUT: Queensland University of Technology)H-Index: 1
#2Suriadi Suriadi (QUT: Queensland University of Technology)H-Index: 12
Last. Hajo A. Reijers (UU: Utrecht University)H-Index: 47
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Abstract Through the application of Robotic Process Automation (RPA) organisations aim to increase their operational efficiency. In RPA, robots, or ‘bots’ for short, represent software agents capable of interacting with software systems by mimicking user actions, thus alleviating the workload of the human workforce. RPA has already seen significant uptake in practice; solution technologies are offered by multiple vendors. Contrasting with this early practical adoption is the hitherto relative la...
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#1Adriano AugustoH-Index: 6
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considers several instantiations of the framework corresponding to four optimization metaheuristics, three automated process discovery approaches (Inductive Miner - directly follows, Fodina, and Split Miner), and one accuracy metric (Markovian F-score). These framework instances are compared using a set of 20 real-life event logs. The evaluation shows that metaheuristic optimization consistently yields visible improvements in F-score for all the three automated process discovery approaches, at t...
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Last. Dirk FahlandH-Index: 25
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#1Sander J. J. Leemans (QUT: Queensland University of Technology)H-Index: 6
#2Anja F. Syring (RWTH Aachen University)H-Index: 1
Last. Wil M. P. van der Aalst (RWTH Aachen University)H-Index: 84
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Process Mining aims to support Business Process Management (BPM) by extracting information about processes from real-life process executions recorded in event logs. In particular, conformance checking aims to measure the quality of a process model by quantifying differences between the model and an event log or another model. Even though event logs provide insights into the likelihood of observed behaviour, most state-of-the-art conformance checking techniques ignore this point of view. In this ...
3 CitationsSource
#1Sander J. J. Leemans (QUT: Queensland University of Technology)H-Index: 6
#2Erik Poppe (QUT: Queensland University of Technology)H-Index: 4
Last. Moe Thandar Wynn (QUT: Queensland University of Technology)H-Index: 20
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Many organisations now seek to analyse and improve their processes using event logs from various IT systems supporting their operations. Process mining aims to obtain insights from such process data, using process discovery, conformance checking and performance measures. While many commercial process mining tools feature user-friendly directly follows-based process maps, they typically do not offer a way to assess the quality of the model, leaving users with potentially unreliable insights, whic...
1 CitationsSource
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