Improving Process Discovery Results by Filtering Outliers Using Conditional Behavioural Probabilities

Sani, Mohammadreza Fani; Zelst, Sebastiaan J. van; Aalst, Wil M. P. van der

doi:10.1007/978-3-319-74030-0_16

Cited by 50 publications

(39 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, classical frequency-based filtering approaches, like (Conforti et al 2017), cannot be applied to address this problem. One of the possible solutions is to use sequential pattern mining techniques to distinguish between events that are part of mainstream behavior and outlier events (Sani et al 2017). However, in case some events are rarely seen during a task execution they can be mistakenly treated as outliers.…”

Section: Challenges and Guidelinesmentioning

confidence: 99%

Robotic Process Mining: Vision and Challenges

et al. 2020

View full text Add to dashboard Cite

Robotic process automation (RPA) is an emerging technology that allows organizations automating repetitive clerical tasks by executing scripts that encode sequences of fine-grained interactions with Web and desktop applications. Examples of clerical tasks include opening a file, selecting a field in a Web form or a cell in a spreadsheet, and copy-pasting data across fields or cells. Given that RPA can automate a wide range of routines, this raises the question of which routines should be automated in the first place. This paper presents a vision towards a family of techniques, termed robotic process mining (RPM), aimed at filling this gap. The core idea of RPM is that repetitive routines amenable for automation can be discovered from logs of interactions between workers and Web and desktop applications, also known as user interactions (UI) logs. The paper defines a set of basic concepts underpinning RPM and presents a pipeline of processing steps that would allow an RPM tool to generate RPA scripts from UI logs. The paper also discusses research challenges to realize the envisioned pipeline.

show abstract

Section: Challenges and Guidelinesmentioning

confidence: 99%

Robotic Process Mining: Vision and Challenges

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Previous studies [2,1] have identified a collection of recurrent quality issues that commonly affect event logs, among these issues also incorrect timestamps. However, in the literature, log filtering is usually addressed from a noise-oriented perspective, focusing on removing infrequent traces and/or events from an event log [4,5,9] or event stream [11,12].…”

Section: Background and Related Workmentioning

confidence: 99%

“…In fact, filtering the log or its L-automaton is common in many process discovery techniques [15,16]. To improve the quality of the Lautomaton, and the effectiveness of our technique, we recommend to apply one of the many techniques to remove infrequent behavior from the log [4,5,9]. In this paper, we rely on the method in [5].…”

Section: Tablementioning

confidence: 99%

“…Thus, timestamp errors may also lead to wrong insights on the performance or conformance of the process at hand. While many approaches have been proposed to filter out or repair events in order to improve the overall quality of an event log [4][5][6][7][8][9][10][11][12], only two of them [6,8] address the problem of repairing timestamp errors and they do so under a strict requirement: the presence of a (reference) process model with execution time annotations as input.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Repair of Same-Timestamp Errors in Business Process Event Logs

Conforti¹,

Rosa

Hofstede

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

This paper contributes an approach for automatically correcting "same-timestamp" errors in business process event logs. These errors consist in multiple events exhibiting the same timestamp within a given process instance. Such errors are common in practice and can be due to the logging granularity or the performance load of the logging system. Analyzing logs that have not been properly screened for such problems is likely to lead to wrong or misleading process insights. The proposed approach revolves around two techniques: one to reorder events with same-timestamp errors, the other to assign an estimated timestamp to each such event. The approach has been implemented in a software prototype and extensively evaluated in different settings, using both artificial and real-life logs. The experiments show that the approach significantly reduces the number of inaccurate timestamps, while the reordering of events scales well to large and complex datasets. The evaluation is complemented by a case study in the meat & livestock domain showing the usefulness of the approach in practice.

show abstract

“…In these cases, and if this behavior is infrequent enough to allow the event log to remain meaningful, the most common way for existing process mining techniques to deal with missing data is by filtering out the affected traces and performing discovery and conformance checking on the resulting filtered event log. While filtering out missing values is straightforward, various methodologies of event log filtering have been proposed in the past to solve the problem of incorrect event attributes: the filtering can take place thanks to a reference model, which can be given as process specification [12], or from information discovered from the frequent and well-formed traces of the same event log; for example extracting an automaton from the frequent traces [7], computing conditional probabilities of frequent sequences of activities [9], or discovering a probabilistic automaton [13]. In the latter cases, the noise is identified as infrequent behavior.…”

Section: Related Workmentioning

confidence: 99%

Mining Uncertain Event Data in Process Mining

Pegoraro

Aalst

2019

2019 International Conference on Process Mining (ICPM)

View full text Add to dashboard Cite

0000−0002−8997−7517] and Wil M.P. van der Aalst [0000−0002−0955−6940]Abstract. Nowadays, more and more process data are automatically recorded by information systems, and made available in the form of event logs. Process mining techniques enable process-centric analysis of data, including automatically discovering process models and checking if event data conform to a certain model. In this paper we analyze the previously unexplored setting of uncertain event logs: logs where quantified uncertainty is recorded together with the corresponding data. We define a taxonomy of uncertain event logs and models, and we examine the challenges that uncertainty poses on process discovery and conformance checking. Finally, we show how upper and lower bounds for conformance can be obtained aligning an uncertain trace onto a regular process model.

show abstract

Improving Process Discovery Results by Filtering Outliers Using Conditional Behavioural Probabilities

Cited by 50 publications

References 19 publications

Robotic Process Mining: Vision and Challenges

Robotic Process Mining: Vision and Challenges

Automatic Repair of Same-Timestamp Errors in Business Process Event Logs

Mining Uncertain Event Data in Process Mining

Contact Info

Product

Resources

About