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Archive for November, 2019

Uncertanty in process mining: discovering models

November 28th, 2019 | by

This post is by Marco Pegoraro, Scientific Assistant in the Process And Data Science group at RWTH Aachen University. Contact him via email for further inquiries.

When applying process mining in real-life settings, the need to address anomalies in data recording when performing analyses is omnipresent. A number of such anomalies can be modeled by using the notion of uncertainty: uncertain event logs contain, alongside the event data, some attributes that describe a certain level of uncertainty affecting the data.

An example of uncertain trace. Some events have a set of possible activity labels, while some others have an interval of possible timestamps.

Uncertainty can be addressed by filtering out the affected events when it appears sporadically throughout an event log. Conversely, in situations where uncertainty affects a signifi cant fraction of an event log, fi ltering away uncertain events can lead to information loss such that analysis becomes very difficult. In this circumstance, it is important to deploy process mining techniques that allow to mine information also from the uncertain part of the process.

In the paper “Discovering Process Models from Uncertain Event Data” (Marco Pegoraro, Merih Seran Uysal, Wil M.P. van der Aalst) we present a methodology to obtain Uncertain Directly-Follows Graphs (UDFGs), models based on directed graphs that synthesize information about the uncertainty contained in the process. We then show how to convert UDFGs in models with execution semantics via fi ltering on uncertainty information and inductive mining.

Robotic Process Automation

November 21st, 2019 | by

This post is by Junxiong Gao, Scientific Assistant in the Process And Data Science group at RWTH Aachen University. Contact him via email for further inquiries.

Robotic Process Automation (RPA) recently gained a lot of attention, both in industry and academia. RPA embodies a collection of tools and techniques that allow business owners to automate repetitive manual tasks. The intrinsic value of RPA is beyond dispute, e.g., automation reduces errors and costs and thus allows us to increase overall business process performance.

However, adoption of current-generation RPA tools requires a manual effort w.r.t. identification, elicitation and programming of the to-be-automated tasks. At the same time, several techniques exist that allow us to track the exact behavior of users, in great detail.

Therefore, in this line of research, we present a novel end-to-end structure design that allows for completely automated, algorithmic automation-rule deduction, on the basis of captured user behavior.