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Part of book or chapter of book . 2019 . Peer-reviewed
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Automated Robotic Process Automation: A Self-Learning Approach

a self-learning approach
Authors: Junxiong Gao; Sebastiaan J. van Zelst; Xixi Lu 0001; Wil M. P. van der Aalst;

Automated Robotic Process Automation: A Self-Learning Approach

Abstract

Robotic Process Automation (RPA) recently gained a lot of attention, in both 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 the 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 the front-end, in great detail. Therefore, in this paper, we present a novel end-to-end approach that allows for completely automated, algorithmic RPA-rule deduction, on the basis of captured user behavior. Furthermore, our proposed approach is accompanied by a publicly available proof-of-concept implementation.

Country
Netherlands
Keywords

knowledge discovery, data mining, information systems, user interaction, robotic process automation

  • BIP!
    Impact byBIP!
    selected citations
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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
29
Top 10%
Top 10%
Top 10%