Automated robotic process automation: a self-learning approach

Junxiong Gao; Sebastiaan J. van Zelst; Xixi Lu; Wil M. P. van der Aalst

doi:10.1007/978-3-030-33246-4_6

Automated robotic process automation: a self-learning approach

Junxiong Gao, Sebastiaan J. van Zelst, Xixi Lu, Wil M. P. van der Aalst

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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.

Original language	English
Title of host publication	On the Move to Meaningful Internet Systems: OTM 2019 Conferences
Subtitle of host publication	Confederated International Conferences: CoopIS, ODBASE, C&TC 2019, Rhodes, Greece, October 21-25, 2019, Proceedings
Editors	Hervé Panetto
Place of Publication	Cham
Publisher	Springer
Pages	95-112
Number of pages	18
ISBN (Electronic)	9783030332464
ISBN (Print)	9783030332471, 9783030332457
DOIs	https://doi.org/10.1007/978-3-030-33246-4_6
Publication status	Published - 2019

Publication series

Name	Lecture notes in computer science
Volume	11877

Name	LNCS sublibrary. SL 2, Programming and software engineering

Keywords

robotic process automation
information systems
user interaction
data mining
knowledge discovery

Access to Document

10.1007/978-3-030-33246-4_6

978-3-030-33246-4_6Final published version, 2.2 MBLicence: Taverne

Cite this

Gao, J., Zelst, S. J. V., Lu, X., & Aalst, W. M. P. V. D. (2019). Automated robotic process automation: a self-learning approach. In H. Panetto (Ed.), On the Move to Meaningful Internet Systems: OTM 2019 Conferences: Confederated International Conferences: CoopIS, ODBASE, C&TC 2019, Rhodes, Greece, October 21-25, 2019, Proceedings (pp. 95-112). (Lecture notes in computer science; Vol. 11877), (LNCS sublibrary. SL 2, Programming and software engineering). Springer. https://doi.org/10.1007/978-3-030-33246-4_6

Gao, Junxiong ; Zelst, Sebastiaan J. van ; Lu, Xixi et al. / Automated robotic process automation : a self-learning approach. On the Move to Meaningful Internet Systems: OTM 2019 Conferences: Confederated International Conferences: CoopIS, ODBASE, C&TC 2019, Rhodes, Greece, October 21-25, 2019, Proceedings. editor / Hervé Panetto. Cham : Springer, 2019. pp. 95-112 (Lecture notes in computer science). (LNCS sublibrary. SL 2, Programming and software engineering).

@inproceedings{057b3a4150a34eaf966f4d2f14d22013,

title = "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.",

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

author = "Junxiong Gao and Zelst, {Sebastiaan J. van} and Xixi Lu and Aalst, {Wil M. P. van der}",

year = "2019",

doi = "10.1007/978-3-030-33246-4_6",

language = "English",

isbn = "9783030332471",

series = "Lecture notes in computer science",

publisher = "Springer",

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Gao, J, Zelst, SJV, Lu, X & Aalst, WMPVD 2019, Automated robotic process automation: a self-learning approach. in H Panetto (ed.), On the Move to Meaningful Internet Systems: OTM 2019 Conferences: Confederated International Conferences: CoopIS, ODBASE, C&TC 2019, Rhodes, Greece, October 21-25, 2019, Proceedings. Lecture notes in computer science, vol. 11877, LNCS sublibrary. SL 2, Programming and software engineering, Springer, Cham, pp. 95-112. https://doi.org/10.1007/978-3-030-33246-4_6

Automated robotic process automation: a self-learning approach. / Gao, Junxiong; Zelst, Sebastiaan J. van; Lu, Xixi et al.
On the Move to Meaningful Internet Systems: OTM 2019 Conferences: Confederated International Conferences: CoopIS, ODBASE, C&TC 2019, Rhodes, Greece, October 21-25, 2019, Proceedings. ed. / Hervé Panetto. Cham: Springer, 2019. p. 95-112 (Lecture notes in computer science; Vol. 11877), (LNCS sublibrary. SL 2, Programming and software engineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Automated robotic process automation

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AU - Zelst, Sebastiaan J. van

AU - Lu, Xixi

AU - Aalst, Wil M. P. van der

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

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Gao J, Zelst SJV, Lu X, Aalst WMPVD. Automated robotic process automation: a self-learning approach. In Panetto H, editor, On the Move to Meaningful Internet Systems: OTM 2019 Conferences: Confederated International Conferences: CoopIS, ODBASE, C&TC 2019, Rhodes, Greece, October 21-25, 2019, Proceedings. Cham: Springer. 2019. p. 95-112. (Lecture notes in computer science). (LNCS sublibrary. SL 2, Programming and software engineering). doi: 10.1007/978-3-030-33246-4_6

Automated robotic process automation: a self-learning approach

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