A Reference Data Model for Process-Related User Interaction Logs

Abb, Luka; Rehse, Jana-Rebecca

doi:10.1007/978-3-031-16103-2_7

Cited by 11 publications

(3 citation statements)

References 29 publications

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“…1. This reference model defines the core attributes of UI logs but remains flexible with regard to the scope, level of abstraction, and case notion [2]. It defines the activity of a UI log as a combination of an action (e.g., click or input) and a target object in the user interface.…”

Section: Specifying and Collecting Ui Logsmentioning

confidence: 99%

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Mastering Robotic Process Automation with Process Mining

Agostinelli

Marrella

Abb

et al. 2022

Lecture Notes in Computer Science

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Robotic Process Automation (RPA) is an emerging automation technology that creates software (SW) robots to partially or fully automate rule-based and repetitive tasks (aka routines) previously performed by human users in their applications' user interfaces (UIs). Successful usage of RPA requires strong support by skilled human experts, from the detection of the routines to be automated to the development of the executable scripts required to enact SW robots. In this paper, we discuss how process mining can be leveraged to minimize the manual and time-consuming steps required for the creation of SW robots, enabling new levels of automation and support for RPA. We first present a reference data model that can be used for a standardized specification of UI logs recording the interactions between workers and SW applications to enable interoperability among different tools. Then, we introduce a pipeline of processing steps that enable us to (1) semi-automatically discover the anatomy of a routine directly from the UI logs, and (2) automatically develop executable scripts for performing SW robots at run-time. We show how this pipeline can be effectively enacted by researchers/practitioners through the SmartRPA tool.

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Section: Specifying and Collecting Ui Logsmentioning

confidence: 99%

“…step (1), and manually define the flowchart diagrams representing the structure of such routines, cf. step (2). These diagrams will drive the development of the executable scripts (also called RPA scripts), allowing for the concrete enactment of SW robots at run-time, cf.…”

Section: Introductionmentioning

confidence: 99%

Mastering Robotic Process Automation with Process Mining

Agostinelli

Marrella

Abb

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Relying on the similarity among the concepts of "data pipeline" and "business process", one of the project's vision is to leverage and customize well-established process mining techniques to reveal fact-based insights into how data pipelines transpire and access dark data [4]. However, traditional event logs used for process mining are limited in scope [2]. They include attributes tailored to recording sequence-flow details of process execution (e.g., timestamp and completion of activities, etc.…”

Section: Introductionmentioning

confidence: 99%

A Reference Data Model to Specify Event Logs for Big Data Pipeline Discovery

Benvenuti,

Marrella,

Rossi

et al. 2023

Lecture Notes in Business Information Processing

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State-of-the-art approaches for managing Big Data pipelines assume their anatomy is known by design and expressed through adhoc Domain-Specific Languages (DSLs), with insufficient knowledge of the dark data involved in the pipeline execution. Dark data is data that organizations acquire during regular business activities but is not used to derive insights or for decision-making. The recent literature on Big Data processing agrees that a new breed of Big Data pipeline discovery (BDPD) solutions can mitigate this issue by solely analyzing the event log that keeps track of pipeline executions over time. Relying on well-established process mining techniques, BDPD can reveal fact-based insights into how data pipelines transpire and access dark data. However, to date, a standard format to specify the concept of Big Data pipeline execution in an event log does not exist, making it challenging to apply process mining to achieve the BDPD task. To address this issue, in this paper we formalize a universally applicable reference data model to conceptualize the core properties and attributes of a data pipeline execution. We provide an implementation of the model as an extension to the XES interchange standard for event logs, demonstrate its practical applicability in a use case involving a data pipeline for managing digital marketing campaigns, and evaluate its effectiveness in uncovering dark data manipulated during several pipeline executions.

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Unsupervised Task Recognition from User Interaction Streams

Rebmann

2023

Lecture Notes in Computer Science

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A Reference Data Model for Process-Related User Interaction Logs

Cited by 11 publications

References 29 publications

Mastering Robotic Process Automation with Process Mining

Mastering Robotic Process Automation with Process Mining

A Reference Data Model to Specify Event Logs for Big Data Pipeline Discovery

Unsupervised Task Recognition from User Interaction Streams

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