1,720,981 research outputs found
Generating executable robotic process automation scripts from unsegmented user interface logs
Get PDFRobotic Process Automation (RPA) is an emerging automation technology in the field of Business Process Management (BPM) that creates software (SW) robots to partially or fully automate rule-based and repetitive tasks (or simply routines) performed by human users in their applications’ user interfaces (UIs). RPA tools are able to capture in dedicated UI logs the execution of many routines of interest. A UI log consists of user actions that are mixed in some order that reflects the particular order of their execution by the user, thus potentially belonging to different routines. Moreover, when considering state-of-the-art RPA technology, it becomes apparent that the current generation of RPA tools is driven by predefined rules and manual configurations made by expert users rather than automated techniques. Towards this direction, this thesis tries to mitigate the involvement of skilled human experts, throughout the development of (i) an interactive approach to the automated segmentation of UI logs (i.e., the challenge to automatically understand which user actions contribute to which routines inside a UI log), and (ii) the SmartRPA approach to the automated identification of the variation points of a routine, to enable the selection of the most suitable routine variants to be implemented with a SW robot directly from a UI log, thus skipping completely the manual modeling activity of the flowchart diagrams. Both the approaches are implemented and evaluated employing both synthetic and real-world datasets
11 Automated segmentation of user interface logs
Get PDFRobotic Process Automation (RPA) tools are able to capture in dedicated User Interface (UI) logs the execution of high volume routines previously performed by a human user on the interface of a computer system, and then emulate their enactment in place of the user by means of a software (SW) robot. A UI log can record information about several routines, whose actions and events are mixed in some order that reflects the particular order of their execution by the user. In addition, the same user action may belong to different routines, making its automated identification far from being trivial. The issue to automatically understand which user actions contribute to a specific routine inside the UI log is also known as segmentation. In this contribution, after discussing in detail the issue of segmentation and all its potential variants, we present a novel segmentation technique that leverages trace alignment in Process Mining for automatically deriving the boundaries of a routine by analyzing the UI logs that keep track of its execution, in order to cluster all user actions associated with the routine itself in well bounded routine traces
Interactive Segmentation of User Interface Logs
No full textRobotic Process Automation (RPA) is an emerging technology that relies on software (SW) robots to automate intensive and repetitive tasks (i.e., routines) performed by human users on the application’s User Interface (UI) of their computer systems. RPA tools are able to capture in dedicated UI logs the execution of many routines of interest. A UI log consists of user actions that are mixed in some order that reflects the particular order of their execution by the user, thus potentially belonging to different routines. In the RPA literature, the challenge to understand which user actions contribute to which routines and cluster them into well-bounded routine traces is known as segmentation. In this paper, we present a novel approach to the discovery of routine traces from unsegmented UI logs, which relies on: (i) a frequent-pattern identification technique to automatically derive the routine behaviors (a.k.a. routine segments) as recorded into a UI log, (ii) a human-in-the-loop interaction to filter out those segments not allowed (i.e., wrongly discovered from the UI log) by any real-world routine under analysis, and (iii) a trace alignment technique to cluster all those user actions belonging to a specific segment into routine traces. We evaluate our approach showing its effectiveness in terms of supported segmentation variants
SmartRPA: A Tool to Reactively Synthesize Software Robots from User Interface Logs
Get PDFRobotic Process Automation (RPA) is an emerging technology that automates intensive routine tasks (or simply routines) previously performed by a human user on the User Interface (UI) of a computer system, by means of a software (SW) robot. To date, RPA tools available in the market strongly relies on the ability of human experts to manually implement the routines to automate. Being the current practice time-consuming and error-prone, in this paper we present SmartRPA, a cross-platform software tool that tackles such issues by exploiting UI logs keeping track of many routine executions to generate executable RPA scripts that automate the routines enactment by SW robots
Verifying Petri Net-Based Process Models using Automated Planning
No full textThe identification of control-flow errors in business processes (BPs) at design time is one of the most relevant tasks of the early phases of the Business Process Management (BPM) lifecycle, named BP verification. In this paper, starting from a Petri net-based representation of a BP model, we show how instances of the verification problem can be represented as planning problems in PDDL (Planning Domain Definition Language) for which planners can find a correct solution in a finite amount of time. If verification problems are converted into planning problems, one can seamlessly leverage the best performing automated planners, with advantages in terms of versatility and customization
A User Evaluation of Process Discovery Algorithms in a Software Engineering Company
Get PDFProcess mining methods allow analysts to use logs of historical executions of business processes in order to gain knowledge about the actual behavior of these processes. One of the most widely studied process mining operations is automated process discovery. An event log is taken as input by an automated process discovery method and produces a business process model as output that captures the control-flow relations between tasks that are described by the event log. In this setting, this paper provides a systematic comparative evaluation of existing implementations of automated process discovery methods with domain experts by using a real-life event log extracted from an international software engineering company and four quality metrics: understandability, correctness, precision, and usefulness. The evaluation results highlight gaps and unexplored trade-offs in the field and allow researchers to improve the lacks in the automated process discovery methods in terms of usability of process discovery techniques in industry
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Supporting Governance in Healthcare Through Process Mining: A Case Study
Get PDFHealthcare organizations are under increasing pressure to improve productivity, gain competitive
advantage and reduce costs. In many cases, despite management already gained some kind of qualitative
intuition about inefciencies and possible bottlenecks related to the enactment of patients' careows, it does
not have the right tools to extract knowledge from available data and make decisions based on a quantitative
analysis. To tackle this issue, starting from a real case study conducted in San Carlo di Nancy hospital in
Rome (Italy), this article presents the results of a process mining project in the healthcare domain. Process mining techniques are here used to infer meaningful knowledge about the patient careflows from raw event logs consisting of clinical data stored by the hospital information systems. These event logs are analyzed using the ProM framework from three different perspectives: the control flow perspective, the organizational perspective and the performance perspective. The results on the proposed case study show that process mining provided useful insights for the governance of the hospital. In particular, we were able to provide answers to the management of the hospital concerning the value of last investments, and the temporal distribution of abandonments from emergency room and exams without reservation
Research Challenges for Intelligent Robotic Process Automation
Get PDFRobotic Process Automation (RPA) is a fast-emerging automation technology in the field of Artificial Intelligence that allows organizations to automate high volume routines. RPA tools are able to capture the execution of such routines previously performed by a human user on the interface of a computer system, and then emulate their enactment in place of the user. In this paper, after an in-depth experimentation of the RPA tools available in the market, we developed a classification framework to categorize them on the basis of some key dimensions. Then, starting from this analysis, we derived four research challenges necessary to inject intelligence into current RPA technology
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