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A Method to Improve the Early Stages of the Robotic Process Automation Lifecycle
Abstract
The robotic automation of processes is of much interest to organizations. A common use case is to automate the repetitive manual tasks (or processes) that are currently done by back-office staff through some information system (IS). The lifecycle of any Robotic Process Automation (RPA) project starts with the analysis of the process to automate. This is a very time-consuming phase, which in practical settings often relies on the study of process documentation. Such documentation is typically incomplete or inaccurate, e.g., some documented cases never occur, occurring cases are not documented, or documented cases differ from reality. To deploy robots in a production environment that are designed on such a shaky basis entails a high risk. This paper describes and evaluates a new proposal for the early stages of an RPA project: the analysis of a process and its subsequent design. The idea is to leverage the knowledge of back-office staff, which starts by monitoring them in a non-invasive manner. This is done through a screen-mouse-key-logger, i.e., a sequence of images, mouse actions, and key actions are stored along with their timestamps. The log which is obtained in this way is transformed into a UI log through image-analysis techniques (e.g., fingerprinting or OCR) and then transformed into a process model by the use of process discovery algorithms. We evaluated this method for two real-life, industrial cases. The evaluation shows clear and substantial benefits in terms of accuracy and speed. This paper presents the method, along with a number of limitations that need to be addressed such that it can be applied in wider contexts.
... step 1 in Fig. 1) [25], and (2) a method to analyze such logs to discover the underlying process model (cf. step 2 in Fig. 1) [15]. This paper significantly extends these contributions by: (1) proposing a novel approach to systematically analyze the screen captures to extract information which is, then, incorporated into the UI Logs (cf. ...
... In addition, other approaches have been proposed in academia, taking a further step in how to automate certain stages of robotization [3,11,20,24,25]. It should be noted that there are different formats proposed for capturing events, although the most representative for this work is the UI Log from [15] which defines it as an extension of the XES format-standard for event logs in Process Mining-which incorporate attributes like the app name (i.e., the name of the app), event type (i.e., mouse click or keystroke), click type (i.e., left, right, or middle), click coords (i.e., position of the mouse on the screen), the keystroke (i.e., the keys that are typed), and the screenshot (i.e., the screen capture associated to this event path). ...
... Using a UI Log, many proposals exist for process discovery, i.e., to automatically or semi-automatically discover the underlying process model that is associated with human behavior [5,6,13,15,19,23]. Moreover, these proposals include functionalities to clean the UI Log from irrelevant information, so that noise in the resulting process model is filtered; and to select variants/cases/activities according to the frequency, length, and other criteria that are useful to identify process candidates to robotize. ...
Robotic Process Automation (RPA) provides a means to automate mundane and repetitive human tasks. Task Mining approaches can be used to discover the actions that humans take to carry out a particular task. A weakness of such approaches, however, is that they cannot deal well with humans who carry out the same task differently for different cases according to some hidden rule. The logs that are used for Task Mining generally do not contain sufficient data to distinguish the exact drivers behind this variability. In this paper, we propose a new Task Mining framework that has been designed to support engineers who wish to apply RPA to a task that is subject to variable human actions. This framework extracts features from User Interface (UI) Logs that are extended with a new source of data, namely screen captures. The framework invokes Supervised Machine Learning algorithms to generate decision models, which characterize the decisions behind variable human actions in a machine-and-human-readable form. We evaluated the proposed Task Mining framework with a set of synthetic UI Logs. Despite the use of only relatively small logs, our results demonstrate that a high accuracy is generally achieved.KeywordsRobotic Process AutomationProcess discoveryTask miningDecision model discovery
... Previous literature shows that there are several benefits associated with implementing RPA, including time savings, error reduction and cost savings [20][21][22][23][24]. For example, the cost of RPA software licenses range between 1/3 and 1/5 of Full Time Equivalent (FTE) costs [24][25][26]. ...
... In some cases, Return on Investment (ROI) from RPA projects can be up to 200% within one year [28]. Time savings or reduced time is another benefit of RPA [20][21][22]29]. Although some studies report that a human employee can complete tasks faster than the bots [30], RPA implementation usually increases the process speed [16,20,24,27,28,31,32]. ...
... Many data intensive processes (i.e., finance, accounting, insurance, healthcare, and telecommunication applications) are prone to human errors. RPA eliminates human errors and improves the accuracy and quality of the processes [6,21,22,24,36]. For example, a case study [16] showed that a task performed by an RPA bot was free of errors. ...
The public sector has increased its use of robotic process automation (RPA) in administration, decision making and citizen services. Available studies mostly focused on the specific cases of using RPA in public organizations. Thus, we lack the helicopter view of the adoption of RPA in a country. In this paper, we present the results of a national survey of RPA adoption in the public sector in Sweden. The results show that the awareness of RPA is high in the Swedish public sector although the level of adoption is still modest. Also, there are notable differences in the level of adoption between central and local government. The study goes beyond the limitations of case studies, and contribute new knowledge of RRA adoption, benefits, routine capability and governance on a national level. The knowledge and insights can serve as a reference for other countries and public administrative models.KeywordsRobotic process automationPublic sectorInformation technology adoptionSurveyRoutine capabilityBenefit
... UI logs can be used to automate tasks and entire processes by having bots emulate the recorded user interactions. This approach to automation is called robotic process automation (RPA) [22] and has lately received considerable attention in research and practice. Within RPA, UBM techniques can be used to derive automation scripts, but also for robotic process mining [26], which for example encompasses the identification of suitable tasks for automation from UI logs [24]. ...
... Papers were considered as relevant if (1) they contained a concrete UI log or (2) they described the UI log collection process in enough detail to infer the captured attributes. [2, 3, 7, 35] log AND "user interface" log AND "task mining" [26] log AND "desktop activity mining" log AND "robotic process automation" [5,22] log AND "robotic process mining" ...
... 2. The atomic target UI element, on which the user action is executed. This attribute is recorded in most UI logs, except for two: Dev et al. [12] only record the usage of specific functions, such as crop in a graphics editor, and Jimenez-Ramirez et al. [8,22] record click coordinates and screenshots, but only use them to match similar user actions and do not map them to target elements. ...
User interaction (UI) logs are high-resolution event logs that record low-level activities performed by a user during the execution of a task in an information system. Each event in a UI log corresponds to a single interaction between the user and the interface, such as clicking a button or entering a string into a text field. UI logs are used for purposes like task mining or robotic process automation (RPA), but each study and tool relies on a different conceptualization and implementation of the elements and attributes that constitute user interactions. This lack of standardization makes it difficult to integrate UI logs from different sources and to combine tools for UI data collection with downstream analytics or automation solutions. To address this, we propose a universally applicable reference data model for process-related UI logs. Based on a review of scientific literature and industry solutions, this model includes the core attributes of UI logs, but remains flexible with regard to the scope, level of abstraction, and case notion. We provide an implementation of the model as an extension to the XES interchange standard for event logs and demonstrate its practical applicability in a real-life RPA scenario.
... In order to implement a successful RPA project and minimize the number of errors after deploying software robots, it is essential to understand the details of a process at a UI level since these are the steps that will be automated and executed using software robots. Several papers, discussed in the next section, focus on proposing new techniques to collect more logs at the user interaction level of an application then applying PM algorithms on the collected logs to generate process maps and discover the process steps (Linn et al., 2018;Jimenez-Ramirez et al., 2019;Leno et al., 2019). ...
... This framework is flexible in terms of input and output formats, and it allows the implementation of custom PM algorithms. A ProM plug-in was used to generate process maps using collected UI logs as an input (Jimenez-Ramirez et al., 2019). Cabello et al. (2020) also used ProM as their process mining tool to discover the as-is processes and the paths that can be automated, while the UiPath RPA tool was used to program the software robots that automate the tasks. ...
... The majority of the papers focused on collecting more event logs at the user interface level, which required using APIs to record the actions that were happening in Excel or Web browsers (Leno et al., 2019;Jimenez-Ramirez et al., 2019). ...
Purpose: Process mining aims to construct, from event logs, process maps that can help discover, automate, improve, and monitor organizational processes. Robotic process automation (RPA) uses software robots to perform some tasks usually executed by humans. It is usually difficult to determine what processes and steps to automate, especially with RPA. Process mining is seen as one way to address such difficulty. This paper aims to assess the applicability of process mining algorithms in accelerating and improving the implementation of RPA, along with the challenges encountered throughout project lifecycles. Methodology: A systematic literature review was conducted to examine the approaches where process mining techniques were used to understand the as-is processes that can be automated with software robots. Eight databases were used to identify papers on this topic. Findings: A total of 19 papers, all published since 2018, were selected from 158 unique candidate papers and then analyzed. There is an increase in the number of publications in this domain. Originality: The literature currently lacks a systematic review that covers the intersection of process mining and robotic process automation. The literature mainly focuses on the methods to record the events that occur at the level of user interactions with the application, and on the preprocessing methods that are needed to discover routines with the steps that can be automated. Several challenges are faced with preprocessing such event logs, and many lifecycle steps of automation project are weakly supported by existing approaches.
... Previous approaches have already identified and addressed the presence of humans within the RPA lifecycle. Some of them (Herm et al., 2020;Jimenez-Ramirez et al., 2019;Sigurðardóttir, 2018) enclose the human activity in the early stages of the identification of activities and processes to robotized, but they miss the later presence of humans during the process execution. In contrast, others (Ravindranath and Bhaskar, 2020;Mohanty and Vyas, 2018) acknowledge the collaboration between robots and humans mainly in data validation activities related to artificial intelligent contexts. ...
... Discovering such non-automatable activities in the early RPA stages has been recognized as a key factor for the success of the RPA development that may, eventually, run stably (Jimenez-Ramirez et al., 2019). In that way, part of the process keeps being executed by humans instead of robots. ...
... First, related to the information systems accessed in the hybrid process, their systems logs must be identified since they yield relevant information for the segmentation activities. As identified in existing literature (Agostinelli et al., 2020;Leno et al., 2020;Jimenez-Ramirez et al., 2019), the UI logs (i.e., logs acquired by monitoring the user interface of the humans while interacting with the information systems) may serve as an additional resource to help in the segmentation along with the systems logs. Second, the transfer points may generate useful traces to understand how humans interact with the new system. ...
After the initial hype on RPA, companies have more realistic expectations of this technology. Its current mature vision relegates the end-to-end robotic automation to a less suitable place and considers the human-robot collaboration as the most natural way for automating robotic processes in real-world settings. This hybrid RPA implies a vertical segmentation of process activities, i.e., some activities are conducted by humans while robots do others. The literature lacks a general method that considers the technical aspect of the solution, the psychological impact of the automation, and the governance mechanisms that a running hybrid process requires. In this sense, this paper proposes an iterative method dealing with all these aspects and results from a series of industrial experiences. Additionally, the paper deeply discusses the role of process mining in this kind of method and how it can continuously boost its iterations. The initial validation of the method in real-world processes reports substantial benefits in terms of efficiency.
... To tackle this gap, several research studies have proposed techniques to analyze User Interaction (UI) logs in order to discover repetitive routines that are amenable to automation via RPA [2,3,4,5,6]. However, existing approaches in this space make various assumptions that limit their applicability. ...
... Second, most of the existing approaches [2,3,4] discover frequent routines and/or automatable routines, but they do not produce an executable routine specification. ...
... Segmentation Candidates Automatable routines discovery discovery [1] Identifying candidate tasks for robotic process automation in textual process descriptions [2] A method to improve the early stages of the robotic process automation lifecycle [3] Discovering automatable routines from user interaction logs [4] Automated robotic process automation: A self-learning approach [5] Automated discovery of data transformations for robotic process automation [6] Automated generation of executable RPA scripts from user interface log [8] Identifying candidate routines for robotic process automation from unsegmented UI logs [11] Discovery of periodic patterns in spatiotemporal sequences [12] Time series chains: A new primitive for time series data mining [13] A framework for the evaluation of session reconstruction heuristics in web-usage analysis [14] Correlating unlabeled events from cyclic business processes execution [15] Discovering process models from unlabelled event logs [16] Automated segmentation of user interface logs using trace alignment techniques [17] Desktop activity mining -a new level of detail in mining business processes [18] Real-time detection of task switches of desktop users [19] Tasktracer: a desktop environment to support multi-tasking knowledge workers [20] Identifying frequent user tasks from application logs [21] Process mining and robotic process automation: A perfect match [22] Automating string processing in spreadsheets using input-output examples [23] The use of process mining in business process simulation model constructionstructuring the field Table 1: Summary of prior work related to the three phases of RPM ...
Robotic Process Automation (RPA) is a technology to automate routine work such as copying data across applications or filling in document templates using data from multiple applications. RPA tools allow organizations to automate a wide range of routines. However, identifying and scoping routines that can be automated using RPA tools is time consuming. Manual identification of candidate routines via interviews, walk-throughs, or job shadowing allow analysts to identify the most visible routines, but these methods are not suitable when it comes to identifying the long tail of routines in an organization. This article proposes an approach to discover automatable routines from logs of user interactions with IT systems and to synthetize executable specifications for such routines. The proposed approach focuses on discovering routines where a user transfers data from a set of fields (or cells) in an application, to another set of fields in the same or in a different application (data transfer routines). The approach starts by discovering frequent routines at a control-flow level (candidate routines). It then determines which of these candidate routines are automatable and it synthetizes an executable specification for each such routine. Finally, it identifies semantically equivalent routines so as to output a set of non-redundant routines. The article reports on an evaluation of the approach using a combination of synthetic and real-life logs. The evaluation results show that the approach can discover automatable routines that are known to be present in a UI log, and that it discovers routines that users recognize as such in real-life logs.
... The term Robotic Process Automation (RPA) refers to a software paradigm in which robots are programs that mimic the behavior of human workers interacting with information systems (IS) [3,13]. This paradigm has become increasingly popular because RPA is of great interest to organizations [5]. ...
... This fact leads to a strong dependency between the data scientist and the RPA developer role (i.e., professional in charge of designing and developing software robots) who must know the business process to automate it. 3 . Secondly, the performance of these components in a production environment depends on the data model performance, which tends to degrade over time [6]. ...
... 2 Completed Thanks to the tracking and exploitation panel, graphs and statistics can be presented that are easy to understand for business experts. 3 Completed ...
Robotic Process Automation (RPA) has quickly evolved from automating simple rule-based tasks. Nowadays, RPA is required to mimic more sophisticated human tasks, thus implying its combination with Artificial Intelligence (AI) technology, i.e., the so-called intelligent RPA. Putting together RPA with AI leads to a challenging scenario since (1) it involves professionals from both fields who typically have different skills and backgrounds, and (2) AI models tend to degrade over time which affects the performance of the overall solution. This paper describes the AIRPA project, which addresses these challenges by proposing a software architecture that enables (1) the abstraction of the robot development from the AI development and (2) the monitor, control, and maintain intelligent RPA developments to ensure its quality and performance over time. The project has been conducted in the Servinform context, a Spanish consultancy firm, and the proposed prototype has been validated with reality settings. The initial experiences yield promising results in reducing AHT (Average Handle Time) in processes where AIRPA deployed cognitive robots, which encourages exploring the support of intelligent RPA development.
... The steps to introduce robotic process automation are reflected in the RPA lifecycle introduced by Jimenez-Ramirez et al. [16], which is given in Fig. 1. ...
... After the selection of a suitable process, the design phase involves creating a visual RPA process model that defines the RPA agent's relevant activities, structure, and data flow [16]. These models also enable communication and exchange about the behavior that the RPA bot should exhibit. ...
... Rather than testing robots only in their execution environment [16], the proposed integration enables initial verifications of RPA applications already at design-time using an existing formal property for decisions in business processes, decision soundness [5]. Based on decisions defined in DMN decision tables, asserting decision soundness increases the quality of RPA bots. ...
Robotic Process Automation promises to release employees from repetitive and monotonous work, providing space for creative and innovative tasks. RPA tools provide a wide range of techniques to automate user interactions, including filling forms and copying values between applications. While it is accepted that decisions play an important role in business processes, they are not a first-class citizen in RPA. This paper proposes a framework and a software architecture that integrates decision management into RPA. The work is evaluated by a prototype that introduces Decision Model and Notation (DMN) capabilities to the RPA software tool UiPath by utilizing Camunda’s decision engine.
... To this end, interactions with the user interface are recorded in what is known as a UI Log (i.e., a series of mouse and keyboard events). These methods have become very convenient in helping analysts to identify candidate processes to be robotized, their different variants and their decision points efficiently (Jimenez-Ramirez et al., 2019). ...
... Fig. 1 a, where the same activity is shown with different properties, i.e., the first includes a checkbox selected while the checkbox is not selected in the second one) but not captured in the UI Log since there is not a mouse or keyboard event over it. In order to understand this human behavior, screen captures can be obtained along with each event in the UI Log (Jimenez-Ramirez et al., 2019). Then, features can be extracted to enrich the UI Log information (Martínez-Rojas et al., 2022) as additional columns for each event (cf. ...
... In fact, as reported in [16], in the early stages of the RPA life-cycle it is required the support of skilled human experts to: (i) identify the candidate routines to automate by means of interviews and observation of workers conducting their daily work, (ii) record the interactions that take place during routines' enactment on the UI of software applications into dedicated UI logs, and (iii) manually specify their conceptual and technical structure (often in form of flowchart diagrams) for defining the behavior of SW robots. ...
... In the field of RPA, segmentation is an issue still not so explored, since the current practice adopted by commercial RPA tools for identifying the routine steps often consists of detailed observations of workers conducting their daily work. Such observations are then "converted" in explicit flowchart diagrams [16], which are manually modeled by expert RPA analysts to depict all the potential behaviours (i.e., the traces) of a specific routine. In this setting, as the routine traces have been already (implicitly) identified, segmentation can be neglected. ...
Robotic Process Automation (RPA) is an emerging technology that allows organizations to automate intensive repetitive tasks (or simply routines) previously performed by a human user on the User Interface (UI) of web or desktop applications. RPA tools are able to capture in dedicated UI logs the execution of several routines and then emulate their enactment in place of the user by means of a software (SW) robot. A UI log can record information about many routines, whose actions are mixed in some order that reflects the particular order of their execution by the user, making their 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 paper, we leverage a concrete use case to explore the issue of segmentation of UI logs, identifying all its potential variants and presenting an up-to-date overview that discusses to what extent such variants are supported by existing literature approaches. Moreover, we offer points of reference for future research based on the findings of this paper.
... One of the problems that has been highlighted by researchers in this space is that of identifying which tasks to automate [15]. This assumes a simple dichotomy in the available agent types: human agents and robotic agents. ...
... Prior studies on RPA have focused on the design phase presenting techniques to identify candidate tasks for automation [15]. Studies have explored an increase in the scope of automation by the agents supported by Artificial Intelligence (AI) and Machine learning to do complex tasks [1,18]. ...
The design and implementation of Robotic process automation (RPA) requires an architecture where there is seamless coordination between humans, robotic agents, and intelligent agents automating information acquisition tasks and decision-making tasks. Effective coordination of agents would need to consider the efficiency of different types of resources in completing tasks, the quality when handling complex tasks, and the cost of resources executing the task. In this work, a novel approach for generating an optimal architecture considering distinct types of resources that include human, intelligent and robotic agents is proposed. An optimal architecture is the optimal enactment of process instances executed by a combination of human and automation agents based on their characteristics. The architecture considers resources, resource types, and their characteristics that meet multiple objectives of process execution.
... The problem of routines identification from UI logs in the context of RPM has attracted significant attention [7]. However, existing approaches for routines identification from UI logs either take as input a segmented UI log [3], [5], [6], [8], or they assume that the log can be trivially segmented by breaking it down at each point where one among a set of "start events" appears [4]. These start events, which act as delimiters between segments, need to be designated by the user. ...
... Also, they confirmed that the last three routine variants are alternative executions of the first routine variant. 4 While the results from the S1 log were positive, our approach could not discover any correct routine from the S2 log. By analyzing the results, we found out that the employee worked with multiple worksheets at the same time, frequently switching between them for visualization purposes only. ...
Robotic Process Automation (RPA) is a technology to develop software bots that automate repetitive sequences of interactions between users and software applications (a.k.a. routines). To take full advantage of this technology, organizations need to identify and to scope their routines. This is a challenging endeavor in large organizations, as routines are usually not concentrated in a handful of processes, but rather scattered across the process landscape. Accordingly, the identification of routines from User Interaction (UI) logs has received significant attention. Existing approaches to this problem assume that the UI log is segmented, meaning that it consists of traces of a task that is presupposed to contain one or more routines. However, a UI log usually takes the form of a single unsegmented sequence of events. This paper presents an approach to discover candidate routines from unsegmented UI logs in the presence of noise, i.e. events within or between routine instances that do not belong to any routine. The approach is implemented as an open-source tool and evaluated using synthetic and real-life UI logs.
... Similarly to many approaches related to the problem at large, existing approaches to event-case correlation in the RPA field often heavily rely on unique start and end events in order to segment the log, either explicitly or implicitly [27,40,26]. ...
Modern software systems are able to record vast amounts of user actions, stored for later analysis. One of the main types of such user interaction data is click data: the digital trace of the actions of a user through the graphical elements of an application, website or software. While readily available, click data is often missing a case notion: an attribute linking events from user interactions to a specific process instance in the software. In this paper, we propose a neural network-based technique to determine a case notion for click data, thus enabling process mining and other process analysis techniques on user interaction data. We describe our method, show its scalability to datasets of large dimensions, and we validate its efficacy through a user study based on the segmented event log resulting from interaction data of a mobility sharing company. Interviews with domain experts in the company demonstrate that the case notion obtained by our method can lead to actionable process insights.
... Nowadays, Process Mining methods are already used in the early stages of the RPA lifecycle to support the human designer [25]. The selection of the process to be automated also takes place in these phases [15], [29]. ...
Robotic Process Automation (RPA) is a frequently used approach for automation in IT systems. RPA uses existing graphical user interfaces to automate simple, rule-based tasks from a user’s perspective. Before automation can be accomplished using RPA, extensive knowledge about individual user interactions must be gathered. This information is represented in a flowchart by a human designer, which is then typically refined in a trial-and-error approach. This approach becomes time-consuming and error-prone as processes become more complex.In parallel, Process Discovery techniques as part of Process Mining enable the automated generation of process models from event logs. Thus, they are considered appropriate to simplify and automate the described creation of flowcharts in the context of RPA. In this regard, the so-called UI logs are particularly relevant. These can be used to record user interactions with various user interfaces within a process.This paper examines existing Process Discovery methods for application in the context of RPA. The goal is to clarify what is needed for the automatic creation of RPA flowcharts with Process Discovery on the basis of UI logs.The results show that existing Process Discovery methods generate process models that are not suitable for immediate translation into RPA flowcharts, as they are mainly control flow oriented. For the creation of RPA flowcharts that are suitable for automation, the integration of linked data is fundamental. Therefore, a prototypical implementation demonstrates a method that enables the automated translation of a process model into an RPA flowchart.KeywordsRPA supportRPA flowchart generationProcess Discovery
... According to [98,99], the implementation of RPA must follow a certain life cycle for correct operation and the minimisation of risks. This RPA life cycle has the following stages: (1) Process discovery, consisting of studying the documentation of the process and determining whether or not it is a candidate for automation. ...
Robotic Process Automation (RPA) is a source of growing applications in a number of industries both as an individual technology and as a complement to other technologies (such as Internet of Things (IoT)). RPA allows the automation of human activities on a computer, especially when these activities are repetitive and high in volume. RPA saves man-hours and increases the productive capacity of the processes. The application of RPA in civil engineering is still in its early stages, and there has been little work on the subject in the literature. This paper presents RPA technology, for the first time in the literature, as a long-term management, control, and auto fault correction process for a low-cost accelerometer that can be used in SHM applications. However, this process requires a significant number of man-hours to stay operational, given the architecture of its applications. With the application of an RPA implementation workflow formulated based on the Design Science Research Method (DSRM), the management and control of the data acquisition process of a low-cost accelerometer located on a structural column are automated and put into operation in this study. RPA also made it possible to automatically detect and notify users of errors in the process, restart the process, and bring the process back online every time errors occurred. In this way, an automated process was obtained that operated continually and freed up human labour.
... The previous sections considered the identification of automation candidates via natural language analysis of the labels and descriptions of processes and their activities. This perspective is complementary to existing works that consider the identification of automation candidates from other angles, such as approaches that aim to identify candidates based on recorded event sequences, represented in the form of event logs, which capture the execution of business processes [23], or as interaction logs, which capture the way in which users employ an information system to perform their tasks [4,9]. These approaches cover a variety of aspects to identify suitable candidates in terms of automation value and fit. ...
The general goal of automation is to relieve humans from repetitive and routine-like tasks. The positive effects of automation have been demonstrated in various contexts and range from efficiency gains to the reduction of errors. In this chapter, we focus on the automation of individual tasks in a process using the so-called software robots, which is often referred to as Robotic Process Automation (RPA). More specifically, we focus on the task of identifying suitable candidates for such automation efforts. In practice, this identification task is associated with substantial manual effort and, hence, is both time- and cost-intensive. Recognizing these issues, we consider how also the identification of automation candidates itself can be supported through automation. We particularly focus on the way in which Natural Language Processing (NLP) may be employed for this purpose. We show how NLP techniques support the identification of automation candidates in widely used process representations, such as process models and textual process descriptions. As such, we demonstrate how tackling one of the key impediments to the adoption of RPA may be supported in an algorithmic and automated manner.
... identified by doi: 10.1007/978-3-030-91431-8_5 convert such observations in explicit flowchart diagrams, which are specified to depict all the potential behaviors (i.e., segments) of the routines of interest, and (iii) finally implement the SW robots that automate the routines enactment on a target computer system. However, the current practice is time-consuming and error-prone, as it strongly relies on the ability of human experts to correctly interpret the routines to automate [14]. Consequently, if SW robots are not designed for the appropriate scope of their work, then their implementation cost will increase while no clear business improvement effect will be achieved [13]. ...
Robotic 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.
... While this approach has proven to be effective to execute rule-based and wellstructured routines [5], it becomes time-consuming and error-prone in presence of routines that are less deterministic and require decisions [7]. In this paper, we tackle the above issue by presenting SmartRPA, an opensource software tool that is able to reason over the UI logs keeping track of many routine executions (cf. ...
Robotic 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.
... Ortiz et al. [5], concluded that up to 80% of time reduction is achieved in repetitive tasks with help of RPA. Jimenez-Ramirez et al. [11], conducted case study to observe effects of RPA on business. For this study Jimenez-Ramirez et al. classified employees in two groups one with RPA and other without RPA. ...
With recent trends of digitization, many corporations are focusing on automation to digitize their non digital information. Robotic process automation, or software robot technology is gaining a lot of attention from corporates for its capability of efficient automation and scalability. Software bots are faster, cheaper, and precise therefore can be utilized by these organizations easily. Software bots can process both structured and unstructured data, for modern and/or legacy systems irrespective of size of organization. There is a lot of research conducted on robotic process automation during recent years. This research proposes method to collect data more efficiently with very high accuracy. This proposed method utilizes the nature of RPA to collect data from source. This method will be beneficial to small scale organizations due to its very easy implementation and ease of exportation of data in required format.
... Ortiz et al. [5], concluded that up to 80% of time reduction is achieved in repetitive tasks with help of RPA. Jimenez-Ramirez et al. [11], conducted case study to observe effects of RPA on business. For this study Jimenez-Ramirez et al. classified employees in two groups one with RPA and other without RPA. ...
User interaction logs allow us to analyze the execution of tasks in a business process at a finer level of granularity than event logs extracted from enterprise systems. The fine-grained nature of user interaction logs open up a number of use cases. For example, by analyzing such logs, we can identify best practices for executing a given task in a process, or we can elicit differences in performance between workers or between teams. Furthermore, user interaction logs allow us to discover repetitive and automatable routines that occur during the execution of one or more tasks in a process. Along this line, this chapter introduces a family of techniques, called Robotic Process Mining (RPM), which allow us to discover repetitive routines that can be automated using robotic process automation technology. The chapter presents a structured landscape of concepts and techniques for RPM, including techniques for user interaction log preprocessing, techniques for discovering frequent routines, notions of routine automatability, as well as techniques for synthesizing executable routine specifications for robotic process automation.
Since half a decade, there has been an increasing interest in Robotic Process Automation (RPA) by business firms. However, academic literature has been lacking attention to RPA, before adopting the topic to a larger extent. The aim of this study is to review and structure the latest state of scholarly research on RPA. This chapter is based on a systematic literature review that is used as a basis to develop a conceptual framework to structure the field. Our study shows that some areas of RPA have been extensively examined by many authors, e.g. potential benefits of RPA. Other categories, such as empirical studies on adoption of RPA or organisational readiness models, have remained research gaps.
Robotic Process Automation (RPA) performs high-volume tasks such as checking invoices. However, the governance and maintenance of large-scale software robot environments can be challenging when robot servers perform automatized tasks simultaneously for customer organizations with complex programming rules, dedicated parameters, and dependencies on timetables. A multivocal literature review (MLR) was conducted to explore whether there are 1) mechanisms to improve software robot maintenance in large-scale robot environments, 2) or software robot maintenance practices for scalable RPA in organizations providing shared services, 3) or governance models for optimizing the performance of software robot maintenance, and 4) is the Center of Excellence (CoE) one of the success factors concerning large-scale robot environments. By doing this, 5) we found eleven functional requirements for the monitoring tool to support maintenance in a large-scale environment. In addition, we adapted them to the RPA monitoring tool abilities for the Finnish Government Shared Services Centre for Finance and HR (Palkeet). As a result, the eleven functional requirements and the monitoring tool abilities are adaptable for other large-scale environments to improve software robot maintenance. However, commercial monitoring tools for RPA maintenance do not fulfil functional requirements, and organizations in large-scale environments must develop their monitoring tools. Based on MLR, either in-house or outsourced CoE seems to be one of the success factors in RPA maintenance in large-scale environments.
Nowadays, with increasing globalization, companies have to be prepared to adapt and respond to the challenges raised by the 4th Industrial Revolution. The adoption of new technologies has been the most used solution, creating new challenges concerning worker-machine interaction. The required skills of workers tend to change, as do their tasks, which, many times, do not add value, contribute to talent waste, the eighth waste of Lean, and generate dissatisfaction. This paper reviews how this problem can be eliminated, by implementing Robotic Process Automation in a company that provides residential hot water solutions. This technology automates repetitive processes. The results obtained show that, through Robotic Process Automation, it is possible to automate the tasks that do not add value and contribute to eighth lean waste.
User interaction (UI) logs are high-resolution event logs that record low-level activities performed by a user during the execution of a task in an information system. Each event in a UI log corresponds to a single interaction between the user and the interface, such as clicking a button or entering a string into a text field. UI logs are used for purposes like task mining or robotic process automation (RPA), but each study and tool relies on a different conceptualization and implementation of the elements and attributes that constitute user interactions. This lack of standardization makes it difficult to integrate UI logs from different sources and to combine tools for UI data collection with downstream analytics or automation solutions. To address this, we propose a universally applicable reference data model for process-related UI logs. Based on a review of scientific literature and industry solutions, this model includes the core attributes of UI logs, but remains flexible with regard to the scope, level of abstraction, and case notion. We provide an implementation of the model as an extension to the XES interchange standard for event logs and demonstrate its practical applicability in a real-life RPA scenario.KeywordsUser behavior miningUI LogData modelRobotic process automationTask mining
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.KeywordsRobotic Process AutomationProcess miningUser Interface (UI) logsReference data model for UI logsSegmentationAutomated generation of SW robots from UI LogsSmartRPA
Robotic 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 (a.k.a. routines) previously performed by human users in their applications’ user interfaces (UIs). Nowadays, successful usage of RPA requires strong support by skilled human experts, from the discovery of the routines to be automated (i.e., the so-called segmentation issue of UI logs) to the development of the executable scripts required to enact SW robots. In this paper, we present a human-in-the-loop approach to filter out the routine behaviors (a.k.a. routine segments) not allowed (i.e., wrongly discovered from the UI log) by any real-world routine under analysis, thus supporting human experts in the identification of valid routine segments. We have also measured to which extent the human-in-the-loop strategy satisfies three relevant non-functional requirements, namely effectiveness, robustness and usability.KeywordsRobotic Process AutomationSegmentation of UI logsDeclarative constraints
Robotic process automation (RPA) is a technology that is presented as a universal tool that solves major problems of modern businesses. It aims to reduce costs, improve quality and create customer value. However, the business reality differs from this aspiration. After interviews with managers, we found that implementation of robots does not always lead to the assumed effect and some robots are subsequently withdrawn from companies. In consequence, people take over robotized tasks to perform them manually again, and in practice, replace back robots—what we call ‘re-manualization’. Unfortunately, companies do not seem to be aware of this possibility until they experience it on their own, to the best of our knowledge, no previous research described or analysed this phenomenon so far. This lack of awareness, however, may pose risks and even be harmful for organizations. In this paper, we present an exploratory study. We used individual interviews, group discussions with managers experienced in RPA, and secondary data analysis to elaborate on the re-manualization phenomenon. As a result, we found four types of ‘cause and effect’ narrations that reflect reasons for this to occur: (1) overenthusiasm for RPA, (2) low awareness and fear of robots, (3) legal or supply change and (4) code faults.KeywordsRobotic process automationRPASoftware robotInvestmentInformation systemsWork manualization
The main aim of this paper is to present the results of a process-project maturity assessment of large organizations in Poland. The paper consists of two main parts: a theoretical part, which primarily outlines the rationale supporting the prospects and the need for an orientation towards process and project organizations, and an empirical part, presenting an attempt to integrate the MMPM and PMMM maturity models, in order to assess organizational level of process-project maturity. The empirical research carried out on a sample of 90 large organizations shows that vast majority of the organizations surveyed are characterized by low levels of process and project maturity, and 13 of the entities examined can be described, based on the assumptions adopted, as a process-project organization (level 4 of process-project maturity). Further, the research conducted has led to an outline of the factors supporting the recognition of process management as a method fundamental to the designing a process-project organization. Maturity model integration has demonstrated the levels of process and project maturity as well as a statistically positive correlation between the degree of process maturity and project maturity. The original character of this paper primarily concerns the need to fill the literature gap, consisting in the scarcity of publications describing integration of process and project management methods and the deficit of works presenting process-project maturity results.KeywordsProcess-project oriented organizationBPMProcess managementProject managementMaturity
Purpose
This study intends to find the industries that have leveraged Robotic Process Automation (RPA) technology and elucidate the extent of the adoption of RPA in various industry domains with benefits. The identification of tasks eligible for RPA itself is a challenge. Therefore, the study further brings out the challenges faced in various industry verticals and postulates the future direction of research and applications in RPA.
Design/methodology/approach
The study focuses on articles from popular databases such as SCOPUS, Web of Science and Google scholar. PRISMA methodology is used for systematic literature review and 113 papers are shortlisted for study. Three questions are framed to carry out the review and set the research direction.
Findings
It is evident from this study that RPA has been widely used in banking and related areas with moderate use in healthcare and manufacturing leading to operational efficiency and productivity. However, there are a lot more opportunities in other domains that need to be taped by leveraging technology advancements and a research agenda has been devised by postulating future directions.
Originality/value
The study brings out a new comprehensive perspective as regards RPA implementation across domains. There is no promising study found that gathers three-dimensional aspects of the meta-themes applications, benefits and challenges. The study summarizes the research agenda and projects the industry domains that have not yet explored, the benefits of RPA. This will be a good reference article for those who develop RPA techniques and organizations that have plans to go for RPA.
Robotic Process Automation (RPA) is an emerging technology in the field of Business Process Management (BPM) that enables the automation of intensive repetitive tasks (or simply routines). RPA solutions access the user interface (UI) layer of software (SW) applications and provide a virtual workforce of SW robots that are able to mimic human keyboard and mouse interactions with a UI as if a real person was doing them. To take full advantage of this technology, organizations leverage the support of skilled human experts that preliminarily observe how routines are executed on the UI of the involved SW applications, and then implement the executable RPA scripts required to automate the routines enactment by SW robots on a target computer system. However, the current practice is time-consuming and error-prone, as it strongly relies on the ability of the human experts to correctly interpret the routines (and their variants) to automate. In this paper, to tackle this issue, we use a design science research method to develop an approach, called SmartRPA, which is able to interpret the UI logs keeping track of many routine executions, and to automatically synthesize SW robots that emulate the most suitable routine variant for any specific intermediate user input that is required during the routine execution. The approach is implemented as an open-source tool and evaluated with four non-functional requirements employing both syntectic and real-world data.
Among the many sources of event data available today, a prominent one is user interaction data. User activity may be recorded during the use of an application or website, resulting in a type of user interaction data often called click data. An obstacle to the analysis of click data using process mining is the lack of a case identifier in the data. In this paper, we show a case and user study for event-case correlation on click data, in the context of user interaction events from a mobility sharing company. To reconstruct the case notion of the process, we apply a novel method to aggregate user interaction data in separate user sessions—interpreted as cases—based on neural networks. To validate our findings, we qualitatively discuss the impact of process mining analyses on the resulting well-formed event log through interviews with process experts.
Robotic Process Automation (RPA) is a comparably new phenomenon in process digitalization and automation. Prior research has identified a clear need to analyze Critical Success Factors (CSF) for RPA. In this study, we set out to derive a corresponding framework. Based on a structured review of the literature and an analysis of 19 expert interviews, we identify 32 CSFs which we subsume in several contextual clusters. Building on prior literature on CSF, we critically discuss how far the success factors we found are RPA-specific or hold for other process automation technologies or process improvement efforts in general, too. Based on this, we highlight implications for both theory and practice and areas for future research.
Robotic process automation (RPA) is considered as a significant aspect of modernizing and digitally transforming public administration towards a higher degree of automation. By adding cognitive artificial intelligence, the use of RPA can be extended, from rule-based, routine processes to more complex applications, involving semi- and unstructured information. However, we lack a clear understanding of what is meant by cognitive RPA and the impacts of RPA on public organizations’ dynamic IT capabilities. To fill this knowledge gap, we carried out a qualitative study by conducting 13 interviews with RPA system suppliers., An abductive approach was used in analyzing the interview data. We contribute with a definition and a conceptual system model of cognitive RPA and a set of propositions for how an extended notion of RPA affects dynamic IT capabilities in public sector organizations.
In this study, we propose a governance framework for two-speed innovation as a fruitful approach to manage digitalisation and change in incumbent firms and public organisations. Two-speed innovation is a process that combines ambidexterity and platform-oriented IT architecture, through a particular configuration; a two-speed organisation, supported by two-speed digital technology. We structure the governance framework on dynamic capabilities and use it to investigate two-speed innovation in three large organisations. We describe how the two-speed dynamics unfold and discuss options for managing the process.
Robotic Process Automation (RPA) adoption is increasing in the public sector for improving the quality and the efficiency of public services. However, we have not yet gained a sufficient understanding of how RPA advances public service practices and process routines in public organizations. To mitigate this gap, we conducted a literature review and analyzed eight reported cases of RPA in public sector organizations through the lens of technology as routine capability (Swanson 2019). The results indicate that most of the cases are from the public sector in the Nordic countries, e.g., Sweden, Norway, and Finland. RPA creates new “machine” routines and becomes integral to humans’ new routines in public service’s processes and practices. RPA as routine capability advanced practices at individual, organizational, and social levels. The evidence also indicated that changes triggered by RPA were intertwined in the four modes of routine capability: design, execution, diffusion, and shift. The research contributed to a deeper understanding of how RPA changes and cultivates routine capability and advances public service practices. In addition, we applied and critically examined technology as routine capability as the analytical framework for understanding how RPA advanced public service practices.
Robotic Process Automation (RPA) deals with the automation of rule-based process tasks to increase process efficiency and to reduce process costs. Due to the utmost importance of business process automation in industry, RPA attracts increasing attention in the scientific field as well. This paper presents the state-of-the-art in the RPA field by means of aSystematic Mapping Study (SMS). In this SMS, 63 publications are identified and analysed. From the SMS findings, aframework for systematically analysing, and comparing RPA works is derived. The discovered thematic clusters suggest further investigations to develop amore elaborated structural research approach for RPA.
Automating business processes is one of the most recurrent topics in industries, independent of its digital orientation. Competitiveness pushes companies to deliver their products or services efficiently and effectively.
Zusammenfassung
Aufgrund neuer Marktanforderungen müssen Organisationen ihr Wertversprechen ständig anpassen. Durch die zunehmende Digitalisierung sind die Organisationen immer mehr befähigt, sich dieser Herausforderung zu stellen. Auch die Deutsche Rentenversicherung befindet sich mit 59.000 Mitarbeiter*innen, die rund 56 Mio. Versicherte und fast 21 Mio. Rentner im In- und Ausland betreuen, im ständigen Wandel. Ein Indikator dafür ist die Einführung von innovativen Dienstleistungen, die auf die digitale Strategie der Organisation ausgerichtet sind. Einen wichtigen Baustein für die Digitalisierungsstrategie der Deutsche Rentenversicherung Bund stellt Robotic Process Automation dar, mit der sich die Deutsche Rentenversicherung erhebliche Vorteile in der Serviceerbringung durch Prozessautomatisierung und Fehlerreduktion erhofft. In der Literatur und Praxis besteht ein zunehmendes Interesse an Robotic Process Automation, jedoch sind die Beiträge im Kontext des öffentlichen Sektors eher knapp. Dieser Artikel berichtet über die Umsetzung von einem Robotic Process Automation-basierten Transformationsprozess der Dienstleistungen bei der Deutsche Rentenversicherung. Konkret werden die ersten Ergebnisse aus der Transformation von vier Dienstleistungen beschrieben, die aus einer strukturierten Analyse von 26 Use Cases mit den serviceerbringenden Abteilungen resultiert haben.
Robotic Process Automation (RPA) is an emerging technology that enables the automation of well-defined and repetitive back office processes by providing a virtual workforce. Even though RPA draws much corporate attention in recent years, many RPA projects fail or lack behind expectations. A major reason is the automation of wrong processes, mainly driven by a lack of objective methods to identify and select suitable process candidates. The goal of this paper is to develop a generalizable method to detect, prioritize, and select process candidates for the automation with RPA. The paper follows the principles of Design Science Research and includes a literature review, expert interviews, and an extensive survey based on the Analytic Hierarchy Process approach with RPA developers, consultants, and end users. As a result, we present a three step approach and a quantifiable model to objectively prioritize suitable RPA process candidates based on suitability values. We empirically show that the most important criteria to select suitable RPA process candidates are a high degree of standardization and high volume.
With the recent developments in robotic process automation (RPA) and artificial intelligence (AI), academics and industrial practitioners are now pursuing robust and adaptive decision making (DM) in real-life engineering applications and automated business workflows and processes to accommodate context awareness, adaptation to environment and customisation. The emerging research via RPA, AI and soft computing offers sophisticated decision analysis methods, data-driven DM and scenario analysis with regard to the consideration of decision choices and provides benefits in numerous engineering applications. The emerging intelligent automation (IA) – the combination of RPA, AI and soft computing – can further transcend traditional DM to achieve unprecedented levels of operational efficiency, decision quality and system reliability. RPA allows an intelligent agent to eliminate operational errors and mimic manual routine decisions, including rule-based, well-structured and repetitive decisions involving enormous data, in a digital system, while AI has the cognitive capabilities to emulate the actions of human behaviour and process unstructured data via machine learning, natural language processing and image processing. Insights from IA drive new opportunities in providing automated DM processes, fault diagnosis, knowledge elicitation and solutions under complex decision environments with the presence of context-aware data, uncertainty and customer preferences. This sophisticated review attempts to deliver the relevant research directions and applications from the selected literature to the readers and address the key contributions of the selected literature, IA’s benefits, implementation considerations, challenges and potential IA applications to foster the relevant research development in the domain.
Zusammenfassung
Robotic Process Automation (RPA) bezeichnet eine Technologie, die die einfache Erstellung von Computerprogrammen (sogenannten Bots) zur Automatisierung von IT-gestützten Geschäftsprozessen über die graphische Benutzeroberfläche ermöglicht. Aktuelle Forschungsbemühungen im Themenfeld RPA haben gezeigt, dass der erfolgreiche Einsatz von RPA allem voran ein realistisches Erwartungsmanagement und eine ausgiebige Prozessaufnahme erfordert. Diese Resultate zeigen die Notwendigkeit von Bewertungsmethoden zur Bestimmung und Klassifizierung von Prozessen im Hinblick auf ihre Verwendbarkeit im Rahmen von RPA.
In der Arbeit werden Ergebnisse eines mehrstufigen Design-Science-Research-Projekts im Kontext eines mittelständischen Industrieunternehmens vorgestellt. Genutzt werden dafür strukturierte mehrstufige qualitative Expertenbefragungen mit dem Ziel der Modellbildung. In diesem Projekt wird demnach ein neues Bewertungsmodell zur Messung der Eignung für eine RPA-Implementierung und eine detailliertere Potenzialanalyse entwickelt (PEPA, Prozesseignung und -Priorisierung für Automatisierung). Das PEPA-Modell, von der Konzeptualisierung bis zur Implementierung, konzentriert sich auf seine verallgemeinerbare Anwendung und bietet ein systematisches Vorgehen zur Eignungsanalyse von Prozessen. Es berücksichtigt dabei wirtschaftliche, technologische und prozessuale Kriterien und ermöglicht eine anschließende Priorisierung der Prozesse in Bezug auf ihre Eignung für eine RPA-Implementierung. Damit geht das vorgeschlagene PEPA-Modell über bestehende Modelle aus der Praxis hinaus.
Robotic Process Automation (RPA) is an emerging technology for automating tasks using bots that can mimic human actions on computer systems. Most existing research focuses on the earlier phases of RPA implementations, e.g. the discovery of tasks that are suitable for automation. To detect exceptions and explore opportunities for bot and process redesign, historical data from RPA-enabled processes in the form of bot logs or process logs can be utilized. However, the isolated use of bot logs or process logs provides only limited insights and not a good understanding of an overall process. Therefore, we develop an approach that merges bot logs with process logs for process mining. A merged log enables an integrated view on the role and effects of bots in an RPA-enabled process. We first develop an integrated data model describing the structure and relation of bots and business processes. We then specify and instantiate a ‘bot log parser’ translating bot logs of three leading RPA vendors into the XES format. Further, we develop the ‘log merger’ functionality that merges bot logs with logs of the underlying business processes. We further introduce process mining measures allowing the analysis of a merged log.
Robotic process automation (RPA) provides a virtual workforce to automatize manual, repetitive and error-prone tasks. However, successful process automation requires knowledge about the potential for automation, effective training of the robots and continuous monitoring of their performance. Within this paper, we illustrate how process mining supports organizations throughout the lifecycle of RPA initiatives. Our process mining application provides a visual and fact-based proof for automation capabilities and enables prioritization of activities. The application further supports the user by monitoring and benchmarking robots to ensure sustainable benefits. These insights are especially valuable for process managers and miners with a special interest in process automation.
A learnable system allows a user to know how to perform correctly any task of the system after having executed it a few times in the past. In this paper, we propose an approach to measure the learnability of interactive systems during their daily use. We rely on recording in a user log the user actions that take place during a run of the system and on replaying them over the system interaction models, which describe the expected ways of executing system tasks. Our approach identifies deviations between the interaction models and the user log and assesses their weight through a fitness value. By measuring the rate of the fitness value for subsequent executions of the system we are able not only to understand if the system is learnable with respect to its tasks, but also to quantify its degree of learnability over time and to identify potential learning issues.
Robotic Process Automation (RPA) emerges as software based solution to automate rules-based business processes that involve routine tasks, structured data and deterministic outcomes. Recent studies report the benefits of the application of RPA in terms of productivity, costs, speed and error reduction. Most of these applications were carried out on back office business process where the customer is not directly involved, therefor a case study was conducted on a BPO provider to verify the benefits and results of applying RPA to a service business process with front and back office activities. The results show that productivity improvement is the main benefit of RPA, nevertheless time reduction was not achieved on this case.
OpusCapita Group is a Finnish company offering financial processes and outsourcing services to medium-sized companies and large corporations. OpusCapita particularly focuses on comprehensive Purchase-to-Pay and Order-to-Cash processes. In hopes to stay ahead of the curve in financial process automation, OpusCapita is betting on Robotic Process Automation (RPA). This teaching case presents challenges faced by Mr. Petri Karjalainen, Senior Vice President at OpusCapita Group, who is looking for ways to introduce RPA to the market, and provide added value to new and existing customers.
Online services are increasingly dependent on user participation. Whether it's online social networks or crowdsourcing services, understanding user behavior is important yet challenging. In this paper, we build an unsupervised system to capture dominating user behaviors from clickstream data (traces of users' click events), and visualize the detected behaviors in an intuitive manner. Our system identifies "clusters" of similar users by partitioning a similarity graph (nodes are users; edges are weighted by clickstream similarity). The partitioning process leverages iterative feature pruning to capture the natural hierarchy within user clusters and produce intuitive features for visualizing and understanding captured user behaviors. For evaluation, we present case studies on two large-scale clickstream traces (142 million events) from real social networks. Our system effectively identifies previously unknown behaviors, e.g., dormant users, hostile chatters. Also, our user study shows people can easily interpret identified behaviors using our visualization tool.
More and more IT organizations or IT outsourcing service providers are embracing Information Technology
Process Automation (ITPA) as it has transformed the way IT services are delivered to end users. As a result, one of the highly claimed benefits of ITPA is total cost reduction in IT operations. However, ITPA subjects to different interpretations on topics like ITPA criteria, use cases as well as its effects on organizations. Today, there is limited literature and conflicting views on such ITPA topics. The objective of this study is to shed some lights on ITPA criteria, use cases as well as the effects ITPA brings. The effects include the benefits as well as negative effects of ITPA. Moreover, some factors to guide organizations how to adopt and deploy ITPA are also discussed. This study has taken a qualitative approach in which ITPA literature was reviewed and some IT professionals were interviewed. Lastly, limitations of the study, future research and conclusion were also provided.
This article addresses the problem of constructing consolidated business process models out of collections of process models that share common fragments. The article considers the construction of unions of multiple models (called merged models) as well as intersections (called digests). Merged models are intended for analysts who wish to create a model that subsumes a collection of process models -- typically representing variants of the same underlying process -- with the aim of replacing the variants with the merged model. Digests, on the other hand, are intended for analysts who wish to identify the most recurring fragments across a collection of process models, so that they can focus their efforts on optimizing these fragments. The article presents an algorithm for computing merged models and an algorithm for extracting digests from a merged model. The merging and digest extraction algorithms have been implemented and tested against collections of process models taken from multiple application domains. The tests show that the merging algorithm produces compact models and scales up to process models containing hundreds of nodes. Furthermore, a case study conducted in a large insurance company has demonstrated the usefulness of the merging and digest extraction operators in a practical setting.
Effective information systems require the existence of explicit process models. A completely specified process design needs
to be developed in order to enact a given business process. This development is time consuming and often subjective and incomplete.
We propose a method that constructs the process model from process log data, by determining the relations between process
tasks. To predict these relations, we employ machine learning technique to induce rule sets. These rule sets are induced from
simulated process log data generated by varying process characteristics such as noise and log size. Tests reveal that the
induced rule sets have a high predictive accuracy on new data. The effects of noise and imbalance of execution priorities
during the discovery of the relations between process tasks are also discussed. Knowing the causal, exclusive, and parallel
relations, a process model expressed in the Petri net formalism can be built. We illustrate our approach with real world data
in a case study.
The case presents a series of dilemmas facing senior executives thinking through the potential application of robotic process automation (RPA) into a human resource (HR) function and global business service (GBS) operations. The executives are pointed to successful RPA implementation by business process service provider Xchanging, operating in the back office of the London insurance market. The teaching case focuses on what can be learned from that experience, and how their own RPA use may differ in HR and GBS contexts. The teaching case requires important decisions to be made about the business case for RPA and cognitive automation, the type of automation to be deployed, how to implement effectively in HR and GBS contexts, and whether to use RPA tactically or strategically, and if the latter, the implications of this decision. Students and practitioners will gain insight into the service automation landscape, RPA risks, challenges, and effective deployment, and will lean how to plan for (a) service automation strategy and building a mature automation capability, (b) mitigate the risks, and (c) progress launch, change management and detailed implementation in multiple business contexts.
In the light of continuous growth in log analytics, application logs remain a valuable source to understand and analyze patterns in user behavior. Today, almost every major software company employs analysts to reveal user insights from log data. To understand the tasks and challenges of the analysts, we conducted a background study with a group of analysts from a major software company. A fundamental analytics objective that we recognized through this study involves identifying frequent user tasks from application logs. More specifically, analysts are interested in identifying operation groups that represent meaningful tasks performed by many users inside applications. This is challenging, primarily because of the nature of modern application logs, which are long, noisy and consist of events from high-cardinality set. In this paper, we address these challenges to design a novel frequent pattern ranking technique that extracts frequent user tasks from application logs. Our experimental study shows that our proposed technique significantly outperforms state of the art for real-world data.
This is the second edition of Wil van der Aalst’s seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several additions and updates, e.g. on inductive mining techniques, the notion of alignments, a considerably expanded section on software tools and a completely new chapter of process mining in the large. It is self-contained, while at the same time covering the entire process-mining spectrum from process discovery to predictive analytics. After a general introduction to data science and process mining in Part I, Part II provides the basics of business process modeling and data mining necessary to understand the remainder of the book. Next, Part III focuses on process discovery as the most important process mining task, while Part IV moves beyond discovering the control flow of processes, highlighting conformance checking, and organizational and time perspectives. Part V offers a guide to successfully applying process mining in practice, including an introduction to the widely used open-source tool ProM and several commercial products. Lastly, Part VI takes a step back, reflecting on the material presented and the key open challenges. Overall, this book provides a comprehensive overview of the state of the art in process mining. It is intended for business process analysts, business consultants, process managers, graduate students, and BPM researchers.
Process-oriented data mining (process mining) uses algorithms and data (in the form of event logs) to construct models that aim to provide insights into organisational processes. The quality of the data (both form and content) presented to the modeling algorithms is critical to the success of the process mining exercise. Cleaning event logs to address quality issues prior to conducting a process mining analysis is a necessary, but generally tedious and ad hoc task. In this paper we describe a set of data quality issues, distilled from our experiences in conducting process mining analyses, commonly found in process mining event logs or encountered while preparing event logs from raw data sources. We show that patterns are used in a variety of domains as a means for describing commonly encountered problems and solutions. The main contributions of this article are in showing that a patterns-based approach is applicable to documenting commonly encountered event log quality issues, the formulation of a set of components for describing event log quality issues as patterns, and the description of a collection of 11 event log imperfection patterns distilled from our experiences in preparing event logs. We postulate that a systematic approach to using such a pattern repository to identify and repair event log quality issues benefits both the process of preparing an event log and the quality of the resulting event log. The relevance of the pattern-based approach is illustrated via application of the patterns in a case study and through an evaluation by researchers and practitioners in the field.
HCI researchers are increasingly collecting rich behavioral traces of user interactions with online systems in situ at a scale not previously possible. These logs can be used to characterize user interactions with existing systems and compare different designs. Large-scale log studies give rise to new challenges in experimental design, data collection and interpretation, and ethics. The chapter discusses how to address these challenges using search engine logs, but the methods are applicable to other types of log data.
We describe an algorithm for computing an image signature, suitable for first-stage screening for duplicate images. Our signature relies on the relative brightness of image regions, and is generally applicable to photographs, text documents, and line art. We give experimental results on the sensitivity and robustness of signatures for actual image collections, and also results on the robustness of signatures under transformations such as resizing, rescanning, and compression.
Desktop activity mining - a new level of detail in mining business processes
- C Linn
- P Zimmermann
- D Werth
Linn, C., Zimmermann, P., Werth, D.: Desktop activity mining -a new level of
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