Computing and Information Systems - Research Publications
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ItemPrescriptive process monitoring based on causal effect estimation(PERGAMON-ELSEVIER SCIENCE LTD, 2023-06)Prescriptive process monitoring methods seek to control the execution of a business process by triggering interventions, at runtime, to optimise one or more performance measure(s) such as cycle time or defect rate. Examples of interventions include, for example, using a premium shipping service to reduce cycle time in an order-to-cash process, or offering better loan conditions to increase the acceptance rate in a loan origination process. Each of these interventions comes with a cost. Thus, it is important to carefully select the set of cases to which an intervention is applied. The paper proposes a prescriptive process monitoring method that incorporates causal inference techniques to estimate the causal effect of triggering an intervention on each ongoing case of a process. Based on this estimate, the method triggers interventions according to a user-defined policy, taking into account the net gain of the interventions. The method is evaluated on four real-life data sets.
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ItemLearning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learning( 2023-03-06)Increasing the success rate of a process, i.e. the percentage of cases that end in a positive outcome, is a recurrent process improvement goal. At runtime, there are often certain actions (a.k.a. treatments) that workers may execute to lift the probability that a case ends in a positive outcome. For example, in a loan origination process, a possible treatment is to issue multiple loan offers to increase the probability that the customer takes a loan. Each treatment has a cost. Thus, when defining policies for prescribing treatments to cases, managers need to consider the net gain of the treatments. Also, the effect of a treatment varies over time: treating a case earlier may be more effective than later in a case. This paper presents a prescriptive monitoring method that automates this decision-making task. The method combines causal inference and reinforcement learning to learn treatment policies that maximize the net gain. The method leverages a conformal prediction technique to speed up the convergence of the reinforcement learning mechanism by separating cases that are likely to end up in a positive or negative outcome, from uncertain cases. An evaluation on two real-life datasets shows that the proposed method outperforms a state-of-the-art baseline.
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ItemProLift: Automated Discovery of Causal Treatment Rules From Event Logs (Extended Abstract)(CEUR, 2022-01-01)ProLift is a Web-based tool that uses causal machine learning, specifically uplift trees, to discover rules for optimizing business processes based on execution data (event logs). ProLift allows users to upload an event log, to specify case treatments and case outcomes, and to visualize treatment rules that increase the probability of positive case outcomes. The target audience of ProLift includes researchers and practitioners interested in leveraging causal machine learning for process improvement.
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ItemRobotic Process Mining(Springer, Cham, 2022-06-27)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.
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ItemDiscovering data transfer routines from user interaction logs(PERGAMON-ELSEVIER SCIENCE LTD, 2022-07)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.
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ItemPrescriptive Process Monitoring for Cost-Aware Cycle Time Reduction(IEEE, 2021)Reducing cycle time is a recurrent concern in the field of business process management. Depending on the process, various interventions may be triggered to reduce the cycle time of a case, for example, using a faster shipping service in an order-to-delivery process or calling a customer to obtain missing information rather than waiting passively. However, each of these interventions comes with a cost. This paper tackles the problem of determining if and when to trigger a time-reducing intervention in a way that maximizes a net gain function. The paper proposes a prescriptive monitoring method that uses orthogonal random forests to estimate the causal effect of triggering a time-reducing intervention for each ongoing case of a process. Based on this estimate, the method triggers interventions according to a user defined policy. The method is evaluated on two real-life datasets.
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ItemAutomated Repair of Process Models with Non-Local Constraints Using State-Based Region Theory(IOS PRESS, 2021-06-26)State-of-the-art process discovery methods construct free-choice process models from event logs. Consequently, the constructed models do not take into account indirect dependencies between events. Whenever the input behaviour is not free-choice, these methods fail to provide a precise model. In this paper, we propose a novel approach for enhancing free-choice process models by adding non-free-choice constructs discovered a-posteriori via region-based techniques. This allows us to benefit from the performance of existing process discovery methods and the accuracy of the employed fundamental synthesis techniques. We prove that the proposed approach preserves fitness with respect to the event log while improving the precision when indirect dependencies exist. The approach has been implemented and tested on both synthetic and real-life datasets. The results show its effectiveness in repairing models discovered from event logs.
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ItemStructural and Behavioral Biases in Process Comparison Using Models and Logs(Springer, 2021)Process models automatically discovered from event logs represent business process behavior in a compact graphical way. To compare process variants, e.g., to explore how the system’s behavior changes over time or between customer segments, analysts tend to visually compare conceptual process models discovered from different “slices” of the event log, solely relying on the structure of these models. However, the structural distance between two process models does not always reflect the behavioral distance between the underlying event logs and thus structural comparison should be applied with care. This paper aims to investigate relations between structural and behavioral process distances and explain when structural distance between two discovered process models can be used to assess the behavioral distance between the corresponding event logs. Keywords: Process mining · Variant analysis · Structural distance · BPMN
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ItemAutomated Repair of Process Models with Non-local Constraints Using State-Based Region Theory(IOS Press, 2022-01-10)State-of-the-art process discovery methods construct free-choice process models from event logs. Consequently, the constructed models do not take into account indirect dependencies between events. Whenever the input behaviour is not free-choice, these methods fail to provide a precise model. In this paper, we propose a novel approach for enhancing free-choice process models by adding non-free-choice constructs discovered a-posteriori via region-based techniques. This allows us to benefit from the performance of existing process discovery methods and the accuracy of the employed fundamental synthesis techniques. We prove that the proposed approach preserves fitness with respect to the event log while improving the precision when indirect dependencies exist. The approach has been implemented and tested on both synthetic and real-life datasets. The results show its effectiveness in repairing models discovered from event logs.
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ItemRobidium: Automated synthesis of robotic process automation scripts from UI logs(CEUR Workshop Proceedings, 2020-01-01)This paper presents Robidium: a tool that discovers au- tomatable routine tasks from User Interactions (UI) logs and generates Robotic Process Automation (RPA) scripts to automate such routines. Unlike record-and-replay features provided by commercial RPA tools, Robidium may take as input an UI log that is not specifically recorded to capture a pre-identified task. Instead, the log may contain mixtures of automatable and non-automatable routines, interspersed with events that are not part of any routine as well as redundant or irrelevant events.