Computing and Information Systems - Research Publications
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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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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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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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ItemMulti-Perspective process model discovery for robotic process automation(CEUR Workshop Proceedings, 2018-01-01)Robotic Process Automation (RPA) is a novel approach for immediate cost reduction and gaining operational efficiency. RPA tools can automate repeatable tasks, thus reducing the error rates and increasing overall process performance. Even more, RPA improves the quality of the data (data completeness, data consistency/correctness, etc.). Although, being widely used in many organizations, RPA suffers from high time consumption allocated to the training of software robots (bots for short). Moreover, the models used for training are often inaccurate, which leads to increase of time spent on testing the bots. One of the possible solutions is to apply process mining in order to extract the information about the processes from UI logs such as clickstreams and keylogs, which can then be used to train the bots. However, traditional process discovery techniques are not suitable for the purpose of RPA, as they discover only control-flow perspective of the process and cannot deal well with the UI logs, producing huge and complex models. The proposed research project aims at shifting process mining techniques from working on event logs to working on UI logs as well as developing multi-perspective automated discovery technique, which can then be applied to train the RPA bots.
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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.
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ItemProcess Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs(IEEE, 2020-10-22)This paper proposes an approach to analyze an event log of a business process in order to generate case-level recommendations of treatments that maximize the probability of a given outcome. Users classify the attributes in the event log into controllable and non-controllable, where the former correspond to attributes that can be altered during an execution of the process (the possible treatments). We use an action rule mining technique to identify treatments that co-occur with the outcome under some conditions. Since action rules are generated based on correlation rather than causation, we then use a causal machine learning technique, specifically uplift trees, to discover subgroups of cases for which a treatment has a high causal effect on the outcome after adjusting for confounding variables. We test the relevance of this approach using an event log of a loan application process and compare our findings with recommendations manually produced by process mining experts.
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ItemAutomated Discovery of Process Models with True Concurrency and Inclusive Choices(Springer International Publishing, 2021-01-01)Enterprise information systems allow companies to maintain detailed records of their business process executions. These records can be extracted in the form of event logs, which capture the execution of activities across multiple instances of a business process. Event logs may be used to analyze business processes at a fine level of detail using process mining techniques. Among other things, process mining techniques allow us to discover a process model from an event log – an operation known as automated process discovery. Despite a rich body of research in the field, existing automated process discovery techniques do not fully capture the concurrency inherent in a business process. Specifically, the bulk of these techniques treat two activities A and B as concurrent if sometimes A completes before B and other times B completes before A. Typically though, activities in a business process are executed in a true concurrency setting, meaning that two or more activity executions overlap temporally. This paper addresses this gap by presenting a refined version of an automated process discovery technique, namely Split Miner, that discovers true concurrency relations from event logs containing start and end timestamps for each activity. The proposed technique is also able to differentiate between exclusive and inclusive choices. We evaluate the proposed technique relative to existing baselines using 11 real-life logs drawn from different industries.
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ItemA Framework for Estimating Simplicity of Automatically Discovered Process Models Based on Structural and Behavioral Characteristics(Springer, 2020)A plethora of algorithms for automatically discovering process models from event logs has emerged. The discovered models are used for analysis and come with a graphical flowchart-like representation that supports their comprehension by analysts. According to the Occam’s Razor principle, a model should encode the process behavior with as few constructs as possible, that is, it should not be overcomplicated without necessity. The simpler the graphical representation, the easier the described behavior can be understood by a stakeholder. Conversely, and intuitively, a complex representation should be harder to understand. Although various conformance checking techniques that relate the behavior of discovered models to the behavior recorded in event logs have been proposed, there are no methods for evaluating whether this behavior is represented in the simplest possible way. Existing techniques for measuring the simplicity of discovered models focus on their structural characteristics such as size or density, and ignore the behavior these models encoded. In this paper, we present a conceptual framework that can be instantiated into a concrete approach for estimating the simplicity of a model, considering the behavior the model describes, thus allowing a more holistic analysis. The reported evaluation over real-life event logs for several instantiations of the framework demonstrates its feasibility in practice.
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ItemAutomated Discovery of Data Transformations for Robotic Process Automation(AAAI Press, 2020)Robotic Process Automation (RPA) is a technology for automating repetitive routines consisting of sequences of user interactions with one or more applications. In order to fully exploit the opportunities opened by RPA, companies need to discover which specific routines may be automated, and how. In this setting, this paper addresses the problem of analyzing User Interaction (UI) logs in order to discover routines where a user transfers data from one spreadsheet or (Web) form to another. The paper maps this problem to that of discovering data transformations by example - a problem for which several techniques are available. The paper shows that a naive application of a state-of-the-art technique for data transformation discovery is computationally inefficient. Accordingly, the paper proposes two optimizations that take advantage of the information in the UI log and the fact that data transfers across applications typically involve copying alphabetic and numeric tokens separately. The proposed approach and its optimizations are evaluated using UI logs that replicate a real-life repetitive data transfer routine.
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ItemIdentifying candidate routines for Robotic Process Automation from unsegmented UI logs(IEEE, 2020-10-22)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.