Computing and Information Systems - Research Publications
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ItemFast and accurate data-driven goal recognition using process mining techniques(ELSEVIER, 2023-10)The problem of goal recognition requests to automatically infer an accurate probability distribution over possible goals an autonomous agent is attempting to achieve in the environment. The state-of-the-art approaches for goal recognition operate under full knowledge of the environment and possible operations the agent can take. This knowledge, however, is often not available in real-world applications. Given historical observations of the agents' behaviors in the environment, we learn skill models that capture how the agents achieved the goals in the past. Next, given fresh observations of an agent, we infer their goals by diagnosing deviations between the observations and all the available skill models. We present a framework that serves as an outline for implementing such data-driven goal recognition systems and its instance system implemented using process mining techniques. The evaluations we conducted using our publicly available implementation confirm that the approach is well-defined, i.e., all system parameters impact its performance, has high accuracy over a wide range of synthetic and real-world domains, which is comparable with the more knowledge-demanding state-of-the-art approaches, and operates fast.
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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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ItemProcess model forecasting and change exploration using time series analysis of event sequence data(ELSEVIER, 2023-05)Process analytics is a collection of data-driven techniques for, among others, making predictions for individual process instances or overall process models. At the instance level, various novel techniques have been recently devised, tackling analytical tasks such as next activity, remaining time, or outcome prediction. However, there is a notable void regarding predictions at the process model level. It is the ambition of this article to fill this gap. More specifically, we develop a technique to forecast the entire process model from historical event data. A forecasted model is a will-be process model representing a probable description of the overall process for a given period in the future. Such a forecast helps, for instance, to anticipate and prepare for the consequences of upcoming process drifts and emerging bottlenecks. Our technique builds on a representation of event data as multiple time series, each capturing the evolution of a behavioural aspect of the process model, such that corresponding time series forecasting techniques can be applied. Our implementation demonstrates the feasibility of process model forecasting using real-world event data. A user study using our Process Change Exploration tool confirms the usefulness and ease of use of the produced process model forecasts.
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ItemStochastic-aware precision and recall measures for conformance checking in process mining(Elsevier BV, 2023-05-01)Process mining studies ways to improve real-world processes using historical event data generated by IT systems that support business processes of organisations. Given an event log of an IT system, process discovery algorithms construct a process model representing the processes recorded in the log, while conformance checking techniques quantify how well the discovered model achieves this objective. State-of-the-art discovery and conformance techniques either completely ignore or consider but hide from the users information about the likelihood of process behaviour. That is, the vast majority of the existing process discovery algorithms construct non-stochastic aware process models. Consequently, few conformance checking techniques can assess how well such discovered models describe the relative likelihoods of traces recorded in the log or how well they represent the likelihood of future traces generated by the same system. Note that this is necessary to support process simulation, prediction and recommendation. Furthermore, stochastic information can provide business analysts with further actionable insights on frequent and rare conformance issues. This article presents precision and recall measures based on the notion of entropy of stochastic automata, which are capable of quantifying and, hence, differentiating, between frequent and rare deviations of an event log and a process model that is enriched with the information on the relative likelihoods of traces it describes. An evaluation over several real-world datasets that uses our open-source implementation of the measures demonstrates the feasibility of using our precision and recall measures in industrial settings. Finally, we propose a range of intuitive desired properties that stochastic precision and recall measures should possess, and study our and other existing stochastic-aware conformance measures with respect to these properties.
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ItemAll that glitters is not gold: Four maturity stages of process discovery algorithms(PERGAMON-ELSEVIER SCIENCE LTD, 2023-03)A process discovery algorithm aims to construct a process model that represents the real-world process stored in event data well; it is precise, generalizes the data correctly, and is simple. At the same time, it is reasonable to expect that better quality input event data should lead to constructed process models of better quality. However, existing process discovery algorithms omit the discussion of this relationship between the inputs and outputs and, as it turns out, often do not guarantee it. We demonstrate the latter claim using several quality measures for event data and discovered process models. Consequently, this paper requests for more rigor in the design of process discovery algorithms, including properties that relate the qualities of the inputs and outputs of these algorithms. We present four incremental maturity stages for process discovery algorithms, along with concrete guidelines for formulating relevant properties and experimental validation. We then use these stages to review several state of the art process discovery algorithms to confirm the need to reflect on how we perform algorithmic process discovery.
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ItemStatistical Tests and Association Measures for Business Processes(IEEE COMPUTER SOC, 2023-07-01)Through the application of process mining, organisations can improve their business processes by leveraging data recorded as a result of the performance of these processes. Over the past two decades, the field of process mining evolved considerably, offering a rich collection of analysis techniques with different objectives and characteristics. Despite the advances in this field, a solid statistical foundation is still lacking. Such a foundation would allow analysis outcomes to be found or judged using the notion of statistical significance, thus providing a more objective way to assess these outcomes. This article contributes several statistical tests and association measures that treat process behaviour as a variable. The sensitivity of these tests to their parameters is evaluated and their applicability is illustrated through the use of real-life event logs. The presented tests and measures constitute a key contribution to a statistical foundation for process mining.
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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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ItemEntropic relevance: A mechanism for measuring stochastic process models discovered from event data(Elsevier BV, 2022)There are many fields of computing in which having access to large volumes of data allows very precise models to be developed. For example, machine learning employs a range of algorithms that deliver important insights based on analysis of data resources. Similarly, process mining develops algorithms that use event data induced by real-world processes to support the modeling of – and hence understanding and long-term improvement of – those processes. In process mining, the quality of the learned process models is assessed using conformance checking techniques, which measure how well the models represent and generalize the data. This article presents the entropic relevance measure for conformance checking of stochastic process models, which are models that also provide information in regard to the likelihood of observing each sequence of observed events. Accurate stochastic conformance measurement allows identification of models that describe the data better, including the captured sequences of process events and their frequencies, with information about the likelihood of the described processes being an essential step toward simulating and forecasting future processes. Entropic relevance represents a blend between the traditional precision and recall quality criteria in conformance checking, in that it both penalizes observed processes that the model does not describe, and also penalizes processes that are permitted by the model yet were not observed. Entropic relevance can be computed in time linear in the size of the input data; and measures a fundamentally different phenomenon than other existing measures. Our evaluation over industrial datasets confirms the feasibility of using the measure in practice.
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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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ItemAgent System Mining: Vision, Benefits, and Challenges(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021)In the Agent-Based Modeling (ABM) paradigm, an organization is a Multi-Agent System (MAS) composed of autonomous agents inducing business processes. Process Mining automates the creation, update, and analysis of explicit business process models based on event data. Process Mining techniques make simplifying assumptions about the processes discovered from data. However, actual business processes are often more complex than those restricted by Process Mining assumptions. Several Process Mining approaches relax these standard assumptions by discovering more realistic process models. These approaches can discover more realistic process models. However, these models are often difficult to visualize and, consequently, to understand. Many MASs induce processes whose behaviors become more complex with each next embraced time step, while the complexities of these MASs remain constant. Thus, the ABM paradigm can cope naturally with the increasing complexity of the discovered process models. This paper proposes Agent System Mining (ASM) and ASM Framework. ASM combines Process Mining and ABM in the Business Process Management (BPM) context to infer MAS models of operational business processes from real-world event data, while ASM Framework maps ASM activities to different phases of the MAS modeling lifecycle. The paper also discusses the benefits of using ASM and outlines challenges associated with the implementation of the ASM Framework.