Detecting Context-Aware Deviations in Process Executions

Lecture notes in business information processing, 2018

Predictive process monitoring is concerned with the analysis of events produced during the execution of a process in order to predict the future state of ongoing cases thereof. Existing techniques in this field are able to predict, at each step of a case, the likelihood that the case will end up in an undesired outcome. These techniques, however, do not take into account what process workers may do with the generated predictions in order to decrease the likelihood of undesired outcomes. This paper proposes a framework for prescriptive process monitoring, which extends predictive process monitoring approaches with the concepts of alarms, interventions, compensations, and mitigation effects. The framework incorporates a parameterized cost model to assess the cost-benefit tradeoffs of applying prescriptive process monitoring in a given setting. The paper also outlines an approach to optimize the generation of alarms given a dataset and a set of cost model parameters. The proposed approach is empirically evaluated using a range of real-life event logs.

Lecture Notes in Computer Science, 2023

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.

ACM Transactions on Knowledge Discovery from Data

Predictive business process monitoring refers to the act of making predictions about the future state of ongoing cases of a business process, based on their incomplete execution traces and logs of historical (completed) traces. Motivated by the increasingly pervasive availability of fine-grained event data about business process executions, the problem of predictive process monitoring has received substantial attention in the past years. In particular, a considerable number of methods have been put forward to address the problem of outcome-oriented predictive process monitoring, which refers to classifying each ongoing case of a process according to a given set of possible categorical outcomes—e.g., Will the customer complain or not? Will an order be delivered, canceled, or withdrawn? Unfortunately, different authors have used different datasets, experimental settings, evaluation measures, and baselines to assess their proposals, resulting in poor comparability and an unclear pictur...

2021

In this document, we provide supplementary material for the paper entitled "Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach." We give more results about the empirical threshold to compute an optimal threshold based on historical data and different treatment duration distributions.

Lecture Notes in Computer Science, 2014

Processes are everywhere in our daily lives. More and more information about executions of processes are recorded in event logs by several information systems. Process mining techniques are used to analyze historic information hidden in event logs and to provide surprising insights for managers, system developers, auditors, and end users. While existing process mining techniques mainly analyze full process instances (cases), this paper extends the analysis to running cases, which have not yet completed. For running cases, process mining can be used to notify future events. This forecasting ability can provide insights for check conformance and support decision making. This paper details a process mining approach, which uses predictive clustering to equip an execution scenario with a prediction model. This model accounts for recent events of running cases to predict the properties of future events. Several tests with benchmark logs investigate the viability of the proposed approach.

2021 3rd International Conference on Process Mining (ICPM), 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 userdefined policy. The method is evaluated on two real-life datasets.

Risk identification is one of the most challenging stages in the risk management process. Conventional risk management approaches provide little guidance and companies often rely on the knowledge of experts for risk identification. In this paper we demonstrate how risk indicators can be used to predict process delays via a method for configuring so-called Process Risk Indicators (PRIs). The method learns suitable configurations from past process behaviour recorded in event logs. To validate the approach we have implemented it as a plug-in of the ProM process mining framework and have conducted experiments using various data sets from a major insurance company.

2012

Discovering predictive performance models is an emerging topic in Process Mining. However, making accurate estimates is not easy especially when considering fine-grain metrics (such as processing times) on complex and flexible processes, where performances may change over time depending on context factors. We try to face such a situation by a general predictive-clustering approach, where different context-related execution scenarios are find and equipped with distinct performanceprediction models. A two-stage forecast can be then made for a new process case by using the model of the cluster it is estimated to belong to. Tests on real-life logs confirmed the validity of the approach.

2020

The main goal of predictive process monitoring is predicting a possible outcome, execution time, and the cost of a business process by using historical data. The predictions are given at runtime, and historical data is provided in terms of an event log. Each predictive monitoring system contains predictive models which are the main part of it. Predictive models are used to make predictions and are built using information contained in the event logs. However, it is not enough just to show the prediction without giving an explanation since users want to know the rationale behind a prediction. If a person wants to take any action based on the prediction, it is definitely needed to explain the prediction in an understandable way otherwise it would be difficult to trust it. Therefore, in this thesis, we will show why explainable predictive monitoring is useful. We will do this by implementing different predictive model explanation methodologies and by investigating their application in real-life scenarios.

2007

Nowadays, process management systems can be used not only in classical business scenarios, but also in highly mobile and dynamic situations, e.g., in supporting operators during emergency management in order to coordinate their activities. In such challenging situations, processes should be adapted, in order to cope with anomalous situations, including connection anomalies and task faults. In this paper, we present a general approach, based on execution monitoring, which is (i) practical, by relying on well-established planning techniques, and (ii) does not require the definition of the adaptation strategy in the process itself (as most of the current approaches do). We prove the correctness and completeness of the approach.