Repairing Event Logs Using Timed Process Models

Process mining aims to infer meaningful insights from process-related data and attracted the attention of practitioners, tool-vendors, and researchers in recent years. Traditionally, event logs are assumed to describe the as-is situation. But this is not necessarily the case in environments where logging may be compromised due to manual logging. For example, hospital staff may need to manually enter information regarding the patient’s treatment. As a result, events or timestamps may be missing or incorrect. In this work, we make use of process knowledge captured in process models, and provide a method to repair missing events in the logs. This way, we facilitate analysis of incomplete logs. We realize the repair by combining stochastic Petri nets, alignments, and Bayesian networks. Keywords: process mining; missing data; stochastic Petri nets; Bayesian network

Enhancing workflow-nets with data for trace completion

The growing adoption of IT-systems for modeling and executing (business) processes or services has thrust the scientific investigation towards techniques and tools which support more complex forms of process analysis. Many of them, such as conformance checking, process alignment, mining and enhancement, rely on complete observation of past (tracked and logged) executions. In many real cases, however, the lack of human or IT-support on all the steps of process execution, as well as information hiding and abstraction of model and data, result in incomplete log information of both data and activities. This paper tackles the issue of automatically repairing traces with missing information by notably considering not only activities but also data manipulated by them. Our technique recasts such a problem in a reachability problem and provides an encoding in an action language which allows to virtually use any state-of-the-art planning to return solutions

Predictive Monitoring of Business Processes

Modern information systems that support complex business processes generally maintain significant amounts of process execution data, particularly records of events corresponding to the execution of activities (event logs). In this paper, we present an approach to analyze such event logs in order to predictively monitor business goals during business process execution. At any point during an execution of a process, the user can define business goals in the form of linear temporal logic rules. When an activity is being executed, the framework identifies input data values that are more (or less) likely to lead to the achievement of each business goal. Unlike reactive compliance monitoring approaches that detect violations only after they have occurred, our predictive monitoring approach provides early advice so that users can steer ongoing process executions towards the achievement of business goals. In other words, violations are predicted (and potentially prevented) rather than merely detected. The approach has been implemented in the ProM process mining toolset and validated on a real-life log pertaining to the treatment of cancer patients in a large hospital

Predictive Process Monitoring Methods: Which One Suits Me Best?

Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for companies to navigate in this domain in order to find, provided certain data, what can be predicted and what methods to use. The main objective of this paper is developing a value-driven framework for classifying existing work on predictive process monitoring. This objective is achieved by systematically identifying, categorizing, and analyzing existing approaches for predictive process monitoring. The review is then used to develop a value-driven framework that can support organizations to navigate in the predictive process monitoring field and help them to find value and exploit the opportunities enabled by these analysis techniques

Discovering stochastic Petri nets with arbitrary delay distributions from event logs

Capturing the performance of a system or business process as accurately as possible is important, as models enriched with performance information provide valuable input for analysis, operational support, and prediction. Due to their computationally nice properties, memoryless models such as exponentially distributed stochastic Petri nets have earned much attention in research and industry. However, there are cases when the memoryless property is clearly not able to capture process behavior, e.g., when dealing with fixed time-outs. We want to allow models to have generally distributed durations to be able to capture the behavior of the environment and resources as accurately as possible. For these more expressive process models, the execution policy has to be specified in more detail. In this paper, we present and evaluate process discovery algorithms for each of the execution policies. The introduced approach uses raw event execution data to discover various classes of stochastic Petri nets. The algorithms are based on the notion of alignments and have been implemented as a plug-in in the process mining framework ProM. Keywords: Process mining; Stochastic Petri nets; Generally distributed transition

Event log reconstruction using autoencoders

Poor quality of process event logs prevents high quality business process analysis and improvement. Process event logs quality decreases because of missing attribute values or after incorrect or irrelevant attribute values are identified and removed. Reconstructing a correct value for these missing attributes is likely to increase the quality of event log-based process analyses. Traditional statistical reconstruction methods work poorly with event logs, because of the complex interrelations among attributes, events and cases. Machine learning approaches appear more suitable in this context, since they can learn complex models of event logs through training. This paper proposes a method for reconstructing missing attribute values in event logs based on the use of autoencoders. Autoencoders are a class of feed-forward neural networks that reconstruct their own input after having learnt a model of its latent distribution. They suit problems of unsupervised learning, such as the one considered in this paper. When reconstructing missing attribute values in an event log, in fact, one cannot assume that a training set with true labels is available for model training. The proposed method is evaluated on two real event logs against baseline methods commonly used in the literature for imputing missing values in large datasets

Earth Movers’ Stochastic Conformance Checking

Process Mining aims to support Business Process Management (BPM) by extracting information about processes from real-life process executions recorded in event logs. In particular, conformance checking aims to measure the quality of a process model by quantifying differences between the model and an event log or another model. Even though event logs provide insights into the likelihood of observed behaviour, most state-of-the-art conformance checking techniques ignore this point of view. In this paper, we propose a conformance measure that considers the stochastic characteristics of both the event log and the process model. It is based on the “earth movers’ distance” and measures the effort to transform the distributions of traces of the event log into the distribution of traces of the model. We formalize this intuitive conformance metric and provide an approximation and a simplified variant. The latter two have been implemented in ProM and we evaluate them using several real-life examples.</p