This paper proposes a prescriptive process monitoring technique that triggers interventions to optimize a cost function under fixed resource constraints.
The paper proposes a prescriptive process monitoring method that uses orthogonal random forest models to estimate the causal effect of triggering a time-reducing intervention for each ongoing case of a process.
Empirical studies have shown that, while DDS models adequately capture the observed sequences of activities and their frequencies, they fail to capture the temporal dynamics of real-life processes.
Two families of generative process simulation models have been developed in previous work: data-driven simulation models and deep learning models.
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.
Existing methods for business process drift detection are based on an explorative analysis of a potentially large feature space and in some cases they require users to manually identify specific features that characterize the drift.
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.
In this setting, this paper proposes an approach for constructing and querying a common type of artifact used for process mining, namely the frequency and time-annotated Directly-Follows Graph (DFG), over multiple event logs belonging to different parties, in such a way that the parties do not share the event logs with each other.
Cryptography and Security
To address this limitation, the paper proposes a second technique wherein the process model is first decomposed into a set of automata, known as S-components, such that the product of these automata is equal to the automaton of the whole process model.
To address this lack of flexibility, this paper presents an interpreter of BPMN process models based on dynamic data structures.
Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances.
The paper also outlines an approach to compile policy specifications into smart contracts for enforcement.
The Decision Model and Notation (DMN) is a recent OMG standard for the elicitation and representation of decision models, and for managing their interconnection with business processes.
The specificity of Caterpillar is that the state of each process instance is maintained on the (Ethereum) blockchain and the workflow routing is performed by smart contracts generated by a BPMN-to-Solidity compiler.
Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances (called cases) of a business process, such as the prediction of the outcome, next activity or remaining cycle time of a given process case.
Given an event stream produced by the execution of a business process, the goal of an online process discovery method is to maintain a continuously updated model of the process with a bounded amount of memory while at the same time achieving similar accuracy as offline methods.
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.
We then show that temporal stability can be enhanced by hyperparameter-optimizing random forests and XGBoost classifiers with respect to inter-run stability.
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.
First, we show that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp.
Business process deviance refers to the phenomenon whereby a subset of the executions of a business process deviate, in a negative or positive way, with respect to its expected or desirable outcomes.