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Found 21 papers, 10 papers with code
Five guidelines to improve context-aware process selection: an Australian banking perspective
In this study, the role of context in the process selection step is considered.
Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learning
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.
Generalization in Automated Process Discovery: A Framework based on Event Log Patterns
We address these shortcomings by proposing a framework of measures that generalize a set of patterns discovered from an event log with representative traces and check the corresponding control-flow structures in the process model via their trace alignment.
Why Do Banks Find Business Process Compliance So Challenging? An Australian Case Study
This is despite Business Process Compliance (BPC) being both a well researched domain in academia and one where significant progress has been made.
AI-Augmented Business Process Management Systems: A Research Manifesto
AI-Augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems, empowered by trustworthy AI technology.
Automated Repair of Process Models with Non-Local Constraints Using State-Based Region Theory
State-of-the-art process discovery methods construct free-choice process models from event logs.
Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction
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.
A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequences
The results show improvements up to four times compared to the state of the art in suffix and remaining time prediction of event sequences, specifically in the realm of business process executions.
Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs
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.
Encoder-Decoder Generative Adversarial Nets for Suffix Generation and Remaining Time Prediction of Business Process Models
This paper proposes an encoder-decoder architecture grounded on Generative Adversarial Networks (GANs), that generates a sequence of activities and their timestamps in an end-to-end way.
Detecting sudden and gradual drifts in business processes from execution traces
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.
Efficient Conformance Checking using Approximate Alignment Computation with Tandem Repeats
Alignments are an established technique to compute the distance between a trace in the event log and the closest execution trace of a corresponding process model.
Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction
Predictive process monitoring aims to predict future characteristics of an ongoing process case, such as case outcome or remaining timestamp.
Automated Discovery of Data Transformations for Robotic Process Automation
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.
Business Process Variant Analysis based on Mutual Fingerprints of Event Logs
The cornerstone of this approach is a technique to learn a directly follows graph called mutual fingerprint from the event logs of the two variants.
Scalable Alignment of Process Models and Event Logs: An Approach Based on Automata and S-Components
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.
Software Engineering
Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring
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.
Discovering Process Maps from Event Streams
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.
Outcome-Oriented Predictive Process Monitoring: Review and Benchmark
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.
Predictive Business Process Monitoring with LSTM Neural Networks
First, we show that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp.
Multivariate Time Series Forecasting
Predictive Process Monitoring
+1
Business Process Deviance Mining: Review and Evaluation
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.
