DIAGNOSIS AND PREDICTION OF BUSINESS PROCESS DEVIANCES
Grant number: DP180102839 | Funding period: 2018 - 2022
This project aims to develop an innovative approach based on process execution semantics, to analyse event data logged by IT systems in order to diagnose and predict business process deviance. Anticipated outcomes include novel business intelligence algorithms producing deviance diagnostics, predictions and recommendations and exposing results via interactive visual analytics. The outcomes are expected to aid process workers in steering business operations towards consistent and compliant outcomes and higher performance, and assist analysts and auditors to explain deviant operations. This should significantly benefit industries such as healthcare, insurance, retail and the government where c..View full description
Related publications (52)
Measuring Fitness and Precision of Automatically Discovered Process Models: A Principled and Scalable Approach
Adriano Augusto, Raffaele Conforti, Abel Armas-Cervantes, Marlon Dumas, Marcello La Rosa
Automated process discovery techniques allow us to generate a process model from an event log consisting of a collection of busine..
Entropic relevance: A mechanism for measuring stochastic process models discovered from event data
Hanan Alkhammash, Artem Polyvyanyy, Alistair Moffat, Luciano García-Bañuelos
There are many fields of computing in which having access to large volumes of data allows very precise models to be developed. For..
All That Glitters Is Not Gold: Towards Process Discovery Techniques with Guarantees
Jan Martijn van der Werf, Artem Polyvyanyy, Bart van Wensveen, Matthieu Brinkhuis, Hajo Reijers
The aim of a process discovery algorithm is to construct from event data a process model that describes the underlying, real-world..
Automated Repair of Process Models with Non-Local Constraints Using State-Based Region Theory
Anna Kalenkova, Josep Carmona, Artem Polyvyanyy, Marcello La Rosa
State-of-the-art process discovery methods construct free-choice process models from event logs. Consequently, the constructed mod..
Scalable alignment of process models and event logs: An approach based on automata and S-components
Daniel Reissner, Abel Armas-Cervantes, Raffaele Conforti, Marlon Dumas, Dirk Fahland, Marcello La Rosa
Given a model of the expected behavior of a business process and given an event log recording its observed behavior, the problem o..
Identifying candidate routines for Robotic Process Automation from unsegmented UI logs
Volodymyr Leno, Adriano Augusto, Marlon Dumas, Marcello La Rosa, Fabrizio Maria Maggi, Artem Polyvyanyy
Robotic Process Automation (RPA) is a technology to develop software bots that automate repetitive sequences of interactions betwe..
Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs
Zahra Dasht Bozorgi, Irene Teinemaa, Marlon Dumas, Marcello La Rosa, Artem Polyvyanyy
This paper proposes an approach to analyze an event log of a business process in order to generate case-level recommendations of t..
Monotone Precision and Recall Measures for Comparing Executions and Specifications of Dynamic Systems
Artem Polyvyanyy, Andreas Solti, Matthias Weidlich, Claudio Di Ciccio, Jan Mendling
The behavioural comparison of systems is an important concern of software engineering research. For example, the areas of specific..
Optimization Framework for DFG-based Automated Process Discovery Approaches
Adriano Augusto, Marlon Dumas, Marcello La Rosa, Sander Leemans, Seppe Vanden Broucke
The problem of automatically discovering business process models from event logs has been intensely investigated in the past two d..
A Framework for Estimating Simplicity of Automatically Discovered Process Models Based on Structural and Behavioral Characteristics
Anna Kalenkova, Artem Polyvyanyy, Marcello La Rosa
A plethora of algorithms for automatically discovering process models from event logs has emerged. The discovered models are used ..
Automated Discovery of Data Transformations for Robotic Process Automation
Volodymyr Leno, Marlon Dumas, Marcello La Rosa, Fabrizio M Maggi, Artem Polyvyanyy
Robotic Process Automation (RPA) is a technology for automating repetitive routines consisting of sequences of user interactions w..
Entropia: A family of entropy-based conformance checking measures for process mining
A Polyvyanyy, H Alkhammash, C Di Ciccio, L García-Bañuelos, A Kalenkova, SJJ Leemans, J Mendling, A Moffat, M Weidlich
This paper presents a command-line tool, called Entropia, that implements a family of conformance checking measures for process mi..
Predictive business process monitoring via generative adversarial nets: The case of next event prediction
F Taymouri, ML Rosa, S Erfani, ZD Bozorgi, I Verenich
Predictive process monitoring aims to predict future characteristics of an ongoing process case, such as case outcome or remaining..
Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring
I Verenich, Marlon Dumas, M La Rosa, Fabrizio Maggi, Irene Teinemaa
Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running ins..
Abstract and Compare: A Framework for Defining Precision Measures for Automated Process Discovery
A Augusto, A Armas Cervantes, R Conforti, M Dumas, M La Rosa, D Reissner
Automated process discovery techniques allow us to extract business process models from event logs. The quality of process models ..