Related papers: OCPM$^2$: Extending the Process Mining Methodology…
One aim of Process Mining (PM) is the discovery of process models from event logs of information systems. PM has been successfully applied to process-oriented enterprise systems but is less suited for communication- and document-oriented…
Process mining methods allow analysts to use logs of historical executions of business processes in order to gain knowledge about the actual behavior of these processes. One of the most widely studied process mining operations is automated…
The academic evolution of process mining is moving toward object centric process mining, marking a significant shift in how processes are modeled and analyzed. IBM has developed its own distinctive approach called Multilevel Process Mining.…
Integrating Internet of Things (IoT) data with business process event logs is crucial for analysing IoT-enhanced processes, yet remains challenging due to differences in abstraction levels and the separation of data sources. Simply…
Object-centric predictive process monitoring explores and utilizes object-centric event logs to enhance process predictions. The main challenge lies in extracting relevant information and building effective models. In this paper, we propose…
Process mining techniques focus on extracting insight in processes from event logs. In many cases, events recorded in the event log are too fine-grained, causing process discovery algorithms to discover incomprehensible process models or…
Process mining has grown popular today given their ability to provide managers with insights into the actual business process as executed by employees. Process mining depends on event logs found in process aware information systems to model…
Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated…
Process discovery algorithms learn process models from executed activity sequences, describing concurrency, causality, and conflict. Concurrent activities require observing multiple permutations, increasing data requirements, especially for…
This paper proposes a novel method for analyzing food retail processes with a focus on reducing food waste. The approach integrates object-centric process mining (OCPM) with stochastic process discovery and analysis. First, a stochastic…
Process mining offers techniques to exploit event data by providing insights and recommendations to improve business processes. The growing amount of algorithms for process discovery has raised the question of which algorithms perform best…
By adequate employing of complex event processing (CEP), valuable information can be extracted from the underlying complex system and used in controlling and decision situations. An example application area is management of IT systems for…
Process mining, a technique turning event data into business process insights, has traditionally operated on the assumption that each event corresponds to a singular case or object. However, many real-world processes are intertwined with…
Object-centric event logs, allowing events related to different objects of different object types, represent naturally the execution of business processes, such as ERP (O2C and P2P) and CRM. However, modeling such complex information…
Process mining methods often analyze processes in terms of the individual end-to-end process runs. Process behavior, however, may materialize as a general state of many involved process components, which can not be captured by looking at…
Today's process modeling languages often force the analyst or modeler to straightjacket real-life processes into simplistic or incomplete models that fail to capture the essential features of the domain under study. Conventional business…
The number of events recorded for operational processes is growing every year. This applies to all domains: from health care and e-government to production and maintenance. Event data are a valuable source of information for organizations…
Data quality assessment and data cleaning are context-dependent activities. Motivated by this observation, we propose the Ontological Multidimensional Data Model (OMD model), which can be used to model and represent contexts as logic-based…
Causal dynamics models (CDMs) have demonstrated significant potential in addressing various challenges in reinforcement learning. To learn CDMs, recent studies have performed causal discovery to capture the causal dependencies among…
This paper presents the results of an industry expert survey about event log generation in process mining. It takes academic assumptions as a starting point and elicits practitioner's assessments of statements about process execution,…