Related papers: Object-Centric Local Process Models
Process mining has emerged as a way to analyze the behavior of an organization by extracting knowledge from event logs and by offering techniques to discover, monitor and enhance real processes. In the discovery of process models,…
Object-centric processes (a.k.a. Artifact-centric processes) are implementations of a paradigm where an instance of one process is not executed in isolation but interacts with other instances of the same or other processes. Interactions…
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…
Process mining is a new emerging research trend over the last decade which focuses on analyzing the processes using event log and data. The raising integration of information systems for the operation of business processes provides the…
The scalability of process mining techniques is one of the main challenges to tackling the massive amount of event data produced every day in enterprise information systems. To this purpose, filtering and sampling techniques are proposed to…
One of the most valuable assets of an organization is its organizational data. The analysis and mining of this potential hidden treasure can lead to much added-value for the organization. Process mining is an emerging area that can be…
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.…
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…
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…
Predictive business process monitoring methods exploit logs of completed cases of a process in order to make predictions about running cases thereof. Existing methods in this space are tailor-made for specific prediction tasks. Moreover,…
Process mining represents an important field in BPM and data mining research. Recently, it has gained importance also for practitioners: more and more companies are creating business process intelligence solutions. The evaluation of process…
Process Mining is a famous technique which is frequently applied to Software Development Processes, while being neglected in Human-Computer Interaction (HCI) recommendation applications. Organizations usually train employees to interact…
Process discovery is one of the primary process mining tasks and starting point for process improvements using event data. Existing process discovery techniques aim to find process models that best describe the observed behavior. The focus…
Process mining is a research area focusing on the design of algorithms that can automatically provide insights into business processes. Among the most popular algorithms are those for automated process discovery, which have the ultimate…
Real-world processes often involve interdependent objects that also carry data values, such as integers, reals, or strings. However, existing process formalisms fall short to combine key modeling features, such as tracking object…
Object-centric process mining is a new paradigm with more realistic assumptions about underlying data by considering several case notions, e.g., an order handling process can be analyzed based on order, item, package, and route case…
Automated process discovery is a class of process mining methods that allow analysts to extract business process models from event logs. Traditional process discovery methods extract process models from a snapshot of an event log stored in…
Providing appropriate structures around human resources can streamline operations and thus facilitate the competitiveness of an organization. To achieve this goal, modern organizations need to acquire an accurate and timely understanding of…
Deep-learning models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) have been successfully used for process-mining tasks. They have achieved better performance for different predictive tasks than traditional…
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. Motivated by…