A synthetic event log with 100,000 traces and 900,000 events that was generated by simulating a simple artificial process model. There are three data attributes in the event log: Priority, Nurse, and Type. Some paths in the model are recorded infrequently based on the value of these attributes. Noise is added by randomly adding one additional event to an increasing number of traces
Process mining allows analysts to exploit logs of historical executions of business processes to ext...
The data set contains an extended set of event logs for evaluating multi-perspective trace clusterin...
Process Discovery is concerned with the automatic generation of a process model that describes a bus...
A synthetic event log with 100,000 traces and 900,000 events that was generated by simulating a simp...
A synthetic event log with 100,000 traces and 900,000 events that was generated by simulating a simp...
Abstract. Past research revealed issues with artificial event data used for comparative analysis of ...
An event log contains a historical record of the steps taken in a business process. An event log con...
Artifical event log generated randomly from a process tree containing 30 activities. The log is neit...
Process discovery algorithms address the problem of learning process models from event logs. Typical...
The goal of process mining is to discover the process behavior from the runtime information of proce...
In the field of process discovery, it is worth noting that most process discovery algorithms assume ...
The dataset contains three types of logs: large logs to test scalability, incomplete logs and log wi...
Effective information systems require the existence of explicit process models. A completely specifi...
Effective information systems require the existence of explicit process models. A completely specifi...
The data set contains a set of event logs for evaluating multi-perspective trace clustering approach...
Process mining allows analysts to exploit logs of historical executions of business processes to ext...
The data set contains an extended set of event logs for evaluating multi-perspective trace clusterin...
Process Discovery is concerned with the automatic generation of a process model that describes a bus...
A synthetic event log with 100,000 traces and 900,000 events that was generated by simulating a simp...
A synthetic event log with 100,000 traces and 900,000 events that was generated by simulating a simp...
Abstract. Past research revealed issues with artificial event data used for comparative analysis of ...
An event log contains a historical record of the steps taken in a business process. An event log con...
Artifical event log generated randomly from a process tree containing 30 activities. The log is neit...
Process discovery algorithms address the problem of learning process models from event logs. Typical...
The goal of process mining is to discover the process behavior from the runtime information of proce...
In the field of process discovery, it is worth noting that most process discovery algorithms assume ...
The dataset contains three types of logs: large logs to test scalability, incomplete logs and log wi...
Effective information systems require the existence of explicit process models. A completely specifi...
Effective information systems require the existence of explicit process models. A completely specifi...
The data set contains a set of event logs for evaluating multi-perspective trace clustering approach...
Process mining allows analysts to exploit logs of historical executions of business processes to ext...
The data set contains an extended set of event logs for evaluating multi-perspective trace clusterin...
Process Discovery is concerned with the automatic generation of a process model that describes a bus...