SearcharxivSearch

arXiv subjects

Simona Fioretto

Publications and source records attributed to Simona Fioretto.

2 recordsLinked to original sources

Predictive Process Monitoring: a comparison survey between different type of event logs

The application of Predictive Process Monitoring (PPM) techniques is becoming increasingly widespread due to their capacity to provide organizations with accurate predictions regarding the future behavior of business processes, thereby facilitating more informed decision-making. A plethora of solutions have been proposed in the literature employing these techniques, yet they differ from one another due to a number of factors. However, in light of the growing recognition of the value of object-centric event logs, including in the context of PPM, this survey focuses on the differences among PPM techniques employed with different event logs, namely traditional event logs and object-centric event logs. In addition, the reviewed methods are classified according to the prediction task they address and the specific methodologies they employ.

cs.SE

A Brief Discussion on KPI Development in Public Administration

Efficient and effective service delivery in Public Administration (PA) relies on the development and utilization of key performance indicators (KPIs) for evaluating and measuring performance. This paper presents an innovative framework for KPI construction within performance evaluation systems, leveraging Random Forest algorithms and variable importance analysis. The proposed approach identifies key variables that significantly influence PA performance, offering valuable insights into the critical factors driving organizational success. By integrating variable importance analysis with expert consultation, relevant KPIs can be systematically developed, ensuring that improvement strategies address performance-critical areas. The framework incorporates continuous monitoring mechanisms and adaptive phases to refine KPIs in response to evolving administrative needs. This study aims to enhance PA performance through the application of machine learning techniques, fostering a more agile and results-driven approach to public administration.

cs.LG