SUPR
Process Mining POC
Dnr:

NAISS 2024/22-1117

Type:

NAISS Small Compute

Principal Investigator:

Amin Jalali

Affiliation:

Stockholms universitet

Start Date:

2024-09-16

End Date:

2025-10-01

Primary Classification:

10201: Computer Sciences

Webpage:

Allocation

Abstract

In this project, we aim to leverage the computational power of NAISS to explore how its resources can enhance the training of machine learning models and the application of process mining techniques. These tasks demand extensive processing power, memory, storage, and GPU capabilities, which go beyond the limits of standard computing systems. NAISS offers a high-performance environment that enables efficient large-scale data analysis, the training of complex AI models, and the application of process mining to optimize model performance and decision-making processes. Access to NAISS will allow us to assess how these advanced capabilities can be integrated into and planned for future projects.