Abstract

Background: Workflow extraction is the connecting link between process modelling and data mining. Extraction of information and make insight from it using event log is the primary objective of workflow mining. The learning got along these logs can build comprehension about the workflow of procedures and association of different processes. That can help with upgrading them if necessary.

Objective: The aim of this paper is to display a process performance based framework where we compare reference model with extracted model (from a large information system) on the basis of key performance indices.

Methods: Proposed approach perform extraction of workflow model using workflow mining. This process is effective and efficient as compare with building work flow model from scratch. This shows a logic about how to program event log data gathered from different sensors (Internet of Events). How we process and investigates to handle and propel the item work process by using the course of action.

Results: Proposed approach displays a process based framework for the legacy system that ensures the effective and efficient working. So that accordingly extracted model behave like referenced model and results are validated by Key Performance Indices (KPI) for evaluating process performance.

Conclusion: In this experimental data centric approach, our progressing work is to research a metric to quantify the nature of reference models and extracted model. On the basis of metric values, we take the decision on legacy information system process management.

Keywords: EBPA (Event-Based Performance Analysis), workflow mining, event logs, process mining, KPI’s (Key Performance Indicators), PPA (Process Performance analysis).

Graphical Abstract