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Extracting Event Data from Databases to Unleash Process Mining Will M.P. van der Aalst 12Architecture of Information Systems, Eindhoven University of Technology, P.O. Box 513, NL5600 MB, Eindhoven,
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How to fill out process mining can be

01
Collect the relevant data: Start by gathering the necessary data about the process you want to analyze. This data can include event logs, system logs, or any other information related to the process.
02
Preprocess the data: Clean and preprocess the collected data to ensure its accuracy and consistency. This step may involve removing duplicates, handling missing values, and transforming the data into a suitable format for further analysis.
03
Apply process mining techniques: Use process mining algorithms and techniques to analyze the preprocessed data. This involves discovering process models, identifying process variations, and extracting valuable insights from the data.
04
Interpret the results: Analyze the results obtained from the process mining analysis. This includes identifying bottlenecks, deviations from expected behavior, and opportunities for process improvement.
05
Take action: Use the insights gained from process mining to make informed decisions and take appropriate actions to optimize the process. Implement process improvements and monitor their impact to ensure continuous improvement.

Who needs process mining can be?

01
Organizations aiming for process optimization: Process mining can be valuable for any organization looking to improve the efficiency and effectiveness of their business processes. It helps identify bottlenecks, inefficiencies, and areas for improvement.
02
Process owners and managers: Process mining provides process owners and managers with a clear view of how their processes are actually being executed. It helps them understand the process variants, deviations, and performance metrics, enabling better decision-making and process optimization.
03
Compliance and audit teams: Process mining can be valuable for compliance and audit teams as it enables them to gain insights into how processes are being followed. It helps identify non-compliance, deviations from expected behavior, and potential fraud or inefficiencies.
04
Continuous improvement teams: Process mining is essential for teams responsible for continuous improvement initiatives. It provides them with data-driven insights into process bottlenecks, inefficiencies, and opportunities for automation or optimization.
05
Data analysts and data scientists: Process mining requires data analysis and interpretation skills. Data analysts and data scientists can leverage process mining techniques to uncover patterns, anomalies, and insights from process data, contributing to process optimization efforts.
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