to be able to apply process mining techniques it is essential to extract event logs from data sources (e.g. databases transaction logs audit trails etc.). xes is the standard format for process mining supported by the majority of process mining tools.
process mining explained by an example the logistics process at smartcoat inc. smartcoat. episode 2 (out of 8): process discovery
l’oréal is discovering improvement opportunities in their order-to-cash process with celonis process mining. watch the video. lufthansa. lufthansa is using celonis to accelerate on-ground operations and reach new levels of process excellence for their passengers.
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normalization: normalization performed when the attribute data are scaled up o scaled down. example: data should fall in the range -2.0 to 2.0 post-normalization. attribute construction: these attributes are constructed and included the given set of attributes helpful for data mining.. the result of this process is a final data set that can be used in modeling.
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process mining gives you access to your end-to-end process so you can locate bottlenecks and prioritize your process improvement efforts to achieve a better roi. how to prepare for process mining the data required for process mining comes in the form of event logs that it systems typically record at various points along your business process