Machine Learning is a branch of artificial intelligence and describes the “learning” of a digital system on the basis of real data. An algorithm examines large amounts of data according to certain patterns or rules. Based on these findings, IT systems are able to find solutions for specific problems. This information can be used, for example, to solve related problems or to analyze unknown data. This technique is based on (self-learning) algorithms, which are adapted or changed during the learning process. In Process Mining, Machine Learning is used for root cause analyses.
How exactly does Machine Learning work?
Let’s start from the beginning – with us, the people. The logical prerequisite for Machine Learning is the existence of data. In order to generate data, some kind of human action is necessary beforehand. Take for instance business processes. The IT systems used here log process cycles, such as the activities of employees. But data collection in completely automated processes was also set up by a human being at the beginning. As soon as the data is available, a person still has to tell the system how to analyze the data set. The system therefore needs rules or problems so that the algorithms “know” what they have to do to analyze the data. The human being therefore supplies the input – the generated data – and defines the framework conditions for the system.
As soon as certain patterns are identified, the system records the results in an appropriate form, for example as rules, graphics, in text form or as key figures. These are then formulated in such a way that they can be interpreted by us humans.
What can Machine Learning do?
> Identify, extract and summarize data
> Make predictions
> Calculate occurrence probabilities of events
> Adapt the system independently to different circumstances and developments
> Data-based optimization of processes
Machine Learning and Process Mining
Large amounts of data – specifically process data – are also examined in Process Mining. This is why Machine Learning is also suitable here, for example to find out why a certain process variant was developed. This type of analysis is also known as automated root cause analysis. The algorithm examines the process data according to certain patterns or rules in order to explain process weaknesses or undesired process deviations, for example. These are then formulated, for example, as rules that provide information about the cause and extent of the vulnerability. At the same time, the rules can be used to make predictions, for example to assess the risk of bottlenecks.
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