History
- Business intelligence (BI) emerged more than 20 years ago and is critical for reporting what is happening within an organization’s systems. Yet current BI applications and data mining technologies are not always suited for evaluating the level of detail required to analyze unstructured data and the human dynamics of business processes.
- Six-Sigma and other quantitative approaches to business process improvement have been employed for over a decade with varying degrees of success. A major limitation to the success of these approaches is the availability of accurate data to form the basis of the analysis. With BPD, many six-sigma organizations are finding the ability to extend their analysis into major business processes effectively.
- Process mining (PM) emerged as a scientific discipline around 1990 when techniques like the Alpha algorithm made it possible to extract process models (typically represented as Petri nets) from event logs. Today, there are over 100 process mining algorithms that are able to discover process models that also include concurrency, e.g., genetic process discovery techniques, heuristic mining algorithms, region-based mining algorithms, and fuzzy mining algorithms. The field of process mining combines ideas, techniques, and methods from both the data mining field and the process modeling and process analysis fields.
Read more about this topic: Business Process Discovery
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