<Li> 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 . </Li> <Li> 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 . </Li> <Li> Process mining According to researchers at Eindhoven University of Technology, (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 . However, the recognition of this wanna - be scientific discipline is extremely limited within few countries . As the hype of Process Mining carried by Eindhoven University of Technology growing, more and more criticisms have emerged pointing out that Process Mining is no more than a set of algorithms which solves a specific and simple business problem: business process discovery and auxiliary evaluation methods . 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 . </Li>

Which complementary roles are typically involved in process discovery