Study On Spaghetti Process Mining with Concept Drift - Swapna, N; Ramaparvathy, L (International Journal of Computer Sciences and Engineering, 2020)
Data science is the occupation of future, because organizations that are unable to use (big) data in a smart way will not survive. Process mining is a rising area that fills the gap between business process management techniques and data-centric analysis techniques such as machine learning and data mining. Process mining seeks the confrontation between event data and process models (hand-made or discovered automatically).It can be applied to any type of operational processes.Business process management is a top down approach. BPM starts by designing your process in high level model. Then you configure your system for managing and controlling the designed process. This system then coordinates work between employees and other resources in organization such that the organization is able to achieve the planned process. On the other end process mining analyzes process in a bottom-up fashion. That is we do not need to have model of process. Process mining uses the history data which is present in IT systems in the form of event data. Using this event data process mining generate process models as per the generated models organizations can take further steps to improve the models that are generated. As a result an organization cannot change the data but it can change the process in which the data is generated and hence work to meet the goals of organization. This paper gives an abstract view of process mining- algorithms used, applications, scope of process mining in diverse disciplines, research issues of Spaghetti process mining with concept drift.