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.

Business intelligence,business process management,operational processes, process mining, event data,process models,concept drift,Spaghetti process mining.

[1]
Martin Prodel, Vincent Augusto, “Optimal processmining for large complex event
logs”,2018 IEEE transactions on automation science and engineering.

[2]
Na Liu, Jiwei Huang, LizhenCui, “A Framework for online process Concept Drift
Detection from Event Streams”, In  the
proceedings of 2018 IEEE International Conference on Services
Computing,pp.105-113.

[3]
Manoj Kumar,Likewin Thomas,Annappa, “Distilling Lasanga from Spaghetti
Processes”, In Proceedings of ACM Woodstock conference, Hong Kong, Hong Kong,
March 25-27, 2017 (ISMSI ’17),

[4]
Wil M.P. vander Aalst ,Verlag Berlin Heidelberg, “Process Mining Analyzing
Sphagetti process”2016 Springer pp.411-428

[5]
Hind R’Bigui ,“The state-of-the-art of business process mining challenges” ,In
proceedings of International Journal of Business Process Integration and
Management 2017.

[6]
Fitri Almira Yasmin et.al , “Process Enhancement in Process Mining: A
Literature Review” , Research Gate Conference paper Dec18.

[7]
Tugba Gurgen Erdogan and Ayca Tarhan A ,“ Goal-Driven Evaluation Method Based
On Process Mining for Healthcare Processes” ,Appl. Sci. 2018,

[8]
Josef K.J. Martens, “Professional use of Process Mining for analyzing Business
Processes”,2013-CEUR workshop proceedings .

 [9] Ufuk Celik , “Process Mining Tools Comparison”,
Online Academic Journal of Information Technology 2018

[10]
W.M.P. van der Aalst, “Finding Structure in Unstructured Processes:The Case for
Process  Mining    Lasanga” ,In the proceedings of Application
of Concurrency to System Design, ACSD 2007.