Process Mining in Data Science: A Literature Review

Abstract

Today, many organizations are required to resolve the difficulties associated with data mining techniques, however, there are many challenges pertaining the accomplishment of information retrieval as a massive quantity of data is inconsistent and therefor forcing the industrialists to perform rapidly to retain afloat. Innovative scientific systems and procedures support to quickly reply inquiries that can indicate growth in productivity, improving efficiency and excellence of services. Although, many tools have been developed for handling of data in real-time and overall led the experienced user to handle real communication software and correctly interpret the results cleverly, efficient and dominant concrete approaches exist such as process mining that ultimately allows an organization to benefit from the data warehouses in their system. Process mining provides insights at time of analyzing processes of particular problems, and also performs the conformance checking of processes aiming at finding bottlenecks. This paper prescribes the primary inside of mining informations systems and explain the various deterministic techniques in process mining used in the auto-learning process model generated from the events data. We also review all modern techniques and alogorithms used in process mining.