Process Mining in Big Data Scenario Antonia Azzini, Ernesto Damiani SESAR Lab - Dipartimento di Informatica Universit` a degli Studi di Milano, Italy antonia.azzini,ernesto.damiani@unimi.it Abstract. In the last years the management and analysis of big data generated from information systems are becoming one of the most im- portant topics in the Business Process Intelligence (BPI). In this field re- searchers show how Process Mining could become very helpful in bridging the gap between data and processes. The aim of this work is to present and discuss a brief review of the literature reporting most of the Pro- cess Mining chances that meet Big Data and the challenges carried out, showing the critical aspects and the advantages of different solutions. Keywords: process mining, big data, business process management 1 Introduction In the last years the management and analysis of big data generated from infor- mation systems are becoming one of the most important topics in the Business Process Intelligence (BPI). In fact there is a need for data scientists to trans- form event data into actionable information, but, a comprehensive data analysis is required. As reported by [7], Big data has become a board-level topic and organizations are investing heavily in related technologies, even if it is not always clear how to derive value from data. Several companies have unused process data that can be used for Process Mining. This is a side-effect of the ongoing automation of business processes, leaving digital traces of real process executions as a byproduct. According to the process science idea, all these digital traces reflect what happens in the real world and enable the application of process mining: indeed, Business Processes can be made visible to understand how these processes are actually executed, by giving a transparency that helps organizations to re-gain control over their complex business environments. They automatically creates this transparency from existing data logs, and the analysis can be easily repeated with little effort to adapt to these changes or to validate the effects of improvement initiatives. To enforce such an aspect, Some authors [7] argue that one needs to care- fully combine process-centric and data-centric approaches. This seems obvious, yet most data science (process science) approaches are process (data) agnostic. Process Mining techniques aim to bridge this gap [18, 3, 20]. This work reports a brief review of the literature showing most of the Process Mining solutions that meet Big Data. 149