When Data Science meets Process Intelligence: Process Mining

Massimiliano De Leoni

When Data Science meets Process Intelligence: Process Mining
Massimiliano De Leoni
Analytics for Information Systems Group - Department of Mathematics and Computer Science
Eindhoven University of Technology - http://www.win.tue.nl/~mdeleoni/
Short bio:
Massimiliano de Leoni is an Assistant Professor of Information Systems at the Technische Universiteit Eindhoven (TU/e), the Netherlands. In 2009, he earned a PhD in Computer Engineering at La Sapienza University of Rome, Italy. He was a guest research fellow at Queensland University of Technology, Vienna University of Economics and Business, and University of Naples. His research interests are in the areas of Process-aware Information Systems and Business Process Management, predominantly on multi-perspective process mining, process-aware decision support systems as well as on visualization techniques for business process management and analysis.
Processes are everywhere, when we enter a hospital or we send a package, when we are enrolled at university or we open a bank account. They are usually supported by information systems that record the processes’ executions in so-called event logs. In the new era of Big Data, these event logs are quickly becoming richer and richer: on the one hand, information systems are more pervasive (e.g., in home automation or logistics) and connected with external systems and, on the other hand, these event logs can be augmented with additional data that come from external “data factories”, including social media, geo-referenced physical objects (e.g. via RFID tags) and questionnaires. The large availability of process data is more than just a matter of volume and all the related challenges. Compared with traditional Business Intelligence, this is an opportunity to gain actionable insights to help organizations make better business decisions and become more effective and competitive. The field that crosses Business Intelligence, Process Management and Data Science is called Process Mining. It provides new means to discover, monitor and improve processes in a variety of application domains, based on the “real” facts recorded in the event logs rather than on the subjectivity of process stakeholders and owners. This talk will give an introduction to several Process-Mining techniques aiming to better understand organizational processes and, consequently, to improve them in a variety of application domains. We will illustrate successful applications to a number of real-life case studies to, e.g., reduce costs, improve customer satisfaction or, also, diagnose unlawful deviations and detect their root-causes.
Thursday, April 19th, 2018 - 12:00
DIBRIS, Valletta Puggia, Conference Room 3rd floor


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