The discipline of process mining was inaugurated in the BPM community. It flourished in a world of small(er) data, with roots in the communities of software engineering and databases and applications mainly in organizational and management settings. The introduction of big data, with its volume, velocity, variety, and veracity, and the big strides in data science research and practice pose new challenges to this research field. The paper positions process mining along modern data life cycle, highlighting the challenges and suggesting directions in which data science disciplines (e.g., machine learning) may interact with a renewed process mining agenda.
CITATION STYLE
Gal, A., & Senderovich, A. (2020). Process minding: Closing the big data gap. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12168 LNCS, pp. 3–16). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58666-9_1
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