Description

As more and more companies are embracing Big data, it has become apparent that the ultimate challenge is to relate massive amounts of event data to processes that are highly dynamic. To unleash the value of event data, events need to be tightly connected to the control and management of operational processes. However, the primary focus of Big data technologies is currently on storage, processing, and rather simple analytical tasks. Big data initiatives rarely focus on the improvement of end-to-end processes. To address this mismatch, we advocate a better integration of data science, data technology and process science. Data science approaches tend to be process agonistic whereas process science approaches tend to be model-driven without considering the “evidence” hidden in the data. Process mining aims to bridge this gap. This paper discusses the interplay between data science and process science and relates process mining to Big data technologies, service orientation, and cloud computing.