Processes Meet Big Data: Connecting Data Science with Process Science

Wil M. P. van der Aalst · Ernesto Damiani

97 citations0/0 files storedIEEE Transactions on Services Computing

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Business Process Modeling and Analysis Big Data and Business Intelligence Big Data Technologies and Applications

Abstract

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 editorial discusses the interplay between data science and process science and relates process mining to Big data technologies, service orientation, and cloud computing.

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References

2011Big data: The next frontier for innovation, competition, and productivity 2015Principles of Scientific Management, The 1993Reengineering the Corporation 2004Workflow mining: discovering process models from event logs 2018Fundamentals of Business Process Management 2011Process Mining 2011Process Mining: Discovery, Conformance and Enhancement of Business Processes 2011The World’s Technological Capacity to Store, Communicate, and Compute Information

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2020Seven Paradoxes of Business Process Management in a Hyper-Connected World 2016Static and Dynamic Process Change 2018Recomposing conformance: Closing the circle on decomposed alignment-based conformance checking in process mining 2020Mapping the Patient’s Journey in Healthcare through Process Mining 2019Scalable Discovery of Hybrid Process Models in a Cloud Computing Environment 2023A Direct Data Aware LSTM Neural Network Architecture for Complete Remaining Trace and Runtime Prediction

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