Process science

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Process science is an emerging scientific field concerned with studying the nature of change.[1][2] It provides terminology and develops sets of methods and tools for studying change.[3][4] Since it is characterized by a highly interdisciplinary approach and a focus on real-world problems, process science can be considered a form of post-disciplinary research.[5] Process science is influenced by numerous fields, including computer science, social science, psychology, natural science, urban science, economics, and engineering.[6]

Process science applies the concept of process to recognize sequences of actions (activities taken by specific actors) and events (dynamics that occur in the environment) that unfold over time. Both are abstract categories formed from instantaneous observations or occurrences.[7] Process science aims to identify and optimize opportunities related to digital trace data.[8] It also focuses on collecting and developing various algorithmic techniques to analyze these data.[9]

Process science can be subdivided into four levels:[10]

  1. Discovery (descriptive process science): capturing and describing change;
  2. Explanation (explanatory process science): understanding why, how and when a change unfolds;
  3. Prediction (predictive process science): anticipating change to happen;
  4. Intervention (prescriptive process science): intervening and shaping the process into desired direction.

The study of processes has been applied to a diverse range of areas, including technological, economic, political, environmental, social, and human aspects of change.[11]

Researchers and practitioners who work on process science-related topics are referred to as process scientists.[10]

History[edit]

The term "process science" has been used in different disciplines, including computer science,[12] business process management,[13] and engineering.[6]

In a paper published in 2021, a group of scholars from diverse scientific backgrounds used the term to introduce an "interdisciplinary study of continuous change".[10] They envisioned a scientific field that gives primacy to processes at various scales. Implicitly drawing on the tenets of process philosophy,[14] process science works on the premise that the world is in a constant state of change and becoming, and scientific work should target at understanding these processes in the study of phenomena of all kinds.[10]

The founding group of the interdisciplinary field of process science includes researchers from social science, management science, and computer science. One of the founding members is the computer scientist Wil van der Aalst.[2]

Principles[edit]

Process science is built on four central tenets:[10]

  1. Processes are in the focus: Any phenomenon should be understood in terms of the processes that constitute the phenomenon.
  2. A science of discovering, explaining, and intervening into processes: The key ambitions of process science is to identify relevant processes, explain their dynamics, and to influence them.
  3. An interdisciplinary science: Process science provides an umbrella term to integrate various disciplines to contribute to discovering, explaining, and changing processes under scrutiny.
  4. A science of impact: Process science articulates a commitment to create societal impact, for example, with regards to the United Nations (UN) Sustainable Development Goals (SDGs).

References[edit]

  1. ^ "Process Science | ERCIS - European Research Center for Information Systems". www.ercis.org. Retrieved 2022-12-19.
  2. ^ a b "Process Science – Community". process-science.net. Retrieved 2022-12-19.
  3. ^ "Chair of Process and Data Science - RWTH AACHEN UNIVERSITY Chair of Process and Data Science - English". www.pads.rwth-aachen.de. Retrieved 2022-12-19.
  4. ^ "Process Science". www.ai4.uni-bayreuth.de. Retrieved 2022-12-19.
  5. ^ Pernecky, Tomas, ed. (2020). Postdisciplinary Knowledge (1st ed.). London, United Kingdom: Routledge. ISBN 978-1-032-33806-4.
  6. ^ a b Judd, Simon; Stephenson, Tom, eds. (2002). Process Science and Engineering for Water and Wastewater Treatment (1st ed.). London, United Kingdom: IWA Publishing. ISBN 9781900222754.
  7. ^ Pentland, Brian T.; Liu, Peng (2017). "Network Models of Organizational Routines: Tracing Associations between Actions". In Mir, Raza; Jain, Sanjay (eds.). The Routledge Companion to Qualitative Research in Organization Studies (1st ed.). London, United Kingdom: Routledge. ISBN 978-1-315-68610-3.
  8. ^ Lazer, David M. J.; Pentland, Alex; Watts, Duncan J.; Aral, Sinan; Athey, Susan; Contractor, Noshir; Freelon, Deen; Gonzalez-Bailon, Sandra; King, Gary; Margetts, Helen; Nelson, Alondra; Salganik, Matthew J.; Strohmaier, Markus; Vespignani, Alessandro; Wagner, Claudia (2020). "Computational social science: Obstacles and opportunities". Science. 369 (6507): 1060–1062. Bibcode:2020Sci...369.1060L. doi:10.1126/science.aaz8170. hdl:1721.1/130299. ISSN 1095-9203. PMID 32855329. S2CID 221342526.
  9. ^ "PADS-UPC". www.cs.upc.edu. Retrieved 2022-12-19.
  10. ^ a b c d e vom Brocke, Jan; van der Aalst, Wil; Grisold, Thomas; Kremser, Waldemar; Mendling, Jan; Pentland, Brian T.; Recker, Jan; Roeglinger, Maximilian; Rosemann, Michael; Weber, Barbara (2021). "Process Science: The Interdisciplinary Study of Continuous Change". SSRN. doi:10.2139/ssrn.3916817. S2CID 237452856.
  11. ^ "Home - Process Science". research.qut.edu.au. Retrieved 2022-12-19.
  12. ^ van der Aalst, Wil; Damiani, Ernesto (2015). "Processes Meet Big Data: Connecting Data Science with Process Science". IEEE Transactions on Services Computing. 8 (6): 810–819. doi:10.1109/TSC.2015.2493732. ISSN 1939-1374. S2CID 10374577.
  13. ^ Mendling, Jan (2016). "From Scientific Process Management to Process Science: Towards an Empirical Research Agenda for Business Process Management" (PDF). In Hochreiner, Christoph; Schulte, Stefan (eds.). Proceedings of the 8th ZEUS Workshop. Vienna, Austria. pp. 1–4. ISSN 1613-0073.{{cite book}}: CS1 maint: location missing publisher (link)
  14. ^ Rescher, Nicholas (2000). Process Philosophy: A Survey of Basic Issues (1st ed.). Pittsburgh, Pennsylvania: University of Pittsburgh Press. ISBN 978-0-8229-7393-5.
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