About BPMDS

The BPMDS working conference, sponsored by IFIP WG8.1, has been a fertile ground for influential research and a catalyst for pioneering developments in business process modeling and support. This year, we reaffirm our commitment to fostering cutting-edge research and facilitating robust discussions that shape the future of business processes. 

Recognizing the evolving nature of our field, we introduce innovations to the submission process designed to enhance participation and engagement. We invite contributions ranging from completed research papers to idea papers. We encourage submissions that not only reflect on the established foundations of BPMDS, but also explore new paradigms, methodologies, and applications. Whether through the lens of AI and machine learning, process mining, human-centric approaches, or the implications of digital assistants such as ChatGPT, we seek to reflect the dynamic and diverse nature of our community.

Topics

BPMDS welcomes submissions on a broad range of topics, that include, but are not limited to:

• Process Mining and Data-Driven BPM: Discussing techniques and tools to obtain process-related information from event logs, including process discovery, conformance checking, and enhancement.

• AI and Machine Learning in BPM: How are artificial intelligence and machine learning being used to improve any facet of the BPM?

• Assistants and BPM: Exploring how assistants such as ChatGPT impact BPM.

• Advancements in Business Process Modeling: Exploring the latest developments, including new symbols, semantics, and modeling approaches.

• Platforms and BPM: Joining innovation and transaction with business process management.

• Case Studies on BPM in Different Industries: Sharing practical experiences and insights from applying BPM in sectors such as healthcare, finance, manufacturing, and services.

• Process Mining and Data-Driven BPM: Discussing techniques and tools to obtain process-related information from event logs, including process discovery, conformance checking, and enhancement.

• AI and Machine Learning in BPM: How are artificial intelligence and machine learning being used to improve any facet of the BPM?

• Assistants and BPM: Exploring how assistants such as ChatGPT impact BPM.

• Advancements in Business Process Modeling: Exploring the latest developments, including new symbols, semantics, and modeling approaches.

• Platforms and BPM: Joining innovation and transaction with business process management.

• Case Studies on BPM in Different Industries: Sharing practical experiences and insights from applying BPM in sectors such as healthcare, finance, manufacturing, and services.

Submissions

We invite two kinds of submissions:

● Completed research papers (15 pages, incl. references) that report on research with evaluated results.

● Idea papers (8 pages, incl. references) that present innovative concepts or directions to inspire discussion.

Submissions should adhere to the LNBIP formatting instructions at https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines.

Special Collection

From the accepted completed research papers, a selection will be invited to submit an extended version to a special collection of the Process Science Journal.

Open Science Principles

BPMDS encourages authors of research papers to follow the principles of transparency, reproducibility, and replicability. In particular, the conference supports the adoption of open data and open source principles and encourages authors to disclose (anonymized and curated) data in order to increase reproducibility and replicability.

Authors are encouraged to make research artefacts (e.g., prototypes, interview protocols, questionnaires) or the datasets (used in, or produced by, the empirical evaluation) reported in the paper available in a suitable form. To facilitate this, we kindly ask authors to include links in their manuscripts to private or public repositories where reviewers can access the associated research artefacts. This information may be presented in a dedicated section, such as  “Data availability” or “Reproducibility”. This requirement does not apply to papers that neither involve an empirical study nor a prototype implementation.

Authors who are unable or choose not to share their research artefacts and datasets with the program committee are encouraged to provide an explanation within their submitted manuscript, detailing the reasons behind their decision. This statement may be removed from the final version of the paper if it gets accepted. Possible reasons may involve privacy restrictions or non-disclosure agreements. 

To enhance the accessibility of research artefacts and datasets, authors are advised to make them accessible via public repositories (e.g., Zenodo, Figshare, GitHub, or institutional archives) under an open data license such as the CC0 dedication or the CC-BY 4.0 license. Making research artefacts and datasets available via cloud services such as Dropbox or Google Docs is discouraged due to the volatility of the links associated with these services.

Finally, authors are encouraged to self-archive their pre- and post-prints in open, preserved repositories, such as their institutional preprint repository, arXiv or other non-profit services, in line with Springer’s copyright agreement (see “License to Publish form for LNCS, CCIS or LNBIP”, §3, available at https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines).

Organization

Organizers

Luise Pufahl, Technical University of Munich, Germany

Han van der Aa, University of Vienna, Austria

Steering Committee

Selmin Nurcan, University Paris 1 Panthéon Sorbonne, France

Rainer Schmidt, Munich University of Applied Sciences, Germany
Pnina Soffer, University of Haifa, Israel

Program Committee

Coming soon