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A natural language querying interface for process mining

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Abstract

In spite of recent advances in process mining, making this new technology accessible to non-technical users remains a challenge. Process maps and dashboards still seem to frighten many line of business professionals. In order to democratize this technology, we propose a natural language querying interface that allows non-technical users to retrieve relevant information and insights about their processes by simply asking questions in plain English. In this work we propose a reference architecture to support questions in natural language and provide the right answers by integrating to existing process mining tools. We combine classic natural language processing techniques (such as entity recognition and semantic parsing) with an abstract logical representation for process mining queries. We also provide a compilation of real natural language questions and an implementation of the architecture that interfaces to an existing commercial tool: Everflow. We also introduce a taxonomy for process mining related questions, and use that as a background grid to analyze the performance of this experiment. Finally, we point to potential future work opportunities in this field.

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Data availability

The question dataset analysed during the current study is available in  https://ic.unicamp.br/~luciana.barbieri/20220306-classifiedpmquestions.csv.

Notes

  1. https://everflow.ai/

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Acknowledgements

We would like to thank Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, for providing the financial support for this work.

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Authors and Affiliations

  1. Institute of Computing, University of Campinas, Campinas, Brazil

    Luciana Barbieri & Edmundo Madeira

  2. Pega Systems, São Paulo, Brazil

    Kleber Stroeh

  3. Chair of Process and Data Science, RWTH Aachen University, Aachen, Germany

    Wil van der Aalst

  4. Fraunhofer Institute for Applied Information Technology FIT, Sankt Augustin, Germany

    Wil van der Aalst

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  1. Luciana Barbieri
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  2. Edmundo Madeira
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  3. Kleber Stroeh
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  4. Wil van der Aalst
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Correspondence to Luciana Barbieri.

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Barbieri, L., Madeira, E., Stroeh, K. et al. A natural language querying interface for process mining. J Intell Inf Syst 61, 113–142 (2023). https://doi.org/10.1007/s10844-022-00759-9

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  • DOI: https://doi.org/10.1007/s10844-022-00759-9

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