{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T05:45:41Z","timestamp":1778651141877,"version":"3.51.4"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031822247","type":"print"},{"value":"9783031822254","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:00:00Z","timestamp":1743120000000},"content-version":"vor","delay-in-days":86,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Process models play a fundamental role in the Business Process Management lifecycle and are crucial for assessing the robustness of proposed algorithms and conducting benchmarks among different tools. However, public models are limited as they expose strategic knowledge. While some researchers developed public repositories of imperative models, there remains a lack of diverse, publicly available multi-perspective declarative models. Our work aims to bridge this gap by providing a tool for generating synthetic MP-Declare process models. We leverage Large Language Models to generate these models, ensuring coverage of diverse aspects and enhancing the resource pool for the BPM community.<\/jats:p>","DOI":"10.1007\/978-3-031-82225-4_46","type":"book-chapter","created":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T03:00:55Z","timestamp":1743303655000},"page":"624-636","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Terpsichora: A\u00a0Tool to\u00a0Generate Synthetic MP-Declare Process Models"],"prefix":"10.1007","author":[{"given":"Wesley","family":"da Silva Santos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juliana Rezende","family":"Coutinho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fernanda","family":"Bai\u00e3o","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Georges Miranda","family":"Spyrides","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"H\u00e9lio C\u00f4rtes Vieira","family":"Lopes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,28]]},"reference":[{"key":"46_CR1","doi-asserted-by":"crossref","unstructured":"Dumas, M., Rosa, L.M., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management. Springer (2018)","DOI":"10.1007\/978-3-662-56509-4"},{"key":"46_CR2","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/s00450-009-0057-9","volume":"23","author":"WMP van der Aalst","year":"2009","unstructured":"van der Aalst, W.M.P., Pesic, M., Schonenberg, H.: Declarative workflows: balancing between flexibility and support. Comput. Sci.-Res. Dev. 23, 99\u2013113 (2009)","journal-title":"Comput. Sci.-Res. Dev."},{"key":"46_CR3","doi-asserted-by":"publisher","unstructured":"Di Ciccio, C., Montali, M.: Declarative process specifications: reasoning, discovery, monitoring. In: van der Aalst, W.M.P., Carmona, J. (eds.) Process Mining Handbook. LNBIP, vol. 448, pp. 108\u2013152. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-08848-3_4","DOI":"10.1007\/978-3-031-08848-3_4"},{"key":"46_CR4","doi-asserted-by":"crossref","unstructured":"Sch\u00f6nig, S., Di Ciccio, C., Maggi, F.M., Mendling, J.: Discovery of multi-perspective declarative process models. In: Service-Oriented Computing, pp. 87\u2013103. Springer, Cham (2016)","DOI":"10.1007\/978-3-319-46295-0_6"},{"key":"46_CR5","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.eswa.2016.08.040","volume":"65","author":"A Burattin","year":"2016","unstructured":"Burattin, A., Maggi, F.M., Sperduti, A.: Conformance checking based on multi-perspective declarative process models. Expert Syst. Appl. 65, 194\u2013211 (2016)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"46_CR6","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s12599-023-00834-7","volume":"66","author":"S Feuerriegel","year":"2024","unstructured":"Feuerriegel, S., Hartmann, J., Janiesch, C., Zschech, P.: Generative AI. Bus. Inf. Syst. Eng. 66(1), 111\u2013126 (2024)","journal-title":"Bus. Inf. Syst. Eng."},{"key":"46_CR7","unstructured":"Corradini, F., Fornari, F., Polini, A., Re, B., Tiezzi, F., et al.: RePROSitory: a repository platform for sharing business PROcess modelS. In: BPM (PhD\/Demos), vol. 2420, pp. 149\u2013153 (2019)"},{"key":"46_CR8","doi-asserted-by":"publisher","unstructured":"Weske, M., Decker, G., Dumas, M., Rosa, L., Mendling, J., Reijers, H.A.: Model collection of the business process management academic initiative. Zenodo (2020). https:\/\/doi.org\/10.5281\/zenodo.3758705","DOI":"10.5281\/zenodo.3758705"},{"key":"46_CR9","doi-asserted-by":"crossref","unstructured":"Sola, D., Warmuth, C., Sch\u00e4fer, B., Badakhshan, P., Rehse, J.R., Kampik, T.: SAP signavio academic models: a large process model dataset. In: International Conference on Process Mining, pp. 453\u2013465. Springer (2022)","DOI":"10.1007\/978-3-031-27815-0_33"},{"key":"46_CR10","doi-asserted-by":"crossref","unstructured":"Alman, A., Donadello, I., Maggi, F.M., Montali, M.: Declarative process mining for software processes: the RuM toolkit and the Declare4Py python library. In: International Conference on Product-Focused Software Process Improvement, pp. 13\u201319. Springer (2023)","DOI":"10.1007\/978-3-031-49269-3_2"},{"key":"46_CR11","unstructured":"Sutton, R., Barto, A.: Reinforcement Learning: An Introduction. MIT Press (2018)"},{"key":"46_CR12","unstructured":"Skydanienko, V., Di Francescomarino, C., Ghidini, C., Maggi, F.M.: A tool for generating event logs from multi-perspective declare models. In: BPM (Dissertation\/Demos\/Industry), vol. 2196, pp. 111\u2013115 (2018)"},{"key":"46_CR13","unstructured":"Donadello, I., Maggi, F.M., Riva, F., Singh, M.: ASP-based log generation with purposes in Declare4Py. In: ICPM Doctoral Consortium\/Demo (2023)"},{"key":"46_CR14","doi-asserted-by":"crossref","unstructured":"Di Ciccio, C., Bernardi, M.L., Cimitile, M., Maggi, F.M.: Generating event logs through the simulation of declare models. In: Enterprise and Organizational Modeling and Simulation: 11th International Workshop, EOMAS: Held at CAiSE 2015, Stockholm, 2015, Selected Papers 11, pp. 20\u201336. Springer (2015)","DOI":"10.1007\/978-3-319-24626-0_2"},{"key":"46_CR15","unstructured":"Recker, J., Indulska, M., Rosemann, M., Green, P.: How good is BPMN really? Insights from theory and practice. In: Ljungberg, J., Andersson, M. (eds.) Proceedings 14th European Conference on Information Systems, Goeteborg (2006)"},{"key":"46_CR16","doi-asserted-by":"publisher","unstructured":"Burattin, A.: Artificial datasets for multi-perspective declare analysis. Zenodo (2015). https:\/\/doi.org\/10.5281\/zenodo.20030","DOI":"10.5281\/zenodo.20030"},{"key":"46_CR17","doi-asserted-by":"crossref","unstructured":"van der Aa, H., Di Ciccio, C., Leopold, H., Reijers, H.A.: Extracting declarative process models from natural language. In: Advanced Information Systems Engineering: 31st International Conference, CAiSE: Rome, Italy, 3\u20137 June 2019, Proceedings 31, pp. 365\u2013382. Springer (2019)","DOI":"10.1007\/978-3-030-21290-2_23"},{"key":"46_CR18","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1007\/s10115-019-01372-z","volume":"62","author":"D Loreti","year":"2020","unstructured":"Loreti, D., Chesani, F., Ciampolini, A., Mello, P.: Generating synthetic positive and negative business process traces through abduction. Knowl. Inf. Syst. 62, 813\u2013839 (2020)","journal-title":"Knowl. Inf. Syst."},{"key":"46_CR19","doi-asserted-by":"crossref","unstructured":"Li, X., Ni, L., Li, R., Liu, J., Zhang, M.: MaD: a dataset for interview-based bpm in business process management. In: 2023 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2023)","DOI":"10.1109\/IJCNN54540.2023.10191898"},{"key":"46_CR20","doi-asserted-by":"crossref","unstructured":"Yan, Z., Dijkman, R., Grefen, P.: Generating synthetic process model collections with properties of labeled real-life models. In: Asia Pacific Business Process Management: Second Asia Pacific Conference, AP-BPM, Brisbane, QLD, Australia, 3\u20134 July 2014. Proceedings 2, pp. 74\u201388. Springer (2014)","DOI":"10.1007\/978-3-319-08222-6_6"},{"key":"46_CR21","doi-asserted-by":"publisher","first-page":"113880","DOI":"10.1016\/j.dss.2022.113880","volume":"165","author":"C van Dun","year":"2023","unstructured":"van Dun, C., Moder, L., Kratsch, W., R\u00f6glinger, M.: ProcessGAN: supporting the creation of business process improvement ideas through generative machine learning. Decis. Support Syst. 165, 113880 (2023)","journal-title":"Decis. Support Syst."},{"key":"46_CR22","doi-asserted-by":"crossref","unstructured":"Nikolenko, S.I.: Synthetic Data for Deep Learning, vol. 174, Springer (2021)","DOI":"10.1007\/978-3-030-75178-4"},{"key":"46_CR23","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.dss.2013.06.014","volume":"56","author":"H Leopold","year":"2013","unstructured":"Leopold, H., Eid-Sabbagh, R.H., Mendling, J., Azevedo, L.G., Baiao, F.A.: Detection of naming convention violations in process models for different languages. Decis. Support Syst. 56, 310\u2013325 (2013)","journal-title":"Decis. Support Syst."},{"key":"46_CR24","doi-asserted-by":"crossref","unstructured":"Li, C., et al.: Parameter-efficient sparsity for large language models fine-tuning. In: De Raedt, L. (ed.) Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23\u201329 July 2022, pp. 4223\u20134229. ijcai.org (2022)","DOI":"10.24963\/ijcai.2022\/586"},{"issue":"3","key":"46_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3186266","volume":"51","author":"P Ivie","year":"2018","unstructured":"Ivie, P., Thain, D.: Reproducibility in scientific computing. ACM Comput. Surv. (CSUR) 51(3), 1\u201336 (2018)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"46_CR26","unstructured":"Thangarasa, I., et al.: SPDF: sparse pre-training and dense fine-tuning for large language models. In: Evans, R.J., Shpitser, I. (eds.) Uncertainty in Artificial Intelligence, UAI 2023, 31 July\u20134 August 2023, Pittsburgh, PA, USA, vol. 216, pp. 2134\u20132146. Proceedings of Machine Learning Research. PMLR (2023)"},{"key":"46_CR27","unstructured":"Xu, B., et al.: ExpertPrompting: instructing large language models to be distinguished experts. CoRR, abs\/2305.14688 (2023)"},{"key":"46_CR28","doi-asserted-by":"crossref","unstructured":"Martino, A., Iannelli, M., Truong, C.: Knowledge injection to counter large language model (LLM) hallucination. In: European Semantic Web Conference, pp. 182\u2013185. Springer (2023)","DOI":"10.1007\/978-3-031-43458-7_34"},{"key":"46_CR29","unstructured":"OpenAI. Function Calling Guide (2024). https:\/\/platform.openai.com\/docs\/guides\/function-calling. Accessed 03 July 2024"},{"key":"46_CR30","unstructured":"Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)"},{"issue":"10","key":"46_CR31","doi-asserted-by":"publisher","first-page":"e1003285","DOI":"10.1371\/journal.pcbi.1003285","volume":"9","author":"GK Sandve","year":"2013","unstructured":"Sandve, G.K., Nekrutenko, A., Taylor, J., Hovig, E.: Ten simple rules for reproducible computational research. PLoS Comput. Biol. 9(10), e1003285 (2013)","journal-title":"PLoS Comput. Biol."},{"key":"46_CR32","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.infsof.2014.07.011","volume":"58","author":"I Moreno-Montes de Oca","year":"2015","unstructured":"Moreno-Montes de Oca, I., Snoeck, M., Reijers, H.A., Rodr\u00edguez-Morffi, A.: A systematic literature review of studies on business process modeling quality. Inf. Softw. Technol. 58, 187\u2013205 (2015)","journal-title":"Inf. Softw. Technol."},{"issue":"2","key":"46_CR33","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.infsof.2009.08.004","volume":"52","author":"J Mendling","year":"2010","unstructured":"Mendling, J., Reijers, H., van der Aalst, W.: Seven process modeling guidelines (7PMG). Inf. Softw. Technol. 52(2), 127\u2013136 (2010)","journal-title":"Inf. Softw. Technol."},{"key":"46_CR34","doi-asserted-by":"publisher","first-page":"120924","DOI":"10.1016\/j.eswa.2023.120924","volume":"233","author":"A Abbad-Andaloussi","year":"2023","unstructured":"Abbad-Andaloussi, A., Burattin, A., Slaats, T., Kindler, E., Weber, B.: Complexity in declarative process models: metrics and multi-modal assessment of cognitive load. Expert Syst. Appl. 233, 120924 (2023)","journal-title":"Expert Syst. Appl."},{"key":"46_CR35","doi-asserted-by":"publisher","first-page":"101824","DOI":"10.1016\/j.is.2021.101824","volume":"102","author":"H van der Aa","year":"2021","unstructured":"van der Aa, H., Rebmann, A., Leopold, H.: Natural language-based detection of semantic execution anomalies in event logs. Inf. Syst. 102, 101824 (2021). https:\/\/doi.org\/10.1016\/j.is.2021.101824","journal-title":"Inf. Syst."},{"key":"46_CR36","unstructured":"OpenAI. Introducing structured outputs in the API (2024). https:\/\/openai.com\/index\/introducing-structured-outputs-in-the-api\/. Accessed 11 Aug 2024"},{"key":"46_CR37","unstructured":"Zheng, L., et al.: Judging LLM-as-a-judge with MT-bench and chatbot arena. Adv. Neural Inf. Process. Syst. 36 (2024)"}],"container-title":["Lecture Notes in Business Information Processing","Process Mining Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-82225-4_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T16:05:39Z","timestamp":1760889939000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-82225-4_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031822247","9783031822254"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-82225-4_46","relation":{},"ISSN":["1865-1348","1865-1356"],"issn-type":[{"value":"1865-1348","type":"print"},{"value":"1865-1356","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"28 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Process Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lyngby","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Denmark","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpm2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpmconference.org\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}