{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T17:37:23Z","timestamp":1792949843491,"version":"4.5.0"},"publisher-location":"Cham","reference-count":16,"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>State-of-the-art solutions for process mining rely\u00a0on proprietary, domain-specific languages to query data recorded during business process execution. To support common analysis tasks, these languages focus on the definition of queries for behavioural patterns. Yet, the use of domain-specific languages for process mining\u00a0has drawbacks: they require specific user training, lead to a decoupling of\u00a0the query models for (i) data extraction and transformation, and (ii) the actual analysis, and induce engineering overhead through the development of a dedicated\u00a0query engine. In this work, we therefore explore the use of standard SQL for process mining tasks. In particular, we demonstrate that the SQL concepts for\u00a0row pattern recognition as realised by the  clause are sufficient\u00a0to capture queries for behavioural patterns as specified in the SIGNAL language by SAP Signavio as well as the Process Querying Language (PQL) by Celonis. Based on a discussion of the respective language features, we outline\u00a0a translation of SIGNAL and PQL queries into standard SQL. This way,\u00a0we provide the basis for the adoption of widely used, general purpose\u00a0query engines for process mining tasks.<\/jats:p>","DOI":"10.1007\/978-3-031-82225-4_2","type":"book-chapter","created":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T03:03:31Z","timestamp":1743303811000},"page":"18-30","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["One Language to\u00a0Rule Them All: Behavioural Querying of\u00a0Process Data Using SQL"],"prefix":"10.1007","author":[{"given":"Jakob","family":"Brand","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timotheus","family":"Kampik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cem","family":"Okulmus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias","family":"Weidlich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,28]]},"reference":[{"issue":"9","key":"2_CR1","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1016\/j.compind.2012.06.002","volume":"63","author":"A Awad","year":"2012","unstructured":"Awad, A., Sakr, S.: On efficient processing of bpmn-q queries. Comput. Ind. 63(9), 867\u2013881 (2012)","journal-title":"Comput. Ind."},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Carmona, J., van Dongen, B.F., Solti, A., Weidlich, M.: Conformance Checking - Relating Processes and Models. Springer, Heidelberg (2018)","DOI":"10.1007\/978-3-319-99414-7"},{"issue":"1","key":"2_CR3","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1007\/s10619-019-07270-1","volume":"38","author":"RM Dijkman","year":"2020","unstructured":"Dijkman, R.M., Gao, J., Syamsiyah, A., van Dongen, B.F., Grefen, P., ter Hofstede, A.H.M.: Enabling efficient process mining on large data sets: realizing an in-database process mining operator. Distrib. Parallel Datab. 38(1), 227\u2013253 (2020)","journal-title":"Distrib. Parallel Datab."},{"key":"2_CR4","doi-asserted-by":"publisher","unstructured":"Francescomarino, C.D., Tonella, P.: The BPMN visual query language and process querying framework. In: Polyvyanyy, A. (ed.) Process Querying Methods, pp. 181\u2013218. Springer, Heidelberg (2022). https:\/\/doi.org\/10.1007\/978-3-030-92875-9_7","DOI":"10.1007\/978-3-030-92875-9_7"},{"key":"2_CR5","unstructured":"Kampik, T., L\u00fccke, A., Horstmann, J., Wheeler, M., Eickhoff, D.: Signal \u2013 the sap signavio analytics query language (2023)"},{"key":"2_CR6","doi-asserted-by":"publisher","unstructured":"Kampik, T., Okulmus, C.: Expressive power and complexity results for signal, an industry-scale process query language. In: BPM Forum 2024. LNBIP. Springer, Heidelberg (2024). https:\/\/doi.org\/10.1007\/978-3-031-70418-5_1","DOI":"10.1007\/978-3-031-70418-5_1"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Michels, J., et al.: The new and improved sql: 2016 standard. SIGMOD Rec. 47(2), 51\u201360 (2018)","DOI":"10.1145\/3299887.3299897"},{"key":"2_CR8","doi-asserted-by":"publisher","unstructured":"de\u00a0Murillas, E.G.L., Reijers, H.A., van\u00a0der Aalst, W.M.P.: Data-aware process oriented query language. In: Polyvyanyy, A. (ed.) Process Querying Methods, pp. 49\u201383. Springer, Heidelberg (2022). https:\/\/doi.org\/10.1007\/978-3-030-92875-9_3","DOI":"10.1007\/978-3-030-92875-9_3"},{"key":"2_CR9","doi-asserted-by":"publisher","unstructured":"P\u00e9rez-\u00c1lvarez, J.M., D\u00edaz, A.C., Parody, L., Quintero, A.M.R., G\u00f3mez-L\u00f3pez, M.T.: Process instance query language and the process querying framework. In: Polyvyanyy, A. (ed.) Process Querying Methods, pp. 85\u2013111. Springer, Heidelberg (2022). 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