Supporting Provenance and Data Awareness in Exploratory Process Mining

Francesca Zerbato; Andrea Burattin; Hagen Völzer; Paul Nelson Becker; Elia Boscaini; Barbara Weber

doi:10.1007/978-3-031-34560-9_27

Supporting Provenance and Data Awareness in Exploratory Process Mining

Francesca Zerbato (Corresponding author)
, Andrea Burattin
, Hagen Völzer
, Paul Nelson Becker
, Elia Boscaini
, Barbara Weber

Process Analytics on Multi-Dimensional Data

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

14 Citations (Scopus)

Abstract

Like other analytic fields, process mining is complex and knowledge-intensive and, thus, requires the substantial involvement of human analysts. The analysis process unfolds into many steps, producing multiple results and artifacts that analysts need to validate, reproduce and potentially reuse. We propose a system supporting the validation, reproducibility, and reuse of analysis results via analytic provenance and data awareness. This aims at increasing the transparency and rigor of exploratory process mining analysis as a basis for its stepwise maturation. We outline the purpose of the system, describe the problems it addresses, derive requirements and propose a design satisfying these requirements. We then demonstrate the feasibility of the central aspects of the design.

Original language	English
Title of host publication	Advanced Information Systems Engineering. CAiSE 2023
Editors	Marta Indulska, Iris Reinhartz-Berger, Carlos Cetina, Oscar Pastor
Publisher	Springer
Pages	454-470
Number of pages	17
ISBN (Electronic)	978-3-031-34560-9
ISBN (Print)	978-3-031-34559-3
DOIs	https://doi.org/10.1007/978-3-031-34560-9_27
Publication status	Published - 8 Jun 2023
Event	35th International Conference on Advanced Information Systems Engineering, CAiSE 2023 - Zaragoza, Spain Duration: 12 Jun 2023 → 16 Jun 2023 https://caise23.svit.usj.es/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13901 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	35th International Conference on Advanced Information Systems Engineering, CAiSE 2023
Abbreviated title	CAiSE 2023
Country/Territory	Spain
City	Zaragoza
Period	12/06/23 → 16/06/23
Internet address	https://caise23.svit.usj.es/

Funding

Acknowledgments. Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2023 Internet of Production – 390621612. We thank Jan-Gustav Michnia for his initial exploration of the FHE library CONCRETE [8]. We followed an abstract research methodology [21] to structure and organize our research collaborations. Acknowledgments. Research funded by the Australian Research Council (grant DP180102839), the European Research Council (PIX Project), and the Estonian Research Council (grant PRG1226). Acknowledgements. This research is supported by the Estonian Research Council (PRG1226) and the European Research Council (PIX Project). Acknowledgment. This work was supported by the NSF, grant number 1952225 and the German Federal Ministry of Education and Research (BMBF), grant number 16DII133. Acknowledgements. A.G.S. was supported by the Valencian Innovation Agency and Innovation through the OGMIOS project (INNEST/2021/57), the Gen-eralitat Valenciana through the CoMoDiD project (CIPROM/2021/023), and the Spanish State Research Agency through the DELFOS (PDC2021-121243-I00,MICIN/AEI/10.13039/501 100011033) and SREC (PID2021-123824OB-I00) projects, and co-financed with ERDF and the European Union Next Generation EU/PRTR. Work funded by the European Research Council (PIX Project). Acknowledgement. This work has been partly funded by SAP SE in the context of the research project “Building Semantic Models for the Process Mining Pipeline”. Acknowledgments. This work is partially funded by Industrial Doctorates from Gen- Acknowledgements. We thank the anonymous reviewers for their valuable feedback. This work has been partially funded by MUR PRIN project 2017TWRCNB SEDUCE, and the PNRR MUR project VITALITY (ECS00000041) Spoke 2 ASTRA - Advanced Space Technologies and Research Alliance. Acknowledgements. This work was supported in part by Centro para el Desar-rollo Tecnológico Industrial (CDTI) under Grant IDI-20210948 (STRATO, nuevaS herramienTas para la modeRnizAción de sisTemas heredadOs). Acknowledgements. Research supported by MCIN/AEI/10.13039/501100011033 and the “European Union NextGenerationEU/PRTR” under contract PID2021-125438OB-I00. Xabier Garmendia enjoys a grant from the University of the Basque Country - PIF20/236. Acknowledgement. This research was supported by ERDF “CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence” (No. CZ.02.1.01/0.0/0.0/16 019/0000822). Computational resources were supplied by the project “e-Infrastruktura CZ” (e-INFRA CZ LM2018140) supported by the Ministry of Education, Youth and Sports of the Czech Republic. A special thanks to the e-INFRA CZ SensitiveCloud team for their participation in the study. This research was also co-founded by the European Union under Grant Agreement No. 101087529. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. Our work is supported by the Bavarian Research Foundation (grant no. AZ-1390-19). This work is part of the ProMiSE project, funded by the Swiss National Science Foundation under Grant No.: 200021 197032. Acknowledgement. This work was supported by the European Social Fund via ”ICT programme” measure, the European Regional Development Fund, and the programme Mobilitas Pluss (2014-2020.4.01.16-0024). Acknowledgement. This work is supported by National Key Research and Development Program (2020AAA0107800), National Natural Science Foundation of China Acknowledgement. The research leading to these results received funding from French Research Agency through the ANR-19-CE25-0003 KOALA project and from the Norwegian Research Council through the DILUTE project (Grant No. 262854/F20). Acknowledgements. This work is supported by the H2020 projects TAILOR: Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization – EC Grant Agreement number 952215 – and SPICE: Social Cohesion, Participation and Inclusion through Cultural Engagement – EC Grant Agreement number 870811, as well as by the Italian PNRR MUR project PE0000013-FAIR. Acknowledgements. This work has been partially funded through the Austrian Acknowledgements. This work has been partly funded by SAP SE in the context of the research project “Building Semantic Models for the Process Mining Pipeline” and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project number 277991500. We are grateful to the whole team that worked collaboratively for the success of CAiSE 2023 and its related events. We sincerely thank the General Chairs, Carlos Cetina and Oscar Pastor, the Local Organization Chair, Raul Lapeña; and the Proceedings Chair, Pierluigi Plebani, for facilitating our work as Program Chairs. We are also thankful to Giancarlo Guizzardi, Bran Selic and Pnina Soffer for their inspirational keynote presentations. We further wish to thank: the Forum Chairs, Cristina Cabanillas and Francisca Pérez; the Workshop Chairs, Pnina Soffer and Marcela Ruiz; the Tutorial Chairs, Fabi-ano Dalpiaz and Jelena Zdravkovic; the Panel Chairs, Hajo A. Reijers and Stefanie Rinderle-Ma; the Doctoral Consortium Chairs, Daniel Méndez and Raimundas Matule-vicius; the Journal-first Track Chairs, Jan Mendling and Lola Burgueño; the Publicity Chairs, Jolita Ralyté, Guilherme H. Travassos, Anna Segooa and Anna Kalenkova; the Web and Social Media Chairs, Jorge Echeverria and Africa Domingo; the Sustainability Chairs, Sergio España and Monica Vitali; the Sponsor Chairs, Pedro Valderas and Antonio Ruiz; the Student Chairs, Selmin Nurcan and Estefania Serral; the PhD Award Chairs, John Krogstie and Camille Salinesi; and the Research Project Exhibition Chairs, Giovanni Giachetti, Jaime Font, Lorena Arcega and José Fabián Reyes.

Funders	Funder number
Valencian Innovation Agency and Innovation	INNEST/2021/57
National Science Foundation	1952225
European Union's Horizon 2020 - Research and Innovation Framework Programme
Australian Research Council	DP180102839
Deutsche Forschungsgemeinschaft	390621612, 277991500, EXC-2023
Agence Nationale de la Recherche	ANR-19-CE25-0003
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung	200021 197032
National Natural Science Foundation of China	61832014, 61832004
Bundesministerium für Bildung und Forschung	16DII133
Ministero dell’Istruzione, dell’Università e della Ricerca	ECS00000041, 2017TWRCNB
Norges Forskningsråd	262854/F20
European Regional Development Fund	CZ LM2018140, 2014-2020.4.01.16-0024, CZ.02.1.01/0.0/0.0/16 019/0000822

Keywords

Analytic Provenance
Data Awareness
Exploratory Analysis
Process Mining
System Requirements and Design
User Support

Access to Document

10.1007/978-3-031-34560-9_27

Cite this

Zerbato, F., Burattin, A., Völzer, H., Becker, P. N., Boscaini, E., & Weber, B. (2023). Supporting Provenance and Data Awareness in Exploratory Process Mining. In M. Indulska, I. Reinhartz-Berger, C. Cetina, & O. Pastor (Eds.), Advanced Information Systems Engineering. CAiSE 2023 (pp. 454-470). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13901 LNCS). Springer. https://doi.org/10.1007/978-3-031-34560-9_27

Zerbato, Francesca ; Burattin, Andrea ; Völzer, Hagen et al. / Supporting Provenance and Data Awareness in Exploratory Process Mining. Advanced Information Systems Engineering. CAiSE 2023. editor / Marta Indulska ; Iris Reinhartz-Berger ; Carlos Cetina ; Oscar Pastor. Springer, 2023. pp. 454-470 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{1f97fcceba9845848cac7cee8de4e008,

title = "Supporting Provenance and Data Awareness in Exploratory Process Mining",

abstract = "Like other analytic fields, process mining is complex and knowledge-intensive and, thus, requires the substantial involvement of human analysts. The analysis process unfolds into many steps, producing multiple results and artifacts that analysts need to validate, reproduce and potentially reuse. We propose a system supporting the validation, reproducibility, and reuse of analysis results via analytic provenance and data awareness. This aims at increasing the transparency and rigor of exploratory process mining analysis as a basis for its stepwise maturation. We outline the purpose of the system, describe the problems it addresses, derive requirements and propose a design satisfying these requirements. We then demonstrate the feasibility of the central aspects of the design.",

keywords = "Analytic Provenance, Data Awareness, Exploratory Analysis, Process Mining, System Requirements and Design, User Support",

author = "Francesca Zerbato and Andrea Burattin and Hagen V{\"o}lzer and Becker, \{Paul Nelson\} and Elia Boscaini and Barbara Weber",

year = "2023",

month = jun,

day = "8",

doi = "10.1007/978-3-031-34560-9\_27",

language = "English",

isbn = "978-3-031-34559-3",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "454--470",

editor = "Marta Indulska and Iris Reinhartz-Berger and Carlos Cetina and Oscar Pastor",

booktitle = "Advanced Information Systems Engineering. CAiSE 2023",

address = "Germany",

note = "35th International Conference on Advanced Information Systems Engineering, CAiSE 2023, CAiSE 2023 ; Conference date: 12-06-2023 Through 16-06-2023",

url = "https://caise23.svit.usj.es/",

}

Zerbato, F, Burattin, A, Völzer, H, Becker, PN, Boscaini, E & Weber, B 2023, Supporting Provenance and Data Awareness in Exploratory Process Mining. in M Indulska, I Reinhartz-Berger, C Cetina & O Pastor (eds), Advanced Information Systems Engineering. CAiSE 2023. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13901 LNCS, Springer, pp. 454-470, 35th International Conference on Advanced Information Systems Engineering, CAiSE 2023, Zaragoza, Spain, 12/06/23. https://doi.org/10.1007/978-3-031-34560-9_27

Supporting Provenance and Data Awareness in Exploratory Process Mining. / Zerbato, Francesca (Corresponding author); Burattin, Andrea; Völzer, Hagen et al.
Advanced Information Systems Engineering. CAiSE 2023. ed. / Marta Indulska; Iris Reinhartz-Berger; Carlos Cetina; Oscar Pastor. Springer, 2023. p. 454-470 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13901 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Supporting Provenance and Data Awareness in Exploratory Process Mining

AU - Zerbato, Francesca

AU - Burattin, Andrea

AU - Völzer, Hagen

AU - Becker, Paul Nelson

AU - Boscaini, Elia

AU - Weber, Barbara

PY - 2023/6/8

Y1 - 2023/6/8

N2 - Like other analytic fields, process mining is complex and knowledge-intensive and, thus, requires the substantial involvement of human analysts. The analysis process unfolds into many steps, producing multiple results and artifacts that analysts need to validate, reproduce and potentially reuse. We propose a system supporting the validation, reproducibility, and reuse of analysis results via analytic provenance and data awareness. This aims at increasing the transparency and rigor of exploratory process mining analysis as a basis for its stepwise maturation. We outline the purpose of the system, describe the problems it addresses, derive requirements and propose a design satisfying these requirements. We then demonstrate the feasibility of the central aspects of the design.

AB - Like other analytic fields, process mining is complex and knowledge-intensive and, thus, requires the substantial involvement of human analysts. The analysis process unfolds into many steps, producing multiple results and artifacts that analysts need to validate, reproduce and potentially reuse. We propose a system supporting the validation, reproducibility, and reuse of analysis results via analytic provenance and data awareness. This aims at increasing the transparency and rigor of exploratory process mining analysis as a basis for its stepwise maturation. We outline the purpose of the system, describe the problems it addresses, derive requirements and propose a design satisfying these requirements. We then demonstrate the feasibility of the central aspects of the design.

KW - Analytic Provenance

KW - Data Awareness

KW - Exploratory Analysis

KW - Process Mining

KW - System Requirements and Design

KW - User Support

UR - https://www.scopus.com/pages/publications/85164020384

U2 - 10.1007/978-3-031-34560-9_27

DO - 10.1007/978-3-031-34560-9_27

M3 - Conference contribution

SN - 978-3-031-34559-3

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 454

EP - 470

BT - Advanced Information Systems Engineering. CAiSE 2023

A2 - Indulska, Marta

A2 - Reinhartz-Berger, Iris

A2 - Cetina, Carlos

A2 - Pastor, Oscar

PB - Springer

T2 - 35th International Conference on Advanced Information Systems Engineering, CAiSE 2023

Y2 - 12 June 2023 through 16 June 2023

ER -

Zerbato F, Burattin A, Völzer H, Becker PN, Boscaini E, Weber B. Supporting Provenance and Data Awareness in Exploratory Process Mining. In Indulska M, Reinhartz-Berger I, Cetina C, Pastor O, editors, Advanced Information Systems Engineering. CAiSE 2023. Springer. 2023. p. 454-470. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-34560-9_27

Supporting Provenance and Data Awareness in Exploratory Process Mining

Abstract

Publication series

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this