Extracting Event Data from Document-Driven Enterprise Systems

Diego Calvanese; Mieke Jans; Tahir Emre Kalayci; Marco Montali

doi:10.1007/978-3-031-34560-9_12

Extracting Event Data from Document-Driven Enterprise Systems

Diego Calvanese, Mieke Jans^*, Tahir Emre Kalayci, Marco Montali

^*Corresponding author for this work

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

Abstract

The preparation of input event data is one of the most critical phases in process mining projects. Different frameworks have been developed to offer methodologies and/or supporting toolkits for data preparation. One of these frameworks, called OnProm, relies on sophisticated semantic technologies to extract event logs from relational databases. The toolkit consists of a series of general steps, meant to work on arbitrary, legacy databases. However, in many settings, the input database is not a legacy one but is structured with conceptually understandable object types and relationships that can be effectively employed to support business users in the extraction process. This is, for example, the case for document-driven enterprise systems. In this paper, we focus on this class of systems and propose a guided approach, erprep, to support a group of business and technical users in setting up OnProm with minimal effort. We demonstrate the approach in a real-life use case.

Original language	English
Title of host publication	Advanced Information Systems Engineering - 35th International Conference, CAiSE 2023, Zaragoza, Spain, June 12-16, 2023 Proceedings
Editors	Marta Indulska, Iris Reinhartz-Berger, Carlos Cetina, Oscar Pastor
Publisher	Springer, Cham
Pages	193-209
Number of pages	17
ISBN (Electronic)	978-3-031-34560-9
ISBN (Print)	9783031345593
DOIs	https://doi.org/10.1007/978-3-031-34560-9_12
Publication status	Published - 8 Jun 2023
Event	35th International Conference on Advanced Information Systems Engineering - Zaragoza, Spain Duration: 12 Jun 2023 → 16 Jun 2023 Conference number: 35 https://caise23.svit.usj.es/

Publication series

Series	Lecture Notes in Computer Science
Volume	13901
ISSN	0302-9743

Conference

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

Keywords

Data preparation
ERP systems
event log extraction
Ontology-based event modeling

Access to Document

10.1007/978-3-031-34560-9_12

Cite this

Calvanese, D., Jans, M., Kalayci, T. E., & Montali, M. (2023). Extracting Event Data from Document-Driven Enterprise Systems. In M. Indulska, I. Reinhartz-Berger, C. Cetina, & O. Pastor (Eds.), Advanced Information Systems Engineering - 35th International Conference, CAiSE 2023, Zaragoza, Spain, June 12-16, 2023 Proceedings (pp. 193-209). Springer, Cham. https://doi.org/10.1007/978-3-031-34560-9_12

Calvanese, Diego ; Jans, Mieke ; Kalayci, Tahir Emre et al. / Extracting Event Data from Document-Driven Enterprise Systems. Advanced Information Systems Engineering - 35th International Conference, CAiSE 2023, Zaragoza, Spain, June 12-16, 2023 Proceedings. editor / Marta Indulska ; Iris Reinhartz-Berger ; Carlos Cetina ; Oscar Pastor. Springer, Cham, 2023. pp. 193-209 (Lecture Notes in Computer Science, Vol. 13901 ).

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title = "Extracting Event Data from Document-Driven Enterprise Systems",

abstract = "The preparation of input event data is one of the most critical phases in process mining projects. Different frameworks have been developed to offer methodologies and/or supporting toolkits for data preparation. One of these frameworks, called OnProm, relies on sophisticated semantic technologies to extract event logs from relational databases. The toolkit consists of a series of general steps, meant to work on arbitrary, legacy databases. However, in many settings, the input database is not a legacy one but is structured with conceptually understandable object types and relationships that can be effectively employed to support business users in the extraction process. This is, for example, the case for document-driven enterprise systems. In this paper, we focus on this class of systems and propose a guided approach, erprep, to support a group of business and technical users in setting up OnProm with minimal effort. We demonstrate the approach in a real-life use case.",

keywords = "Data preparation, ERP systems, event log extraction, Ontology-based event modeling",

author = "Diego Calvanese and Mieke Jans and Kalayci, {Tahir Emre} and Marco Montali",

note = "data source: company data that we cannot make publicly available ; 35th International Conference on Advanced Information Systems Engineering, CAiSE 2023 ; Conference date: 12-06-2023 Through 16-06-2023",

year = "2023",

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doi = "10.1007/978-3-031-34560-9_12",

language = "English",

isbn = "9783031345593",

series = "Lecture Notes in Computer Science",

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

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Calvanese, D, Jans, M, Kalayci, TE & Montali, M 2023, Extracting Event Data from Document-Driven Enterprise Systems. in M Indulska, I Reinhartz-Berger, C Cetina & O Pastor (eds), Advanced Information Systems Engineering - 35th International Conference, CAiSE 2023, Zaragoza, Spain, June 12-16, 2023 Proceedings. Springer, Cham, Lecture Notes in Computer Science, vol. 13901 , pp. 193-209, 35th International Conference on Advanced Information Systems Engineering, Zaragoza, Spain, 12/06/23. https://doi.org/10.1007/978-3-031-34560-9_12

Extracting Event Data from Document-Driven Enterprise Systems. / Calvanese, Diego; Jans, Mieke; Kalayci, Tahir Emre et al.
Advanced Information Systems Engineering - 35th International Conference, CAiSE 2023, Zaragoza, Spain, June 12-16, 2023 Proceedings. ed. / Marta Indulska; Iris Reinhartz-Berger; Carlos Cetina; Oscar Pastor. Springer, Cham, 2023. p. 193-209 (Lecture Notes in Computer Science, Vol. 13901 ).

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

TY - GEN

T1 - Extracting Event Data from Document-Driven Enterprise Systems

AU - Calvanese, Diego

AU - Jans, Mieke

AU - Kalayci, Tahir Emre

AU - Montali, Marco

N1 - Conference code: 35

PY - 2023/6/8

Y1 - 2023/6/8

N2 - The preparation of input event data is one of the most critical phases in process mining projects. Different frameworks have been developed to offer methodologies and/or supporting toolkits for data preparation. One of these frameworks, called OnProm, relies on sophisticated semantic technologies to extract event logs from relational databases. The toolkit consists of a series of general steps, meant to work on arbitrary, legacy databases. However, in many settings, the input database is not a legacy one but is structured with conceptually understandable object types and relationships that can be effectively employed to support business users in the extraction process. This is, for example, the case for document-driven enterprise systems. In this paper, we focus on this class of systems and propose a guided approach, erprep, to support a group of business and technical users in setting up OnProm with minimal effort. We demonstrate the approach in a real-life use case.

AB - The preparation of input event data is one of the most critical phases in process mining projects. Different frameworks have been developed to offer methodologies and/or supporting toolkits for data preparation. One of these frameworks, called OnProm, relies on sophisticated semantic technologies to extract event logs from relational databases. The toolkit consists of a series of general steps, meant to work on arbitrary, legacy databases. However, in many settings, the input database is not a legacy one but is structured with conceptually understandable object types and relationships that can be effectively employed to support business users in the extraction process. This is, for example, the case for document-driven enterprise systems. In this paper, we focus on this class of systems and propose a guided approach, erprep, to support a group of business and technical users in setting up OnProm with minimal effort. We demonstrate the approach in a real-life use case.

KW - Data preparation

KW - ERP systems

KW - event log extraction

KW - Ontology-based event modeling

U2 - 10.1007/978-3-031-34560-9_12

DO - 10.1007/978-3-031-34560-9_12

M3 - Conference article in proceeding

SN - 9783031345593

T3 - Lecture Notes in Computer Science

SP - 193

EP - 209

BT - Advanced Information Systems Engineering - 35th International Conference, CAiSE 2023, Zaragoza, Spain, June 12-16, 2023 Proceedings

A2 - Indulska, Marta

A2 - Reinhartz-Berger, Iris

A2 - Cetina, Carlos

A2 - Pastor, Oscar

PB - Springer, Cham

T2 - 35th International Conference on Advanced Information Systems Engineering

Y2 - 12 June 2023 through 16 June 2023

ER -

Calvanese D, Jans M, Kalayci TE, Montali M. Extracting Event Data from Document-Driven Enterprise Systems. In Indulska M, Reinhartz-Berger I, Cetina C, Pastor O, editors, Advanced Information Systems Engineering - 35th International Conference, CAiSE 2023, Zaragoza, Spain, June 12-16, 2023 Proceedings. Springer, Cham. 2023. p. 193-209. (Lecture Notes in Computer Science, Vol. 13901 ). doi: 10.1007/978-3-031-34560-9_12

Extracting Event Data from Document-Driven Enterprise Systems

Abstract

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Conference

Keywords

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