An Abstraction-Based Approach for Privacy-Aware Federated Process Mining

Rafiei, Majid; Aalst, Wil M. P. van der

doi:10.1109/access.2023.3263673

Cited by 3 publications

(5 citation statements)

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“…(2021) develop an algorithm based on the Heuristic mining algorithm that doesn’t require organizations to share their event log. Instead, a trusted server initializes the mining process and aggregates results from the participating organizations, while only the global process model is shared with all participants. Use abstractions for privacy-aware federated process mining: Within the approach of Rafiei and Van Der Aalst (2023), abstractions of the actual event logs (e.g. directly follows and handover relations) are shared.…”

Section: Resultsmentioning

confidence: 99%

“…Further, the insights need to be correct, which is a particularly important challenge when only abstractions of process models are shared, merged or analyzed (Liu et al. , 2023a; Rafiei and Van Der Aalst, 2023).…”

Section: Resultsmentioning

confidence: 99%

“…Thus, it is a challenge "to not get lost" in the analytical possibilities and restrict coPM analysis to valuable opportunities. Further, the insights need to be correct, which is a particularly important challenge when only abstractions of process models are shared, merged or analyzed (Liu et al, 2023a;Rafiei and Van Der Aalst, 2023).…”

Section: Data Get Access To Distributed Datamentioning

confidence: 99%

“…(9) Use abstractions for privacy-aware federated process mining: Within the approach of Rafiei and Van Der Aalst (2023), abstractions of the actual event logs (e.g. directly follows and handover relations) are shared.…”

Section: Business Process Management Journalmentioning

confidence: 99%

“…Aksu et al (2016),Aouachria et al (2017),Gerke et al (2009b),Jacobi et al (2020),Lamghari et al (2020),Maruster et al (2003),Morales-Sandoval et al (2021),Rafiei and Van Der Aalst (2023),Talamo et al (2015),van der Aalst (2011bvan der Aalst ( , 2021 Encounter distributed knowledge and controlAouachria et al (2017), Corradini et al (2022), Hernandez-Resendiz et al (2021), Partington et al (2015), Rafiei and Van Der Aalst (2023), van der Aalst (2010), van der Aalst et al (2012) Merge intraorganizational process models Aksu et al (2016), Claes and Poels (2014), D'Iddio et al (2016a, b), Deokar and Tao (2020), Elkoumy et al (2020a, b), Engel et al (2016), Hernandez-Resendiz et al (2021), Jacobi et al (2020), Khan et al (2021), Liu et al (2019), Partington et al (2015), Rozsnyai et al (2012), T€ onnissen and Teuteberg (2019), van der Aalst (2010), van der Aalst (2011b), van der Aalst et al (2012), Zeng et al (2013)Get access to distributed dataData (The data for cross-organizational PM is usually distributed and therefore has to be collected from multiple organizations. This requires access to the data and causes further challenges in the form of varying data granularity, quality and structure.…”

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confidence: 99%

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Laying the ground for future cross-organizational process mining research and application: a literature review

Rott,

Böhm,

Krcmar

2024

BPMJ

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Purpose Process mining (PM) has emerged as a leading technology for gaining data-based insights into organizations’ business processes. As processes increasingly cross-organizational boundaries, firms need to conduct PM jointly with multiple organizations to optimize their operations. However, current knowledge on cross-organizational process mining (coPM) is widely dispersed. Therefore, we synthesize current knowledge on coPM, identify challenges and enablers of coPM, and build a socio-technical framework and agenda for future research.Design/methodology/approach We conducted a literature review of 66 articles and summarized the findings according to the framework for Information Technology (IT)-enabled inter-organizational coordination (IOC) and the refined PM framework. The former states that within inter-organizational relationships, uncertainty sources determine information processing needs and coordination mechanisms determine information processing capabilities, while the fit between needs and capabilities determines the relationships’ performance. The latter distinguishes three categories of PM activities: cartography, auditing and navigation.Findings Past literature focused on coPM techniques, for example, algorithms for ensuring privacy and PM for cartography. Future research should focus on socio-technical aspects and follow four steps: First, determine uncertainty sources within coPM. Second, design, develop and evaluate coordination mechanisms. Third, investigate how the mechanisms assist with handling uncertainty. Fourth, analyze the impact on coPM performance. In addition, we present 18 challenges (e.g. integrating distributed data) and 9 enablers (e.g. aligning different strategies) for coPM application.Originality/valueThis is the first article to systematically investigate the status quo of coPM research and lay out a socio-technical research agenda building upon the well-established framework for IT-enabled IOC.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Data Get Access To Distributed Datamentioning

confidence: 99%

Section: Business Process Management Journalmentioning

confidence: 99%

mentioning

confidence: 99%

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Laying the ground for future cross-organizational process mining research and application: a literature review

Rott,

Böhm,

Krcmar

2024

BPMJ

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From Identities to Quantities: Introducing Items and Decoupling Points to Object-Centric Process Mining

Graves,

Koren,

Rafiei

et al. 2024

Lecture Notes in Business Information Processing

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A Data-Driven Approach to Discovering Process Choreography

Hernandez-Resendiz,

Tello-Leal,

Sepúlveda

2024

Algorithms

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Implementing approaches based on process mining in inter-organizational collaboration environments presents challenges related to the granularity of event logs, the privacy and autonomy of business processes, and the alignment of event data generated in inter-organizational business process (IOBP) execution. Therefore, this paper proposes a complete and modular data-driven approach that implements natural language processing techniques, text similarity, and process mining techniques (discovery and conformance checking) through a set of methods and formal rules that enable analysis of the data contained in the event logs and the intra-organizational process models of the participants in the collaboration, to identify patterns that allow the discovery of the process choreography. The approach enables merging the event logs of the inter-organizational collaboration participants from the identified message interactions, enabling the automatic construction of an IOBP model. The proposed approach was evaluated using four real-life and two artificial event logs. In discovering the choreography process, average values of 0.86, 0.89, and 0.86 were obtained for relationship precision, relation recall, and relationship F-score metrics. In evaluating the quality of the built IOBP models, values of 0.95 and 1.00 were achieved for the precision and recall metrics, respectively. The performance obtained in the different scenarios is encouraging, demonstrating the ability of the approach to discover the process choreography and the construction of business process models in inter-organizational environments.

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An Abstraction-Based Approach for Privacy-Aware Federated Process Mining

Abstract: Funded under the Excellence Strategy of the Federal Government and the Länder. We also thank the Alexander von Humboldt Stiftung for supporting our research.

Cited by 3 publications

References 43 publications

Laying the ground for future cross-organizational process mining research and application: a literature review

Laying the ground for future cross-organizational process mining research and application: a literature review

From Identities to Quantities: Introducing Items and Decoupling Points to Object-Centric Process Mining

A Data-Driven Approach to Discovering Process Choreography

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