Chapter 1 INTRODUCTION
1.4. The state of the art of process mining challenges
1.4.7 Cross-Organizational mining
Buijs et al. (2012b) explored an original framework in which collections of process models can be compared with their events logs across organisations. The method is based on three types of metrics: metrics related to process models, metrics related to process executions, and metrics to compare process models and/or process executions. The authors demonstrated that even simple metrics offer useful insights regarding how to enhance processes. However, this method approach is generic and any metric can be used.
Buijs et al. (2013b) presented and compared four approaches to discover a configurable process model from a collection of event logs by extending the ETM genetic algorithm. They demonstrated that mining one configurable process model with commonalities and differences among variants is better than discovering one process model per organisation. This was the first paper in which a configurable process model was constructed based on a collection of event logs.
Zeng et al. (2013) introduced a process mining approach to discover the coordination patterns between various organisations and the process model of each organisation for cross- organizational workflow from the distributed running log. Since this log contains information about resource allocation, an RM-WE-Net model is proposed to represent the process model mined. Based on the model discovered for each organisation and the coordination patterns, a process integration approach is then proposed to obtain the model for a cross-organizational workflow. Nonetheless, the approach cannot handle some special structures such as choices or invisible tasks for one single organization.
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Buijs and Reijers (2014b) proposed a comparison procedure of similar business processes across multiple organisations based on the alignment between registered behaviour and modelled behaviour. Their analytical procedure provides the possibility to analyse the actual execution of a process within a particular organisation with its intended model, and also with the variants of the same model used by other organisations.
Schunselaar et al. (2015a) offered an implementation of YAWL into the cloud. Their implementation allows multiple non-competitive organisations to help each other, and provide through configurable process models the opportunity to support different variants of the same process.
Sebu and Ciocarlie (2015) provided, after deep analysis of multiple inter- organisational cooperation modes, a process-based approach for determining compatibility between organisations. The method stands on using process mining algorithms and graph comparison methods to determine the most suitable organisations for profitable collaborations.
Yilmaz & Karagoz (2015) developed an environment where cross-organizational process mining is applied with the unsupervised learning in which predictor variables related to performances of organisations are used. The proposed approach consists of (1) mining the process models of organisations, (2) computing performance indicators, (3) clustering organisations based on performance indicators, and finally (4) underlining discrepancies between process models to make recommendations. But, Analyzers can fail in case there are loops in the process models.
Burattin et al. (2015) proposed a possible approach toward a complete solution to support the Cross-Organizational process mining while preventing the confidentiality of the dataset and processes. The framework uses AES as a symmetric cryptosystem for strings, and Paillier for the homomorphic encryption of numerical values. Unfortunately, the available analysis plugins in ProM do not involve numerical data attributes except conformance checking plugin to support the proposed framework.
Aksu et al. (2016) proposed a generic Cross-Organizational Process Mining Framework to accurately comparing organisations based on the usage of a software product such as Enterprise Resource Planning (ERP). The framework considers as input an event log, semantics, i.e., the meaning of terms in an organisation, and organisational context, i.e., the characteristics of an organisation. Through these inputs, the methodology is able to identify what to compare between organisations and how.
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We can clearly observe that several techniques have been applied in many papers to deal with the challenge of cross-organizational mining. Most of them focused on commonality and collaboration between organisations, specifically on similarities between the process models and behaviou of organisations. However, although these approaches provide the way to successful cooperation, organisations might refuse such collaborations to avoid leakage of private information. Burattin et al. (2015) proposed a possible approach for outsourcing process mining, which is capable of preventing the confidentiality of data when operating cross-organizational mining by the encryption of strings and numerical attributes.
Nonetheless, this approach was implemented in ProM and most of the analysis plugins do not involve numerical data. So, each plugin needs to be appropriately modified and adopt a full encryption of numerical values. Concerning the other presented approaches, the confidentiality of data has not been considered. Thus, besides comparative approaches, concentrating the focus toward privacy and security problems related to cross organisational cooperation is necessary. Table 1.6 summarises the techniques applied to handle cross- organizational mining.
Table 1.6. Techniques applied for cross-organizational mining.
Paper Ref. Used methodology Outcome
Buijs et al.
(2012b)
Generic approach Compare collections of process models and their events logs across organisations Buijs et al.
(2013b)
ETM genetic algorithm based approach
Discover configurable process model based on a collection of event logs Zeng et al.
(2013)
Coordination patterns and RM_WF_Net based approach
Discover a model for a cross- organizational workflow Buijs et al.
(2014b)
Alignment based method Compare procedure of similar processes across organisations
Schunselaar et al. (2015a)
Implementation of YAWL into the cloud
Support different variants of the same process across non-competitive
organisations Sebu et al.
(2015)
Process mining algorithms and graph comparison methods based approach
Determine the most suitable organisations for profitable collaborations Yilmaz et al.
(2015)
Unsupervised learning based cross-organizational mining
Generating recommendations using cross- organizational process mining for process
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framework performance improvement
Burattin et al.
(2015)
AES and Paillier cryptography based approach
Prevent the confidentiality of data when operating cross-organizational mining Aksu et al.
(2016)
Generic Cross-Organizational Process Mining Framework
Accurately compare organisations based on their event logs and semantic and
organisational context