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Assessing event correlation in non-process-aware information systems

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Abstract

Many present-day companies carry out a huge amount of daily operations through the use of their information systems without ever having done their own enterprise modeling. Business process mining is a well-proven solution which is used to discover the underlying business process models that are supported by existing information systems. Business process discovery techniques employ event logs as input, which are recorded by process-aware information systems. However, a wide variety of traditional information systems do not have any in-built mechanisms with which to collect events (representing the execution of business activities). Various mechanisms with which to collect events from non-process-aware information systems have been proposed in order to enable the application of process mining techniques to traditional information systems. Unfortunately, since business processes supported by traditional information systems are implicitly defined, correlating events into the appropriate process instance is not trivial. This challenge is known as the event correlation problem. This paper presents an adaptation of an existing event correlation algorithm and incorporates it into a technique in order to collect event logs from the execution of traditional information systems. The technique first instruments the source code to collect events together with some candidate correlation attributes. Based on several well-known design patterns, the technique provides a set of guidelines to support experts when instrumenting the source code. The event correlation algorithm is subsequently applied to the data set of events to discover the best correlation conditions, which are then used to create event logs. The technique has been semi-automated to facilitate its validation through an industrial case study involving a writer management system and a healthcare evaluation system. The study demonstrates that the technique is able to discover an appropriate correlation set and obtain well-formed event logs, thus enabling business process mining techniques to be applied to traditional information systems.

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Acknowledgments

This work was supported by the FPU Spanish Program and the R&D projects ALTAMIRA (PII2I09-0106-2463), PEGASO/MAGO (TIN2009-13718-C02-01), MAESTRO (Alarcos Quality Center) and MOTERO (JCCM and FEDER, PEII11-0366-9449). Additionally, this work was supported by the University of Innsbruck.

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Authors and Affiliations

  1. Instituto de Tecnologías y Sistemas de Información (ITSI), University of Castilla-La Mancha, Paseo de la Universidad 4, 13071 , Ciudad Real, Spain

    Ricardo Pérez-Castillo, Ignacio García-Rodríguez de Guzmán & Mario Piattini

  2. University of Innsbruck, Technikerstraße 21a, 6020 , Innsbruck, Austria

    Barbara Weber & Jakob Pinggera

Authors
  1. Ricardo Pérez-Castillo
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  2. Barbara Weber
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  3. Ignacio García-Rodríguez de Guzmán
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  4. Mario Piattini
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  5. Jakob Pinggera
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Corresponding author

Correspondence to Ricardo Pérez-Castillo.

Additional information

Communicated by Dr. Tony Clark, Balbir Barn, Alan Brown, and Florian Matthes.

Appendix 1: Reference and discovered business process models

Appendix 1: Reference and discovered business process models

This appendix provides the reference business process models that are supported by the two traditional information systems under study. Owing to space limitations, each figure depicts the reference model and different variations of the models discovered as regards the reference model in terms of business tasks that were not discovered by means of the different correlations sets.

Firstly, Fig. 9 depicts the reference model of the AELG-member systems and presents the four variations of business process models discovered using the correlation sets A, B, C and D (cf. Sect. 4.4). The missing business tasks for each correlation data set are highlighted and the correlation set ID are specified (A, B, C and D).

Fig. 9
figure 9

Reference and discovered business process models of the AELG-Members system

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Secondly, Fig. 10 provides the same information for the CHES system. In this case, two different business process models were discovered using correlation sets E and F (cf. Sect. 4.4).

Fig. 10
figure 10

Reference and discovered business process models of the CHES

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Pérez-Castillo, R., Weber, B., de Guzmán, I.GR. et al. Assessing event correlation in non-process-aware information systems. Softw Syst Model 13, 1117–1139 (2014). https://doi.org/10.1007/s10270-012-0285-5

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