Generating event logs from non-process-aware systems enabling business process mining

  • Authors:

  • Ricardo Perez-Castillo;Barbara Weber;Jakob Pinggera;Stefan Zugal;Ignacio Garcia-Rodriguez de Guzman;Mario Piattini

  • Affiliations:

  • Alarcos Research Group, University of Castilla-La Mancha, Ciudad Real, Spain;Quality Engineering, University of Innsbruck, Innsbruck, Austria;Quality Engineering, University of Innsbruck, Innsbruck, Austria;Quality Engineering, University of Innsbruck, Innsbruck, Austria;Alarcos Research Group, University of Castilla-La Mancha, Ciudad Real, Spain;Alarcos Research Group, University of Castilla-La Mancha, Ciudad Real, Spain

  • Venue:
  • Enterprise Information Systems
  • Year:
  • 2011

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Abstract

As information systems age they become legacy information systems (LISs), embedding business knowledge not present in other artefacts. LISs must be modernised when their maintainability falls below acceptable limits but the embedded business knowledge is valuable information that must be preserved to align the modernised versions of LISs with organisations' real-world business processes. Business process mining permits the discovery and preservation of all meaningful embedded business knowledge by using event logs, which represent the business activities executed by an information system. Event logs can be easily obtained through the execution of process-aware information systems (PAISs). However, several non-process-aware information systems also implicitly support organisations' business processes. This article presents a technique for obtaining event logs from traditional information systems (without any in-built logging functionality) by statically analysing and modifying LISs. The technique allows the modified systems to dynamically record event logs. The approach is validated with a case study involving a healthcare information system used in Austrian hospitals, which shows the technique obtains event logs that effectively and efficiently enable the discovery of embedded business processes. This implies the techniques provided within the process mining field, which are based on event logs, may also be applied to traditional information systems.