CODIFICATION OF KNOWLEDGE IN BUSINESS PROCESS IMPROVEMENT PROJECTS

In times of globalization, new technologies and high market transparency, companies search for ways to raise the efficiency of their business processes and achieve long-term customer relationships. Therefore enterprises are strongly devoted to business process improvement (BPI) initiatives. However, in times of globally spanning inter-organizational business processes, conducting BPI initiatives is particularly challenging du to the necessary diverse and distributed knowledge. Successful BPI projects require the participation of a variety of employees who are directly involved in a business process. The employees have tacit process knowledge that needs to be transformed into explicit knowledge to derive improvement opportunities in a BPI project. To reach this, a set of easy to understand and well-structured BPI techniqus is required to encourage employees to participate in corresponding initiatives. Further, the codification of the results gained in such initiatives is decisive to enable their proper documentation, communication and processing. \ \ The paper at hand introduces a BPI roadmap for coordinating the structured use of BPI techniqus in a project. The roadmap is based upon a set of formal conceptual model types for codifying the results generated by each techniqu. In addition, reports are specified that process the model information and facilitate the communication and documentation of the results. The presented approach thus contributes to the systematic transformation of employees´ tacit process knowledge to explicit knowledge in the course of BPI initiatives. By applying the roadmap in a use case, its benefits for BPI initiatives are illustrated.

Business Process Retrieval Based on Behavioral Semantics

This paper develops a framework for retrieving business processes considering search requirements based on behavioral semantics properties; it presents a framework called "BeMantics" for retrieving business processes based on structural, linguistics, and behavioral semantics properties. The relevance of the framework is evaluated retrieving business processes from a repository, and collecting a set of relevant business processes manually issued by human judges. The "BeMantics" framework scored high precision values (0.717) but low recall values (0.558), which implies that even when the framework avoided false negatives, it prone to false positives. The highest pre- cision value was scored in the linguistic criterion showing that using semantic inference in the tasks comparison allowed to reduce around 23.6 % the number of false positives. Using semantic inference to compare tasks of business processes can improve the precision; but if the ontologies are from narrow and specific domains, they limit the semantic expressiveness obtained with ontologies from more general domains. Regarding the perform- ance, it can be improved by using a filter phase which indexes business processes taking into account behavioral semantics propertie

Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis (Extended)

This extended paper presents 1) a novel hierarchy and recursion extension to the process tree model; and 2) the first, recursion aware process model discovery technique that leverages hierarchical information in event logs, typically available for software systems. This technique allows us to analyze the operational processes of software systems under real-life conditions at multiple levels of granularity. The work can be positioned in-between reverse engineering and process mining. An implementation of the proposed approach is available as a ProM plugin. Experimental results based on real-life (software) event logs demonstrate the feasibility and usefulness of the approach and show the huge potential to speed up discovery by exploiting the available hierarchy.Comment: Extended version (14 pages total) of the paper Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis. This Technical Report version includes the guarantee proofs for the proposed discovery algorithm