دانلود رایگان مقاله چارچوبی برای استخراج موثر دیدگاه سازمانی فرآیندهای کسب و کار

Abstract

Process mining aims at discovering processes by extracting knowledge from event logs. Such knowledge may refer to different business process perspectives. The organisational perspective deals, among other things, with the assignment of human resources to process activities. Information about the resources that are involved in process activities can be mined from event logs in order to discover resource assignment conditions, which is valuable for process analysis and redesign. Prior process mining approaches in this context present one of the following issues: (i) they are limited to discovering a restricted set of resource assignment conditions; (ii) they do not aim at providing efficient solutions; or (iii) the discovered process models are difficult to read due to the number of assignment conditions included. In this paper we address these problems and develop an efficient and effective process mining framework that provides extensive support for the discovery of patterns related to resource assignment. The framework is validated in terms of performance and applicability.

8. Conclusions and future work

In this paper we presented a process mining framework to discover resource-aware process models. Our approach is based upon the mining approach introduced in Ref. [16], which we extended with pre-processing and post-processing phases. This increased effi- ciency while generating simplified process models that provide the same valuable information, as demonstrated by our evaluations. Since our approach relies on DPIL [20], the mining capabilities are limited to its expressiveness. Therefore, inter-case dependencies, such as those represented in the History-Based Distribution pattern, cannot be discovered. It is an interesting question for future research how such dependencies can be mined and effectively depicted in a process model. Furthermore, there might be more ways to prune discovered models that take into account more knowledge besides hierarchies and transitive reduction. By pruning more intelligently, 8 For space limitations, we refer to Ref. [38] for details on this principle. a better model could be obtained. Finally, we plan to investigate options for mapping the output to graphical process modelling notations to increase readability.