- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Process, as an important knowledge resource, must be effectively managed and improved. The main problems are the large number of processes, their specific features, and the complicated relationships between them, which all lead to the increase in complexity and create a high-dimensionality problem. Traditional process management systems are unable to manage and improve processes with a high volume of data. Data mining techniques, however, can be employed to identify valuable patterns. With the aid of these patterns, suggestions for process improvement can be presented. Further, process ontology can be applied to share the process patterns between people, facilitate the process understanding, and develop the reusability of the extracted patterns for process improvement.
This study presents a combined three-part, five-stage framework of data mining, process improvement, and process ontology. To evaluate the applicability and effectiveness of the proposed framework, a real process dataset is applied. Two clustering and classification techniques are used to discover valuable patterns as the process ontology. The output of these two techniques can be considered as the recommendations for improving the processes. The proposed framework can be exploited to support process improvement methodologies in organizations.
7. Discussion and conclusions
In organizations, there are typically many BPs with specific features, leading to an increase in the dimensionality, complexity, uncertainty, time, cost, resistance of employees, and misunderstanding of the processes. In this situation, DM techniques can support PI procedures by extracting valuable patterns hidden in the high volume of BPs for the purpose of recommending improvements. The contribution of this research work is in four main areas as follows:
First, this paper presents a broad variety of PFs in order to identify the behavior of BPs. These PFs were provided based on the vast literature related to the concepts of BPM and KM. In addition, a large real dataset including the information related to these PFs for the entire BPs in the organization was prepared. This large BP dataset along with the wide variety of PFs were considered as an input to the DM techniques in the framework developed in the current study.
Second, this paper developed a three-part, five-stage framework implementing DM techniques and a process ontology concept for PI. An actual high-volume BP dataset was employed to evaluate the applicability of the proposed framework. The proposed framework integrated three life cycles (including organizational ontology (process ontology), DM, and PI) to identify the behavior of processes. This framework can simultaneously benefit from these life cycles with a unified approach. It consists of five stages, where, in each stage, the activities of these life cycles are implemented. Further, there are mutual relationships between the activities of these life cycles.