Applying data mining techniques to business process reengineering based on simultaneous use of two novel proposed approaches

Ghanadbashi, Saeedeh; Khanbabaei, Mohammad; Abadeh, Mohammad Saniee

doi:10.1504/ijbpim.2013.056963

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, Mathew and George () used data mining for process reengineering and extracting knowledge hidden in the high volume of process dataset. Ghanadbashi, Khanbabaei, and Abadeh () presented a literature review of using data mining in different phases of process reengineering and developed a combinational model of data mining, knowledge management, and process monitoring architecture.…”

Section: Literature Reviewmentioning

confidence: 99%

Applying clustering and classification data mining techniques for competitive and knowledge‐intensive processes improvement

Khanbabaei

Alborzi

Sobhani

et al. 2019

Knowl Process Manag

Self Cite

View full text Add to dashboard Cite

Processes as one of the valuable knowledge resources can create sustainable competitive advantages in organizations. There is a large number of processes in organizations. They generate a high volume of process data that leads to the high‐dimensionality problems, complex relationships, dynamic changes, and difficulties in the understanding of the process by human resources. Traditional process improvement methodologies have weaknesses in environment with the large number of processes. Data mining techniques can support process improvement in this environment. They can recommend the improvement suggestions through extracting valuable patterns from a high volume of the process dataset. Recently, knowledge‐intensive processes have been increasingly concentrated in the field of process improvement. These types of processes can induce a competitive behavior over the other processes. The main problem is the improvement of competitive and knowledge‐intensive processes in a high volume of process dataset. The main purpose of this paper is to present a model to identify the behavior of competitive and knowledge‐intensive processes and recommend improvement suggestions. For this purpose, data mining techniques are applied to extract valuable patterns hidden in a high volume of process dataset. In this regard, K‐means clustering and C5 classification algorithms are applied to extract valuable patterns. A real process dataset was used to evaluate the effectiveness and applicability of the model. The results confirmed that the proposed model can apply data mining techniques to support competitive and knowledge‐intensive process improvement in a high volume of process dataset.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Applying clustering and classification data mining techniques for competitive and knowledge‐intensive processes improvement

Khanbabaei

Alborzi

Sobhani

et al. 2019

Knowl Process Manag

Self Cite

View full text Add to dashboard Cite

show abstract

Developing an integrated framework for using data mining techniques and ontology concepts for process improvement

Khanbabaei

Sobhani

Alborzi

et al. 2018

Journal of Systems and Software

View full text Add to dashboard Cite

How to Predict the Innovation to SMEs? Applying the Data Mining Process to the Spinner Innovation Model

2023

View full text Add to dashboard Cite

Despite the importance of small and medium-sized enterprises (SMEs) for the growth and development of companies, the high failure rate of these companies persists, and this correspondingly demands the attention of managers. Thus, to boost the company success rate, we may deploy certain approaches, for example predictive models, specifically for the SME innovation. This study aims to examine the variables that positively shape and contribute towards innovation of SMEs. Based on the Spinner innovation model, we explore how to predict the innovation of SMEs by applying the variables, namely knowledge creation, knowledge transfer, public knowledge management, private knowledge management and innovation. This study applied the data mining technique according to the cross industry standard process for data mining (CRISP-DM) method while the Statistical Package for the Social Sciences (SPSS_Version28) served to analyze the data collected from 208 SME employees in Oporto, Portugal. The results demonstrate how the Spinner innovation model positively influences the contributions of the SMEs. This SME-dedicated model fosters the creation of knowledge between internal and external interactions and increases the capacity to predict the SME innovation by 56%.

show abstract

Applying data mining techniques to business process reengineering based on simultaneous use of two novel proposed approaches

Cited by 3 publications

References 0 publications

Applying clustering and classification data mining techniques for competitive and knowledge‐intensive processes improvement

Applying clustering and classification data mining techniques for competitive and knowledge‐intensive processes improvement

Developing an integrated framework for using data mining techniques and ontology concepts for process improvement

How to Predict the Innovation to SMEs? Applying the Data Mining Process to the Spinner Innovation Model

Contact Info

Product

Resources

About