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

Khanbabaei, Mohammad; Sobhani, Farzad Movahedi; Alborzi, Mahmood; Radfar, Reza

doi:10.1016/j.jss.2017.11.019

Cited by 12 publications

(3 citation statements)

References 26 publications

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“…Marquez-Chamorro, Resinas, Ruiz-Cortés, and Toro (2017) employed a variety of data mining techniques such as neural network, random trees, support vector machine, and C4.5 decision tree for making appropriate decisions about the improvement of processes. Khanbabaei, Sobhani, Alborzi, and Radfar (2018) presented a framework of clustering and classification data mining techniques to extract valuable patterns hidden in the large number of processes for process improvement purposes. Table 2 compares previous studies of using data mining for process improvement with the contribution of the current study.…”

Section: Studies Related To Data Mining Approach For Process Improvmentioning

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

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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.

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Section: Studies Related To Data Mining Approach For Process Improvmentioning

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

“…As a result of all these new changes, organizations nowadays find that the traditional BPM systems present several limitations [6]. In the literature, different approaches have bean proposed to improve business process by applying diverse techniques and technologies such as recommended systems [7] [8], Ontologies [9], data analysis, data mining and process mining [10] [11], complex event processing [1] [12], Ubiquitous Computing [13], Internet of things -IoT [14] [15], just to name few.…”

Section: Introductionmentioning

confidence: 99%

IoDEP: Towards an IoT-Data Analysis and Event Processing Architecture for Business Process Incident Management

Ismaili-Alaoui

Bäına

Benali

2022

IJACSA

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IoT is becoming a hot spot area of technological innovations and economic development promises for many industries and services. This new paradigm shift affects all the enterprise architecture layers from infrastructure to business. Business Process Management (BPM) is a field among others that is affected by this new technology. To assist data and events explosion resulting, among others, from IoT, data analytic processes combined with event processing techniques, examine large data sets to uncover hidden patterns, unknown correlations between collected events, either at a very technical level (incident/anomaly detection, predictive maintenance) or at business level (customer preferences, market trends, revenue opportunities) to provide improved operational efficiency, better customer service and competitive advantages over rival organizations. In order to capitalize the business value of data and events generated by IoT sensors, IoT, Data Analytics and BPM need to meet in the middle. In this paper, we propose an end-to-end IoT-BPM integration architecture (IoDEP: IoT-Data-Event-Process) for a proactive business process incident management. A case study is presented and the obtained results from our experimentations demonstrate the benefit of our approach and allowed us to confirm the efficiency of our assumptions.

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“…These results could be used to make pedagogical interventions to prevent failures of the academically underperforming students. Many researchers have used data mining techniques in the domain of education [2][3][4][5][6][7][8][9]. Most researchers have used classification algorithms to train the classification /prediction model using a dataset consisting of students' information.…”

Section: Introductionmentioning

confidence: 99%

A novel academic performance estimation model using two stage feature selection

Chaudhury¹,

Tripathy

2020

IJEECS

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<span lang="EN-GB">Educational data mining has gained tremendous interest from researchers across the globe. Using data mining techniques in the field of education several significant findings have been made. Accurate academic performance estimation is a challenging task. In this study we have developed a novel model to estimate the academic performance of students. Techniques like conversion of categorical attributes into dummy variables, classification, two staged feature selection and an improved differential evolutionary algorithm were used. Our proposed model outperformed existing models of students’ academic performance determination and gave a new direction to it. The proposed model can help not only to reduce the number of academic failures but also help to comprehend the factors contributing to a student’s academic performance (poor, average or outstanding).Computer</span>

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

Cited by 12 publications

References 26 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

IoDEP: Towards an IoT-Data Analysis and Event Processing Architecture for Business Process Incident Management

A novel academic performance estimation model using two stage feature selection

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