On Integrating Data Mining into Business Processes

Wegener, Dennis; Rüping, Stefan

doi:10.1007/978-3-642-12814-1_16

Cited by 14 publications

(8 citation statements)

References 12 publications

(20 reference statements)

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“…it has been identified that CRISP-DM lacks in the deployment phase [4], in guidance towards implementing particular tasks of data mining methodologies [5], and in the definition of phases important for engineering projects [6]. In addition, we detected that many redundancies and inefficiencies exists when following the CRISP standard data mining process model in parallel to standard BPM approaches [2].…”

Section: Introductionmentioning

confidence: 91%

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On Reusing Data Mining in Business Processes - A Pattern-Based Approach

Wegener

Rüping

2011

Business Process Management Workshops

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Abstract. Today's business applications demand high flexibility in processing information and extracting knowledge from data. Thus, data mining becomes more and more an integral part of operating a business. However, the integration of data mining into business processes still requires a lot of coordination and manual adjustment. This paper aims at reducing this effort by reusing successful data mining solutions. We describe a novel approach on facilitating the integration based on process patterns for data mining and demonstrate that these patterns allow for easy reuse and can significantly speed up the process of integration. We empirically evaluate our approach in a case study of fraud detection in the health care domain.

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Section: Introductionmentioning

confidence: 91%

“…In previous work [2] we presented an initial discussion on how to integrate data mining into business processes. Here, we focus on how to enable the reuse of existing solutions that have been proven to be successful.…”

Section: Introductionmentioning

confidence: 99%

On Reusing Data Mining in Business Processes - A Pattern-Based Approach

Wegener

Rüping

2011

Business Process Management Workshops

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“…Rupnik and Jaklic () developed a three‐stage structure for applying data mining in operational processes. Wegener and Rüping () determined the roles of the business, information technology, and data mining experts via the integration of data mining with process reengineering. Wegener and Rüping () developed a concept to reuse the capabilities of successful data mining solutions for processes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Rupnik and Jaklic (2009) developed a three-stage structure for applying data mining in operational processes. Wegener and Rüping (2010) Mitschang (2014) employed a recommendation system for process improvement using data mining. Ghattas, Soffer, and Peleg (2014) applied data mining techniques to enhance the performance of processes.…”

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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“…In this secondary study carried out by Dennis Wegener, it was pointed that a concept for theintegration that facilitates the modelling of the data mining process within the business process as well as the technical deployment into the business is a crucial factor of success for business today [1].Business process management (BPM) is a discipline combiningon Integrating Data Mining into Business Processes and software capabilities and business expertise to accelerate business process improvementand to facilitate business innovation [6]. Integrating data mining in business processes involves different groups of users with different responsibilities,knowledge and background.…”

Section: Introductionmentioning

confidence: 99%

Development of BPMN Architecture using Integration of Data Mining

Saluja

Singh

Chaturvedi³

2018

IJERMT

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Abstractata mining is an important factor of success for modern business processes. Modern Integrating data mining with business solutions will improve the business process model (BPM) and enhance the overall business process management framework. The proposed architecture promises to provide great support for flexible design, deployment and managementof business processes. Incorporating services of data mining in order to choose the appropriate business model, determining missing standards and deploying machine learning techniques in an applicable manner is a challenging task discussed in the paper. Theproposed work describes the overall contribution of data mining services and validates the novelty in architecture by defining user roles for business, decision mining and IT standards. Supervised and unsupervised learning technique is used for determining similarity between the proposed model and the previously stored models.

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On Integrating Data Mining into Business Processes

Cited by 14 publications

References 12 publications

On Reusing Data Mining in Business Processes - A Pattern-Based Approach

On Reusing Data Mining in Business Processes - A Pattern-Based Approach

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

Development of BPMN Architecture using Integration of Data Mining

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