Business process anomaly detection using ontology-based process modelling and Multi-Level Class Association Rule Learning

Sarno, Riyanarto; Sinaga, Fernandes

doi:10.1109/ic3ina.2015.7377738

Cited by 32 publications

(28 citation statements)

References 4 publications

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“…Sarno and Sinaga [11] used ontology to capture the business process anomalies in comparison with the company's principles. Unlike the approach that is proposed by Figueiredo and de Oliveira [10], Sarno and Sinaga [11] approach detects anomalies automatically. Yet, the change management is implemented manually based on the captured anomalies.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent and Adaptive Model for Change Management

Alshahrani¹

2020

IJACSA

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The continuous and rapid changes that are taking place in the world today makes the change management crucial to any organization. The existing change management models bridge the gap between motivation, planning, and implementation. These models are highly significant as the organizational change is not a rare event anymore, but it is an ongoing process, and the 'business as usual' model becomes insignificant to most organizations. To automatize the theoretical model of change management, an intelligent and adaptive model for change management is developed in this paper, which takes into consideration all the positive and negative effects (factors) that may take place at any time and any place internally (internal factors) or externally (external factors). Based on these factors, accordingly, the proposed model can efficiently find a reasonable solution that adapts to the existing situation to avoid any failure of organizational management. The proposed system is built based on a decision support system (DSS) with inputs that represent the influencing factors and an output that represents feedback on the method of management. In this paper, the proposed change management model has been verified, and the results have been reported accordingly.

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Section: Related Workmentioning

confidence: 99%

An Intelligent and Adaptive Model for Change Management

Alshahrani¹

2020

IJACSA

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“…Sarno et al [22] proposed a Multi-Level Class Association Rule Learning (ML-CARL) to identify fraud in business process. It is aided by the Semantic Web Rule Language (SWRL) Rule that utilized to ensure the conformance among the typical business process model Standard Operating Procedure (SOP) and event logs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…and (22) here 0 < α < 1 and γ > 0 are constants. The optimal selection of every bat is chosen dependent upon the fitness function (accurateness of the classifier).…”

Section: The Position Of the I Th Rule Of Pefcars Ruleset Be Represenmentioning

confidence: 99%

Proportion frequency occurrence count with bat algorithm (FOCBA) for rule optimization and mining of proportion equivalence fuzzy constraint class association rules (PEFCARs)

Ramesh¹,

Saravanan²

2018

PEN

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Fuzzy Class Association Rules (FCARs) play an important role in decision support systems and have thus been extensively studied. Mining the important rules in FCARs becomes very difficult task, so Enhanced Equivalence Fuzzy Class Rule tree (EEFCR-tree) algorithm is proposed in this work. However, a major weakness of FCARs Miner is that when the number of constrained rules in a given class dominates the total constrained rules; its performance becomes slower than the normal method. To solve this problem this paper proposes a Proportion of Constraint Class Estimation (PPCE) algorithm for mining Enhanced Proportion Equivalence Fuzzy Constraint Class Association Rules (EPEFCARs) in order to save memory usage, run time and accuracy. Then, Proportion Frequency Occurrence count with Bat Algorithm (PFOCBA) is proposed for pruning rules which much satisfying the class constraints. Finally, an efficient algorithm is proposed for mining PEFCARs rules. Experimental results show that the proposed EPEFCR-tree algorithm is more efficient than Enhanced Equivalence Fuzzy Class Rule tree (EEFCRtree), Novel Equivalence Fuzzy Class Rule tree (NECR-tree) Miner results are measured in terms of run time, accuracy and memory usage. Experiments show that the proposed method is faster than existing methods.

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“…It can create a set of data into smaller sections of tree-related decisions that gradually developed in the same time [ [20]. The end result of the decision tree is a tree with leaf nodes and nodes decisions.…”

Section: E Decision Tree Classificationmentioning

confidence: 99%

Scalable Attack Analysis of Business Process based on Decision Mining Classification

Rahmawati¹,

Sarno²

2017

EECSI

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Banking crime is one of the widespread phenomena in 2016 are closely associated with the used of computer-based technology and internet networks that constantly evolving. One of them is the burglary of customer accounts through the internet banking facility. To overcome this, we need a method of how to detect a conspiracy of bank burglary case of customer accounts. The way to scalable is by get a mining decision to get a decision tree and from the decision tree to get a decision attribute value to determine the level of anomalies. Then of all the attributes decision point is calculated rate of fraud. The rate of fraud is classified through level of security of attack by the attacker then entropy gain is used to calculate the relative effort between the level of attacks in the decision tree. The results show that the method could classify three levels of attacks and the corresponding entropy gains. The paper uses decision trees algorithm, alpha++ and dotted chart analysis to analyze an attack that can be scalable. The results of the analysis show that the accuracy achieved by 0.87%.

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Business process anomaly detection using ontology-based process modelling and Multi-Level Class Association Rule Learning

Cited by 32 publications

References 4 publications

An Intelligent and Adaptive Model for Change Management

An Intelligent and Adaptive Model for Change Management

Proportion frequency occurrence count with bat algorithm (FOCBA) for rule optimization and mining of proportion equivalence fuzzy constraint class association rules (PEFCARs)

Scalable Attack Analysis of Business Process based on Decision Mining Classification

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