Evaluation and Extracting Factual Software Architecture of Distributed System by Process Mining Techniques

Sahlabadi, Mahdi; Sahlabadi, Amir Hossein; Muniyandi, Ravie Chandren; Shukur, Zarina

doi:10.17576/apjitm-2017-0602-08

Cited by 5 publications

(4 citation statements)

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“…The maximum fitness measurement value is used as an assessment criterion, i.e., f = 1. The following event logs have been used for evaluation (ProM, 2010) (Sahlabadi et al, 2014;2017).…”

Section: Resultsmentioning

confidence: 99%

Distributed Learning Automata Approach for Workflow Mining: Discovering Process Model Using Condensate Drops Method

Naderifar

Shukur

Sahran

2019

Journal of Computer Science

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An information system is a process of collecting, processing, storing and distributing information, which leads to efficient decisionmaking and control in organizations. Examples of information systems include classical management systems, systems for workflow management, systems for case handling and middleware. Information systems collect information concerning important people, locations and other important matters in an organization and store relevant events in some form of structure. Based on event logs, from information systems, the discovery of process models can be made automatically by process mining techniques, without having an a priori model. By learning from the event logs, process mining aims to discover, monitor and improve processes. This paper proposes a method to discover a process model based on distributed learning automata and the condensate approach. In this proposed method, each event in the log is called a drop, which had its condensate and can be combined with other condensates. Each drop is connected to other drops and become a larger drop. All of those drops would obtain reward if it represents sequence of an event log. The evaluation results demonstrated that the proposed method could detect various patterns in the event log and discover a more efficient process model in terms of fitness, total node and total path of the mined process model.

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“…The maximum fitness measurement value is used as an assessment criterion, i.e., f = 1. The following event logs have been used for evaluation (ProM, 2010) (Sahlabadi et al, 2014;2017).…”

Section: Resultsmentioning

confidence: 99%

Distributed Learning Automata Approach for Workflow Mining: Discovering Process Model Using Condensate Drops Method

Naderifar

Shukur

Sahran

2019

Journal of Computer Science

Self Cite

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“…It comprises two key steps: simulation using Colored Petri Nets (CPN) and thread discovery employing Process Mining (PM) techniques. One of our cases has undergone implementation using this method and has been subsequently published [32] ].…”

Section: Developmentmentioning

confidence: 99%

LPMSAEF: Lightweight process mining-based software architecture evaluation framework for security and performance analysis

Sahlabadi,

Muniyandi,

Shukur

et al. 2024

Heliyon

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“…Ideally, N is all traces in L that correspond to the possible firing sequences of (N, M) and M indicates the current marking. Function γ defines the technique so-called "Play-in" [54].…”

Section: Sa Models Discoverymentioning

confidence: 99%

Process Mining Discovery Techniques for Software Architecture Lightweight Evaluation Framework

Sahlabadi¹,

Muniyandi²,

Shukur³

et al. 2023

Computers, Materials &Amp; Continua

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This research recognizes the limitation and challenges of adapting and applying Process Mining as a powerful tool and technique in the Hypothetical Software Architecture (SA) Evaluation Framework with the features and factors of lightweightness. Process mining deals with the largescale complexity of security and performance analysis, which are the goals of SA evaluation frameworks. As a result of these conjectures, all Process Mining researches in the realm of SA are thoroughly reviewed, and nine challenges for Process Mining Adaption are recognized. Process mining is embedded in the framework and to boost the quality of the SA model for further analysis, the framework nominates architectural discovery algorithms Flower, Alpha, Integer Linear Programming (ILP), Heuristic, and Inductive and compares them vs. twelve quality criteria. Finally, the framework's testing on three case studies approves the feasibility of applying process mining to architectural evaluation. The extraction of the SA model is also done by the best model discovery algorithm, which is selected by intensive benchmarking in this research. This research presents case studies of SA in service-oriented, Pipe and Filter, and component-based styles, modeled and simulated by Hierarchical Colored Petri Net techniques based on the cases' documentation. Process mining within this framework deals with the system's log files obtained from SA simulation. Applying process mining is challenging, especially for a SA evaluation framework, as it has not been done yet. The research recognizes the problems of process mining adaption to a hypothetical lightweight SA evaluation framework and addresses these problems during the solution development.

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Evaluation and Extracting Factual Software Architecture of Distributed System by Process Mining Techniques

Cited by 5 publications

References 0 publications

Distributed Learning Automata Approach for Workflow Mining: Discovering Process Model Using Condensate Drops Method

Distributed Learning Automata Approach for Workflow Mining: Discovering Process Model Using Condensate Drops Method

LPMSAEF: Lightweight process mining-based software architecture evaluation framework for security and performance analysis

Process Mining Discovery Techniques for Software Architecture Lightweight Evaluation Framework

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