Detection of Dataflow Anomalies in Business Process An Overview of Modeling Approaches

Chadli, Najat; Kabbaj, Mohamed Issam; Bakkoury, Zohra

doi:10.1145/3289402.3289537

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…To gather the data needed to model a process, event logs and Open Platform Communications Unified Architecture (OPC/UA) communication protocol are used (Conforti et al 2017;Cavalieri, Salafia, and Scroppo 2019;van der Aalst 2018). Log automaton, Petri nets and data flow matrix-based approaches deal with detection of data anomalies, while statistical analysis aims to reduce data dimensionality and perform 'data preparation' activities (Guzikowski et al 2010;Conforti et al 2017;Chadli et al 2018). Ontology, NoSQL databases and relational data modelling support 'data representation', while Extensible Stylesheet Language Transformations enables 'data transformation' (Gruber 2009;Meyer et al 2013;Krenczyk and Jagodzinski 2015;Meyer et al 2015;Hassani and Ghannouchi 2017).…”

Section: Planmentioning

confidence: 99%

Linking data science to lean production: a model to support lean practices

Pozzi

Cannas

Ciano

2021

International Journal of Production Research

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The literature discusses data science (DS) as a very promising set of techniques and tools to support lean production (LP) practices. DS could aid manufacturing companies in transforming massive real-time data into meaningful knowledge, increasing process transparency and product quality information and supporting improvement activities through data-driven decision-making. However, no attempt has been made in the literature to formalise the links between DS and LP practices. Thus, this study aims to overcome this gap by clarifying the DS techniques and tools that can support LP practices and how to apply them. This study employs a quantitative bibliometric method-specifically, a keyword co-occurrence network analysis-on a set of papers extracted from Scopus. The results obtained allowed the researchers to identify a set of DS techniques and tools that can support LP practices and to develop a model to guide their implementation based on the typical improvement implementation stages of the plan-do-check-act cycle. The model shows how to use DS techniques and tools in LP for: identifying areas for improvement and subsequent implementation (plan); enabling a better knowledge and process management (do); identifying/predicting potential problems and employing statistical process control (check); providing remedial actions and effectively applying process improvement (act).

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Section: Planmentioning

confidence: 99%

Linking data science to lean production: a model to support lean practices

Pozzi

Cannas

Ciano

2021

International Journal of Production Research

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“…Modeling and validation of data flow are very important for anomaly detection; thus, Chadli et al [2] explain three approaches that are used for detecting data flow anomalies and its proper method and tools. Tao and Fang [11] opt for workflow nets with tables (WFT-nets) to model workflow systems and detect inconsistent data.…”

Section: Application Of Data Flowmentioning

confidence: 99%

“…The data flow that is represented by data objects (depicted by parallelogram) and is associated with tasks as their input or output, respectively, is often overlooked. Recently, many researchers are starting to take the data flow seriously and use it to create greater value especially, in the data privacy protection and data anomaly detection tasks [2]. Referring to Figure 1, the black part is the business process diagram, which illustrates the execution sequence of the business process.…”

Section: Introductionmentioning

confidence: 99%

Recovering Latent Data Flow from Business Process Model Automatically

Wang

Ali

et al. 2022

Wireless Communications and Mobile Computing

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Process-driven applications evolve rapidly through the interaction between executable BPMN (Business Process Modeling and Notation) models, business tasks, and external services. Given these components operate on some shared process data, it is imperative to recover the latent data by visiting relation, which is known as data flow among these tasks. Data flow will benefit some typical applications including data flow anomaly checking and data privacy protection. However, in most cases, the complete data flow in a business process is not explicitly defined but hidden in model elements such as form declarations, variable declarations, and program code. Some methods to recovering data flow based on process model analysis of source code have some drawbacks; i.e., for security reasons, users do not want to provide source code but only encapsulated methods; therefore, data flows are difficult to analyze. We propose a method to generate running logs that are used to produce a complete data flow picture combined with the static code analysis method. This method combines the simple and easy-to-use characteristics of static code analysis methods and makes up for the shortcomings of static code analysis methods that cannot adapt to complex business processes, and as a result, the analyzed data flow is inaccurate. Moreover, a holistic framework is proposed to generate the data flow graph. The prototype system designed on Camunda and Flowable BPM (business process management) engine proves the applicability of the solution. The effectiveness of our method is validated on the prototype system.

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Detection of Dataflow Anomalies in Business Process An Overview of Modeling Approaches

Cited by 2 publications

References 7 publications

Linking data science to lean production: a model to support lean practices

Linking data science to lean production: a model to support lean practices

Recovering Latent Data Flow from Business Process Model Automatically

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