International Journal of Computing and Digital Systems
In a distributed peer-to-peer network, blockchain technology enables the secure transmission of d... more In a distributed peer-to-peer network, blockchain technology enables the secure transmission of digital assets. This is done based on intellectual agreement capabilities. Indeed, blockchain world has developed into a tool for managing transversal processes on an unbiased platform. Process mining has evolved as a well-known toolkit for comprehending different organization processes. Recently, researchers developed strategies for resolving the issue of collecting reliable data gathered from blockchains in order to improve the examination of blockchain applications employing process mining. There is yet to be a clear assessment of the utility of process mining on public blockchain event logs. In this paper, we will validate the applicability of process mining on public blockchain event logs by treating ChickenHunt data set. ChickenHunt is a competitive game that is operated as a Decentralized application on Ethereum blockchain network.
In the company environment, the management team is responsible for producing normative models. Th... more In the company environment, the management team is responsible for producing normative models. The normative model is considered a standard model that aims at auditing all business processes in the same context. In this regard, the audit operation encompasses four process mining activities, in a hybrid evaluation (offline and online), which are the detect, the check, the compare, and the promote activities. This is still well performed for structured business processes. Otherwise, complex processes may deviate from the initial defined normative model context. Indeed, the latter must be refined for more precise results. Therefore, the combination of human knowledge, control-flow discovery algorithms, and process mining activities is required. To this end, we present a technique for reducing the complexity of unstructured process models (Spaghetti process models) into structured ones (Lasagna process models). This framework outputs a refined normative model for improving the future Bu...
Business process modelling languages are a very prominent mechanism to understand companies’ busi... more Business process modelling languages are a very prominent mechanism to understand companies’ business processes. This mechanism allows managers to communicate and transversal information through a semantic flow for business process improvement. Examples of Business Process modelling languages are Unified Modeling Language (UML) or Business Process Modeling Notation (BPMN). UML presents alternative views to model company’s functionalities. While, BPMN still have a very abstracted level to model Business Processes. In this context, there are many BPMN extensions to break down its high-level of abstraction. However, most of existing BPMN extensions model a significant increase in business process complexity that deviate it from its crucial objective. This put forward the difficulty of detailing control flow related to information systems without reaching complexity. To that end, we propose our TM4BPMN extension for breaking-down BPMN high abstraction to low-level, to maintain BPMN faci...
Executing loosely structured processes generate unstructured behaviours. Thus, an Unstructured Bu... more Executing loosely structured processes generate unstructured behaviours. Thus, an Unstructured Business Process (UBP) still has more issues that are difficult to be analysed and to be understood due to its complexity and variability. Moreover, the need of an instantiate response is clearly appeared in operational systems. Therefore, it is required to study related challenges that can be acquired during the transition from the structured BP to the unstructured one. In this context, process mining plays a dominant role to understand business process complexity using event data resulted from business process execution. Mainly, this paper treats three challenges related to unstructured BPs. The first challenge is how to support UBPs at runtime using process mining techniques. The second challenge is how to manage UBP variability taking into consideration variant conditions. The third challenge is how to adapt dynamically UBPs according to the company business rules and conditions.
Transdisciplinary Journal of Engineering & Science
Process mining is a young discipline that provides a variety of techniques for extracting data fr... more Process mining is a young discipline that provides a variety of techniques for extracting data from recorded event logs. In this context, process model is simulated, from event logs, to help enterprises to better understand their business processes. In this context, the discovery technique has always been a significant focus of process mining research. But despite the availability of several proposed approaches, participated in this process model visualization, we observe as the number of activities increases, the representation of the entire control flow becomes quite tedious. Indeed, event logs complexity make the process model visualization more difficult in terms of logical links and number of causal relationships between activities. Therefore, we propose a new method for simplifying the process model control flow visualization. This is done by using mainly string similarity algorithms to visually summarizes the number of activities, sequence of execution, relative significance,...
International Journal of E-Services and Mobile Applications, 2022
The information retrieval system is a set of resources and tools that allow users to search for i... more The information retrieval system is a set of resources and tools that allow users to search for information in a given domain. This system permits users to perform their research according to their objectives in diverse ways producing different behaviors. Even users with the same objective may follow different paths and stand different sub-processes, which are introduced as self-defined Business Processes that vary in terms of structure, objective, and result. This puts forward the difficulty of obtaining and studying these user’s behaviors. This paper targets the problem of representing and managing self-defined business process variability. A special interest is given to the use of process mining to deal with this challenge. In this regard, a case study about citizens in interaction with the Electronic Administration is presented, to discover and manage variability of this process type. The main result is a set of recommendations to end users.
Revista de Informática Teórica e Aplicada, Jan 19, 2021
The refined process mining framework contains a set of activities that use extracted information ... more The refined process mining framework contains a set of activities that use extracted information from event logs, discovered models and normative ones. Among these activities, we find those dealing with running events in a Structured Business Process (SBP) context, which are the Detect, the Predict and the Recommend activities. These three activities are nominated as operational support system that performs well on SBP while, it stills a challenging task for an Unstructured Business Process (UBP), because of its complex structure. In this regard, a special interest is given to the use of existing process mining techniques to analyse unstructured processes, from the extraction of a process model based on event data to recommendations at a later stage. To this end, we propose the orchestration of process mining activities into an UBP operational support approach, through the following phases: 1.Preparing Normative model, 2.Detect violations, 3.Preparing predictive model and Predictions and 4.Preparing the recommender model and Recommendations.
Learning and Analytics in Intelligent Systems, Dec 1, 2019
Supply chain is a network between a company and its suppliers to produce and distribute a product... more Supply chain is a network between a company and its suppliers to produce and distribute a product to the final customer. This network includes different activities, people, entities, etc. The interaction between these elements provides a cross-organization Business Process. It has mainly treated with process mining techniques, to handle the resulted process instances as event logs. These events are obtained by implementing the auto identification technology within the supply chain related to materials or personal. By doing so, the provided events are not simply presented, they emerged more challenges like complexity and data confidentiality. Therefore, in this work we develop a descriptive framework, based on a literature study, to answer supply chain challenges related to the process mining field. This is done, by implementing process mining within the supply chain, using auto identification technology and taking into consideration recent challenges related to cross-organization Business Process.
Annual International Conference on Arab Women in Computing, 2019
Process mining has emerged as a research field that focuses on the analysis of processes using ev... more Process mining has emerged as a research field that focuses on the analysis of processes using event data. Process mining is a current topic that reveals several challenges, the most important of which have defined in the Process Mining Manifesto [1]. However, none of the published works have mentioned the progress of process challenges from data-intensive system to knowledge-intensive system related to process mining field. Therefore, the objective of this paper is to provide researchers with the recent challenges emerged during the passage from data-intensive system to knowledge-intensive system.
Nowadays, Big data promises automated actionable knowledge creation and predictive models for use... more Nowadays, Big data promises automated actionable knowledge creation and predictive models for use by humans and computers. Therefore, one of the principal responsibilities of a data scientist is to make reliable predictions based on data, particularly, when the amount of available data is enormous. To do so, it is useful if some of the analysis can be automated and used process mining techniques.In this context, the ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to timely counteract to undesired situations, in order to prevent losses. Therefore, the techniques focus on predicting the remaining time influence other predictive process monitoring dimensions like: cost, delays, etc, i.e., predicting the remaining time, to accomplish an activity, helps respectively to predict the suitable resource and the next executing probable event. Indeed, a considerable number ...
2018 International Conference on Intelligent Systems and Computer Vision (ISCV)
Improving business processes and maintaining acceptable levels of performance are main factors in... more Improving business processes and maintaining acceptable levels of performance are main factors in the success of any organization. Related to BPM (Business Process Management) [1] approaches, a variety of methodologies and techniques are available. These approaches include business process improvement methodologies (BPIM) [2] and process mining [3]. Actually, with BPM life cycle a continuous methodology of improvement business processes is defined, starting by the design phase and ending with the optimize phase. In this paper, we present a new vision BPM life cycle whose content differs from the classical one [4]. On each phase, we define a set of improvement indicators. These indicators recast business processes for best optimization results.
Process discovery technique aims at automatically generating a process model that accurately desc... more Process discovery technique aims at automatically generating a process model that accurately describes a Business Process (BP) based on event data. Related discovery algorithms consider recorded events are only resulting from an operational BP type. While the management community defines three BP types, which are: Management, Support and Operational. They distinguish each BP type by different proprieties like the main business process objective as domain knowledge. This puts forward the lack of process discovery technique in obtaining process models according to business process types (Management and Support). In this paper, we demonstrate that business process types can guide the process discovery technique in generating process models. A special interest is given to the use of process mining to deal with this challenge.
International Journal of Computing and Digital Systems
In a distributed peer-to-peer network, blockchain technology enables the secure transmission of d... more In a distributed peer-to-peer network, blockchain technology enables the secure transmission of digital assets. This is done based on intellectual agreement capabilities. Indeed, blockchain world has developed into a tool for managing transversal processes on an unbiased platform. Process mining has evolved as a well-known toolkit for comprehending different organization processes. Recently, researchers developed strategies for resolving the issue of collecting reliable data gathered from blockchains in order to improve the examination of blockchain applications employing process mining. There is yet to be a clear assessment of the utility of process mining on public blockchain event logs. In this paper, we will validate the applicability of process mining on public blockchain event logs by treating ChickenHunt data set. ChickenHunt is a competitive game that is operated as a Decentralized application on Ethereum blockchain network.
In the company environment, the management team is responsible for producing normative models. Th... more In the company environment, the management team is responsible for producing normative models. The normative model is considered a standard model that aims at auditing all business processes in the same context. In this regard, the audit operation encompasses four process mining activities, in a hybrid evaluation (offline and online), which are the detect, the check, the compare, and the promote activities. This is still well performed for structured business processes. Otherwise, complex processes may deviate from the initial defined normative model context. Indeed, the latter must be refined for more precise results. Therefore, the combination of human knowledge, control-flow discovery algorithms, and process mining activities is required. To this end, we present a technique for reducing the complexity of unstructured process models (Spaghetti process models) into structured ones (Lasagna process models). This framework outputs a refined normative model for improving the future Bu...
Business process modelling languages are a very prominent mechanism to understand companies’ busi... more Business process modelling languages are a very prominent mechanism to understand companies’ business processes. This mechanism allows managers to communicate and transversal information through a semantic flow for business process improvement. Examples of Business Process modelling languages are Unified Modeling Language (UML) or Business Process Modeling Notation (BPMN). UML presents alternative views to model company’s functionalities. While, BPMN still have a very abstracted level to model Business Processes. In this context, there are many BPMN extensions to break down its high-level of abstraction. However, most of existing BPMN extensions model a significant increase in business process complexity that deviate it from its crucial objective. This put forward the difficulty of detailing control flow related to information systems without reaching complexity. To that end, we propose our TM4BPMN extension for breaking-down BPMN high abstraction to low-level, to maintain BPMN faci...
Executing loosely structured processes generate unstructured behaviours. Thus, an Unstructured Bu... more Executing loosely structured processes generate unstructured behaviours. Thus, an Unstructured Business Process (UBP) still has more issues that are difficult to be analysed and to be understood due to its complexity and variability. Moreover, the need of an instantiate response is clearly appeared in operational systems. Therefore, it is required to study related challenges that can be acquired during the transition from the structured BP to the unstructured one. In this context, process mining plays a dominant role to understand business process complexity using event data resulted from business process execution. Mainly, this paper treats three challenges related to unstructured BPs. The first challenge is how to support UBPs at runtime using process mining techniques. The second challenge is how to manage UBP variability taking into consideration variant conditions. The third challenge is how to adapt dynamically UBPs according to the company business rules and conditions.
Transdisciplinary Journal of Engineering & Science
Process mining is a young discipline that provides a variety of techniques for extracting data fr... more Process mining is a young discipline that provides a variety of techniques for extracting data from recorded event logs. In this context, process model is simulated, from event logs, to help enterprises to better understand their business processes. In this context, the discovery technique has always been a significant focus of process mining research. But despite the availability of several proposed approaches, participated in this process model visualization, we observe as the number of activities increases, the representation of the entire control flow becomes quite tedious. Indeed, event logs complexity make the process model visualization more difficult in terms of logical links and number of causal relationships between activities. Therefore, we propose a new method for simplifying the process model control flow visualization. This is done by using mainly string similarity algorithms to visually summarizes the number of activities, sequence of execution, relative significance,...
International Journal of E-Services and Mobile Applications, 2022
The information retrieval system is a set of resources and tools that allow users to search for i... more The information retrieval system is a set of resources and tools that allow users to search for information in a given domain. This system permits users to perform their research according to their objectives in diverse ways producing different behaviors. Even users with the same objective may follow different paths and stand different sub-processes, which are introduced as self-defined Business Processes that vary in terms of structure, objective, and result. This puts forward the difficulty of obtaining and studying these user’s behaviors. This paper targets the problem of representing and managing self-defined business process variability. A special interest is given to the use of process mining to deal with this challenge. In this regard, a case study about citizens in interaction with the Electronic Administration is presented, to discover and manage variability of this process type. The main result is a set of recommendations to end users.
Revista de Informática Teórica e Aplicada, Jan 19, 2021
The refined process mining framework contains a set of activities that use extracted information ... more The refined process mining framework contains a set of activities that use extracted information from event logs, discovered models and normative ones. Among these activities, we find those dealing with running events in a Structured Business Process (SBP) context, which are the Detect, the Predict and the Recommend activities. These three activities are nominated as operational support system that performs well on SBP while, it stills a challenging task for an Unstructured Business Process (UBP), because of its complex structure. In this regard, a special interest is given to the use of existing process mining techniques to analyse unstructured processes, from the extraction of a process model based on event data to recommendations at a later stage. To this end, we propose the orchestration of process mining activities into an UBP operational support approach, through the following phases: 1.Preparing Normative model, 2.Detect violations, 3.Preparing predictive model and Predictions and 4.Preparing the recommender model and Recommendations.
Learning and Analytics in Intelligent Systems, Dec 1, 2019
Supply chain is a network between a company and its suppliers to produce and distribute a product... more Supply chain is a network between a company and its suppliers to produce and distribute a product to the final customer. This network includes different activities, people, entities, etc. The interaction between these elements provides a cross-organization Business Process. It has mainly treated with process mining techniques, to handle the resulted process instances as event logs. These events are obtained by implementing the auto identification technology within the supply chain related to materials or personal. By doing so, the provided events are not simply presented, they emerged more challenges like complexity and data confidentiality. Therefore, in this work we develop a descriptive framework, based on a literature study, to answer supply chain challenges related to the process mining field. This is done, by implementing process mining within the supply chain, using auto identification technology and taking into consideration recent challenges related to cross-organization Business Process.
Annual International Conference on Arab Women in Computing, 2019
Process mining has emerged as a research field that focuses on the analysis of processes using ev... more Process mining has emerged as a research field that focuses on the analysis of processes using event data. Process mining is a current topic that reveals several challenges, the most important of which have defined in the Process Mining Manifesto [1]. However, none of the published works have mentioned the progress of process challenges from data-intensive system to knowledge-intensive system related to process mining field. Therefore, the objective of this paper is to provide researchers with the recent challenges emerged during the passage from data-intensive system to knowledge-intensive system.
Nowadays, Big data promises automated actionable knowledge creation and predictive models for use... more Nowadays, Big data promises automated actionable knowledge creation and predictive models for use by humans and computers. Therefore, one of the principal responsibilities of a data scientist is to make reliable predictions based on data, particularly, when the amount of available data is enormous. To do so, it is useful if some of the analysis can be automated and used process mining techniques.In this context, the ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to timely counteract to undesired situations, in order to prevent losses. Therefore, the techniques focus on predicting the remaining time influence other predictive process monitoring dimensions like: cost, delays, etc, i.e., predicting the remaining time, to accomplish an activity, helps respectively to predict the suitable resource and the next executing probable event. Indeed, a considerable number ...
2018 International Conference on Intelligent Systems and Computer Vision (ISCV)
Improving business processes and maintaining acceptable levels of performance are main factors in... more Improving business processes and maintaining acceptable levels of performance are main factors in the success of any organization. Related to BPM (Business Process Management) [1] approaches, a variety of methodologies and techniques are available. These approaches include business process improvement methodologies (BPIM) [2] and process mining [3]. Actually, with BPM life cycle a continuous methodology of improvement business processes is defined, starting by the design phase and ending with the optimize phase. In this paper, we present a new vision BPM life cycle whose content differs from the classical one [4]. On each phase, we define a set of improvement indicators. These indicators recast business processes for best optimization results.
Process discovery technique aims at automatically generating a process model that accurately desc... more Process discovery technique aims at automatically generating a process model that accurately describes a Business Process (BP) based on event data. Related discovery algorithms consider recorded events are only resulting from an operational BP type. While the management community defines three BP types, which are: Management, Support and Operational. They distinguish each BP type by different proprieties like the main business process objective as domain knowledge. This puts forward the lack of process discovery technique in obtaining process models according to business process types (Management and Support). In this paper, we demonstrate that business process types can guide the process discovery technique in generating process models. A special interest is given to the use of process mining to deal with this challenge.
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Papers by Zineb Lamghari