@conference {Hmami2022,
	title = {Applying change tree method in configurable process mining},
	booktitle = {2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2022},
	year = {2022},
	note = {cited By 0},
	abstract = {Changes are unexpected events that can be visualized on the logs of each business process. By analysing these logs, particularly change logs ones, we can provide valuable information about changes that occurred during the process{\textquoteright}s execution phases. In this paper we propose to apply a new version of the change tree method to analyse change logs of a configurable process model obtained through configurable process mining approaches, especially change mining ones. The configurable process model is a model that describes a family of business processes. By representing changes related to configurable models, managers can easily detect and retrieve information about changes and obtain answers to questions such as when the change occurs and how long it takes, and which elements are affected by those changes. This information will not be restricted to a single business process but to a collection of process variants. {\textcopyright} 2022 IEEE.},
	keywords = {Business Process, Change logs, Change mining, Change tree method, Component, Configurable process mining, Configurable process models, Data mining, Forestry, Process mining, Tree method, Unexpected events},
	doi = {10.1109/IRASET52964.2022.9738005},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127982693\&doi=10.1109\%2fIRASET52964.2022.9738005\&partnerID=40\&md5=2c6cd5ffd83ee6a993a4045f7235ecdc},
	author = {Hmami, A. and Sba{\"\i}, H. and Fredj, M.}
}
@conference {Khannat2021768,
	title = {Configurable Process Mining: Semantic Variability in Event Logs},
	booktitle = {International Conference on Enterprise Information Systems, ICEIS - Proceedings},
	volume = {1},
	year = {2021},
	note = {cited By 0},
	pages = {768-775},
	abstract = {Configurable process model represents a reference model regrouping multiple business process variants. The configurable process models offer various benefits like reusability and more flexibility when compared to business process models. The challenges encountered while managing this type of models are related to the creation and the configuration. Recently, process mining offers techniques to discover, check conformance of models, and enhance configurable process models using a collection of event logs, that captures traces during the execution of process variants. However, existing works in configurable process discovery lack the incorporation of semantics in the resulting model. Historically, semantic process mining has been applied to event logs to improve process discovery with respect to semantic. Furthermore, from the best of our knowledge, configurable process mining approaches do not fully support semantics. In this paper, we propose a novel method to enrich the collection of event logs with configurable process ontology concepts by introducing semantic annotations that capture variability of elements present in the logs. This is a first step towards discovering a semantically enriched configurable process. Copyright {\textcopyright} 2021 by SCITEPRESS {\textendash} Science and Technology Publications, Lda. All rights reserved.},
	keywords = {Business Process, Configurable process model discovery, Configurable process models, Data mining, Event logs, Information systems, Model discoveries, Ontology, Ontology{\textquoteright}s, Process Discovery, Process mining, Process variants, Reference modeling, Reusability, Semantics},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137949536\&partnerID=40\&md5=dd49bcf12987200729d1f5e491d2994b},
	author = {Khannat, A. and Sba{\"\i}, H. and Kjiri, L.}
}
@conference {Hmami2021,
	title = {Enhancing change mining from a collection of event logs: Merging and Filtering approaches},
	booktitle = {Journal of Physics: Conference Series},
	volume = {1743},
	number = {1},
	year = {2021},
	note = {cited By 3},
	abstract = {An event log is the key element of all change mining and process mining approaches. Those approaches bridge the gap between conventional business process management and data analysis techniques such as machine learning and data mining. In this day, companies and business organizations usually use a family of business processes that may face different variations and adjustments. Still, those processes are widely identical, with a slight difference in specific points. Consequently, performing a process mining or a change mining for each process will be a redundant task. The use of a configurable process model is a practical solution for redundancy problem. Thus, the process mining areas such as discovering verifying the conformity of a business process and enhancing processes, are reduced considerably. However, the configurable process models and the variability concept are rarely introduced in change mining approaches. The existing methods that analyse and manage event logs do not then consider the variability issue. Therefore, the fact of using a collection of event log becomes a challenging task. Our proposed approach is to merge and filter a collection of event logs from the same family with respect to variability. Our goal is to enhance change mining from a collection of event logs and detect changes in variable fragments of the obtained event log. {\textcopyright} Published under licence by IOP Publishing Ltd.},
	keywords = {Business organizations, Business Process, Business process management, Configurable process models, Data analysis techniques, Data mining, Data Science, Enterprise resource management, Filtration, Practical solutions, Redundancy problems, Variable fragment},
	doi = {10.1088/1742-6596/1743/1/012020},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101766097\&doi=10.1088\%2f1742-6596\%2f1743\%2f1\%2f012020\&partnerID=40\&md5=b0f4b6f1485dd0ca46c497f084c56c74},
	author = {Hmami, A. and Sba{\"\i}, H. and Fredj, M.}
}
@article {Hmami2021632,
	title = {Handling Sudden and Recurrent Changes in Business Process Variability: Change Mining based Approach},
	journal = {International Journal of Advanced Computer Science and Applications},
	volume = {12},
	number = {4},
	year = {2021},
	note = {cited By 1},
	pages = {632-640},
	abstract = {Changes are random and unavoidable actions in business processes, and they are frequently overlooked by managers, especially when managers need to deal with a collection of process variants. Because they must manage every single business process variant separately which is a timeconsuming task. They exist many approaches to manage a collection of business process and deal with variability. Such as process mining approaches, that can discover configurable business process models, enhancing them and verify conformity automatically. However, those approaches do not cover changes and concept drift that occur over time. This paper presents a novel change mining approach that discovers changes in a collection of event logs and reports them on a change log. This change log can be analyzed to determine whether the changes are sudden or recurrent and recommend afterward some improvement to the configurable process model. {\textcopyright} 2021. All Rights Reserved.},
	keywords = {Business Process, Change logs, Change mining, Component, Concept drifts, Configurable process models, Data mining, Managers, Process mining, Process variants, Sudden change, Variability},
	doi = {10.14569/IJACSA.2021.0120479},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105804608\&doi=10.14569\%2fIJACSA.2021.0120479\&partnerID=40\&md5=826c6a1f82674abf6a9a671f65401022},
	author = {Hmami, A. and Fredj, M. and Sba{\"\i}, H.}
}
@article {Sikal2020183,
	title = {Configurable process model: Discovery approach from event logs},
	journal = {Advances in Science, Technology and Engineering Systems},
	volume = {5},
	number = {1},
	year = {2020},
	note = {cited By 0},
	pages = {183-190},
	abstract = {In the domain of business process management, the configurable process model is widely used to optimize time and cost of business process models design, which is known as the concept of "reuse". Using process mining techniques for process model discovery helps to provide a better view on processes and improve quality of models. The majority of existing configurable model discovery approaches work intensively on control flow discovery as main process perspective without considering other perspectives such as resources and data, and do not propose a detailed discovery of variability elements. In addition, the configurable process model creation is generally done by merging variant models not directly from event logs, which is not the optimal way to get a reliable configurable process model. This paper presents an overview of new multi-perspective variability discovery approach. The approach respects the variability of different process perspectives and allows users to create a configurable process model directly from event logs. {\textcopyright} 2020 ASTES Publishers. All rights reserved.},
	doi = {10.25046/aj050124},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081330401\&doi=10.25046\%2faj050124\&partnerID=40\&md5=716f2277e1926d108d92b8a313d3bac9},
	author = {Sikal, R. and Sba{\"\i}, H. and Kjiri, L.}
}
@conference {Khannat2020139,
	title = {Event logs pre-processing for configurable process discovery: Ontology-based approach},
	booktitle = {Colloquium in Information Science and Technology, CIST},
	volume = {2020-June},
	year = {2020},
	note = {cited By 1},
	pages = {139-144},
	abstract = {Nowadays, process mining offers key tools for companies to discover and develop continuously their processes. Moreover, many research works focus on the application of process mining techniques to manage configurable process models. To provide configurable process models with high quality and less complexity using process mining techniques, the preparation of event logs before discovering processes is strongly required. Therefore, various approaches have introduced semantics to derive knowledge from process execution traces. Although, this knowledge is used for discovering variability rules or deriving process variants. However, existing works lack the discovery of semantically enriched configurable process models from event logs. Since event logs are usually combined with semantics and domain ontology for optimizing process mining applications, it seems to be important to introduce a new approach of semantically enriched configurable process discovery. It will be helpful during configuration, validation, and enhancement of configurable processes that are extracted from event logs. In this paper, we present a new framework for event logs preprocessing using ontologies. Our aim is to enrich a collection of event logs with concepts from ontologies as a first step towards discovering a semantically enriched configurable process. {\textcopyright} 2021 IEEE.},
	keywords = {Configurable process models, Data mining, Domain ontologies, New approaches, Ontology, Pre-processing, Process Discovery, Process execution, Process mining, Process variants, Semantics},
	doi = {10.1109/CiSt49399.2021.9357303},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103812408\&doi=10.1109\%2fCiSt49399.2021.9357303\&partnerID=40\&md5=c1a3e5ace97f4f0e5deedd34b987c311},
	author = {Khannat, A. and Sba{\"\i}, H. and Kjiri, L.}
}
@conference {Hmami2020,
	title = {A new Framework to improve Change Mining in Configurable Process},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 1},
	abstract = {The configurable process model is an important approach to reduce the redundancy involved in modeling, reengineering, and adapting a business process with its environment. While process mining approaches aim to discover and, verify conformance and enhance a process, connecting configurable processes and process mining shows considerable improvements, especially in terms of time and cost. However, the configurable process has rarely been applied in change mining. Our goal in this paper is to highlight the importance of using variability to improve change mining from a collection of event logs. We propose a framework that can detect and recommend changes, by mining changes from a collection of event logs. The main purpose is to allow engineers and business process designers to easily adjust business process with the environment changes. {\textcopyright} 2020 ACM.},
	keywords = {Business Process, Change mining, Configurable process models, Data mining, Environment change, Event logs, Information systems, Information use, Process mining},
	doi = {10.1145/3386723.3387853},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123042985\&doi=10.1145\%2f3386723.3387853\&partnerID=40\&md5=cbdd186e5e2298cef0babe5bb443e4d0},
	author = {Hmami, A. and Sba{\"\i}, H. and Fredj, M.}
}
@conference {Khannat202020,
	title = {Towards mining semantically enriched configurable process models},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	pages = {20-24},
	abstract = {Providing configurable process model with high quality is a primary objective to derive process variants with better accuracy and facilitate process model reuse. For this purpose, many research works have been interested in configurable process mining techniques to discover and configure processes from event logs. Moreover, to use the knowledge captured by event logs when mining processes, the concept of semantic process mining is introduced. It allows for combining semantic technologies with process mining. Despite the diversity of works in mining and customizing configurable process models, the application of these techniques is still limited to use semantics in minimizing the complexity of discovered processes. However, it seems to be pertinent to discover semantically enriched configurable process models directly from event logs. Consequently, this can facilitate using semantic in configuring, verifying conformance or enhancing discovered configurable processes. In this paper, we present a comparative study of existing works that focus on mining configurable process models with respect to semantic technologies. Our aim is to propose a new framework to automatically discover semantically enriched configurable processes. {\textcopyright} 2020 ACM.},
	keywords = {Comparative studies, Configurable process models, Data mining, Intelligent systems, Mining process, Primary objective, Process mining, Process Modeling, Process variants, Semantic technologies, Semantic Web, Semantics},
	doi = {10.1145/3419604.3419797},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096408590\&doi=10.1145\%2f3419604.3419797\&partnerID=40\&md5=a77606eacf64646ccd3459e57f5cabdf},
	author = {Khannat, A. and Sba{\"\i}, H. and Kjiri, L.}
}
@conference {Sikal20181,
	title = {Configurable process mining: A comparative study},
	booktitle = {Proceedings of the 2018 International Conference on Optimization and Applications, ICOA 2018},
	year = {2018},
	pages = {1-6},
	doi = {10.1109/ICOA.2018.8370585},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048784383\&doi=10.1109\%2fICOA.2018.8370585\&partnerID=40\&md5=7c94107c941b68ce4932aa663659b32d},
	author = {Sikal, R. and Sba{\"\i}, H. and Kjiri, L.}
}
@conference {Sikal2018137,
	title = {Configurable Process Mining: Variability Discovery Approach},
	booktitle = {Colloquium in Information Science and Technology, CIST},
	volume = {2018-October},
	year = {2018},
	pages = {137-142},
	doi = {10.1109/CIST.2018.8596526},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061447388\&doi=10.1109\%2fCIST.2018.8596526\&partnerID=40\&md5=74eec99e91001d7dee97e25a580cf92e},
	author = {Sikal, R. and Sba{\"\i}, H. and Kjiri, L.}
}
@conference {ElFaquih2015,
	title = {Configurable process models: A semantic validation},
	booktitle = {2015 10th International Conference on Intelligent Systems: Theories and Applications, SITA 2015},
	year = {2015},
	note = {cited By 1},
	abstract = {Nowadays, variability management has been introduced in the field of business process modeling by means of the configurable process models solution, with a view to resolving problems of redundancy and design time errors. Nevertheless, the existing approaches of variability modeling lack of semantic representation, and consequently, present many limits regarding automation of modeling, configuring and evolving configurable processes. Recently, semantic web technologies, especially ontologies, have been proposed in order to increase the level of automation and reduce human intervention in different domains. Business process management has benefited from the emergence of these technologies and the concept of semantic business process management appeared to make the whole business process accessible to intelligent queries as for machine reasoning. Whereas most approaches focus on providing mechanisms for modeling and configuring process models, their validation was treated syntactically rather than semantically. In this paper, we propose an ontology-based approach to enhance configurable processes validation using ontologies. {\textcopyright} 2015 IEEE.},
	doi = {10.1109/SITA.2015.7358436},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962355570\&doi=10.1109\%2fSITA.2015.7358436\&partnerID=40\&md5=05f3833dcf81fed54a94453d9d919358},
	author = {El Faquih, L. and Sba{\"\i}, H. and Fredj, M.}
}
@article {Sbai2015111,
	title = {Generating services supporting variability from configurable process models},
	journal = {Journal of Theoretical and Applied Information Technology},
	volume = {72},
	number = {1},
	year = {2015},
	note = {cited By 0},
	pages = {111-124},
	abstract = {Today, a PAIS (Process-Aware Information System) is the broadly adopted information system by enterprises, which aims to define how to use services and business processes to achieve the business goals of enterprises. For this purpose, research work in PAIS was interested in aligning business processes and services in order to achieve a good connection between business process models as represented in the process layer, and service models as represented in the application layer In this context, to improve the reuse of business processes and services, the concepts of configurable process models and configurable services (which support variability) have emerged as new leading edge concepts for improving reuse. Indeed, variability refers to the characteristic of a system to adapt, specialize and configure itself with the context of use. More recently, configurable process models become the most popular solution to deal with adaptability in a PAIS. This has increased the research focus in how to extract services from configurable process models. Several approaches have developed solutions for proposing services generation from business process. However, generating configurable services from configurable process model has been neglected in the context of PAIS. In this paper, we propose an MDA approach for an automatic generation of configurable service models from configurable process models. {\textcopyright} 2005 - 2015 JATIT \& LLS. All rights reserved.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922695803\&partnerID=40\&md5=c3ae357bd43958ac23a5336e13ba61e6},
	author = {Sba{\"\i}, H. and Fredj, M. and Chakir, B.}
}
@conference {Faquih20151,
	title = {Towards a semantic enrichment of configurable process models},
	booktitle = {Colloquium in Information Science and Technology, CIST},
	volume = {2015-January},
	number = {January},
	year = {2015},
	note = {cited By 2},
	pages = {1-6},
	abstract = {In recent years, the concept of Semantic business process management has attracted significant research work, with the emergence of semantic web technologies (SW), which aimed to describe resources in a formal representation with clearly defined semantics in order to be machine-readable. Enriching business process with semantics improves the representation of processes and permits the automation of different tasks such as modeling, configuration, evolution, and promotes more flexible and adaptive solutions according to business requirement changes. Nowadays, the key concept of reuse in Business Process Management is the Configurable Process Model (CPM) solution which is an important approach for reusing and customizing business process models with a set of process variants. However, existing approaches in CPM lack of semantic representation. In this paper, we propose an ontology-based approach for enriching Configurable Process Models with semantics, as a first step towards the enhancement of the CPM evolution process. {\textcopyright} 2014 IEEE.},
	doi = {10.1109/CIST.2014.7016585},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938088544\&doi=10.1109\%2fCIST.2014.7016585\&partnerID=40\&md5=3fe8210773c4d071e489ee75f3bf7189},
	author = {Faquih, L.E. and Sba{\"\i}, H. and Fredj, M.}
}
@conference {Sbai2014616,
	title = {On managing evolution of configurable services based on configurable processes},
	booktitle = {2014 2nd World Conference on Complex Systems, WCCS 2014},
	year = {2014},
	note = {cited By 0},
	pages = {616-621},
	abstract = {Today, SOA is the most dominant and broadly adopted architecture style by enterprises. It defines how to use services to achieve business goals, and allows interoperability with multiple customers within different business domains. The growing use of SOA has increased research interest on how to improve the reuse of services. Hence, managing services variability was introduced by many approaches as a new leading edge concept for improving reuse. Indeed, variability refers to the characteristic of a system to adapt, specialize and configure itself with the context of use. Several proposals have been made in this respect. However, none of these seem to deal with the evolution of these configurable services. In service oriented systems, the evolution of services is usually introduced by the evolution of the associated business process. Therefore, if a configurable process model (CPM for short) should evolve in reaction to changing business needs, the associated configurable services (CS for short) are also impacted by this change. In our previous work, we proposed a complete methodology for managing evolution in configurable process models. In this paper, we extend our proposed approach to develop an evolution process for managing the changes of configurable services (variable services) caused by changing the associated configurable process model. {\textcopyright} 2014 IEEE.},
	doi = {10.1109/ICoCS.2014.7060934},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988220675\&doi=10.1109\%2fICoCS.2014.7060934\&partnerID=40\&md5=371668ec9f07b988342b6b176b8c7d7e},
	author = {Sba{\"\i}, H. and Fredj, M. and Chakir, B. and Kjiri, L.}
}
@conference {Faquih2014131,
	title = {Semantic variability modeling in business processes: A comparative study},
	booktitle = {2014 9th International Conference for Internet Technology and Secured Transactions, ICITST 2014},
	year = {2014},
	note = {cited By 2},
	pages = {131-136},
	abstract = {Business Process Management (BPM) is an approach that aligns information systems (IT view) with business processes (Business view). Both the dynamic environment of business and the fast changes in requirements have created the need to reuse business processes in order to gain in cost and time. To enhance reuse in BPM, significant research efforts have been made on the development of methods and languages in order to support variability modeling. However, there is no single standard for modeling variability in configurable process models. Each modeling language integrating variability proposes its own method for representing variability, and different representations are used to express the same variability concepts. In addition to that, human intervention is still mandatory in order to create, configure and evolve configurable process models. With semantic annotations and the usage of ontology, these tasks could be automated. In this paper, we propose a comparative study to assess variability modeling approaches in software engineering domains, which include software product line engineering, services engineering and process engineering. This comparative study is conducted in terms of semantics use. {\textcopyright} 2014 Infonomics Society.},
	doi = {10.1109/ICITST.2014.7038792},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962362206\&doi=10.1109\%2fICITST.2014.7038792\&partnerID=40\&md5=f5188b09e77e3ca69d35da9a3d953678},
	author = {Faquih, L.E. and Sba{\"\i}, H. and Fredj, M.}
}
@conference {Sbai20141082,
	title = {Support for resources changes in configurable process models},
	booktitle = {International Conference on Multimedia Computing and Systems -Proceedings},
	year = {2014},
	note = {cited By 1},
	pages = {1082-1087},
	abstract = {Today, researches on business process models reuse are interested in configurable process modeling which is an important approach for reusing and customizing business process models with a set of process variants. However, enterprises are involved in a competitive environment which imposes them to often evolve their configurable process models in terms of activity, data and resource. So, creating configurable process models becomes insufficient since the configurable process models should evolve to meet new needs. To address this issue, we propose a reusable process patterns system for guiding designers in managing changes and evolution of configurable process models. In this paper, we focus on how to manage resources changes. We present one pattern of our proposed system, the variation point resource insertion process pattern. {\textcopyright} 2014 IEEE.},
	doi = {10.1109/ICMCS.2014.6911319},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928751275\&doi=10.1109\%2fICMCS.2014.6911319\&partnerID=40\&md5=c8fd1066a03aedd1bcdb5fedc72a9dec},
	author = {Sba{\"\i}, H. and Fredj, M. and Kjiri, L.}
}
@conference {Sbai2013382,
	title = {Towards a process patterns based approach for promoting adaptability in configurable process models},
	booktitle = {ICEIS 2013 - Proceedings of the 15th International Conference on Enterprise Information Systems},
	volume = {3},
	year = {2013},
	note = {cited By 3},
	pages = {382-387},
	abstract = {Reference process modeling approach has been proposed in order to provide a generic solution for reuse in business process management. These models are intended to be adapted by different organizations to respond to new requirements. As a result of this adaptation, a set of variants of the reference process model must be managed. In this context, configurable solutions have been developed to manage variability by integrating all possible configurations. However, enterprises are involved in competitive and complex environments which impose them to often adapt their configurable process models to deal with the increasing globalisation. Therefore, configurable process models should evolve over time in terms of activities, resources and data to meet new needs. In this paper, we propose a process patterns based approach to guide designers in evolving configurable process models at design time as a first step of our contribution to provide an automated support for evolving configurable process models. The presentation of our process patterns system is limited in this paper to the Activity Insertion Process Pattern.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84887716124\&partnerID=40\&md5=f45ed794bc8aa6fa44256d12d21c58e4},
	author = {Sba{\"\i}, H. and Fredj, M. and Kjiri, L.}
}
@conference {Sbai201222,
	title = {A process pattern for managing evolution of configurable reference process models},
	booktitle = {CiSt 2012 - Proceedings: 2012 Colloquium in Information Science and Technology},
	year = {2012},
	note = {cited By 1},
	pages = {22-25},
	doi = {10.1109/CIST.2012.6388057},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872551237\&doi=10.1109\%2fCIST.2012.6388057\&partnerID=40\&md5=631ccb9b68bc1ab1c8ad1eb6e57c7078},
	author = {Sba{\"\i}, H. and Fredj, M. and Kjiri, L.}
}
