@article {Fissaa2021151,
	title = {Combining planning and learning for context aware service composition},
	journal = {International Journal of Data Analysis Techniques and Strategies},
	volume = {13},
	number = {1-2},
	year = {2021},
	note = {cited By 0},
	pages = {151-169},
	abstract = {Computing vision introduced by Mark Weiser in the early {\textquoteright}90s has defined the basis of what is called now ubiquitous computing. This new discipline results from the convergence of powerful, small and affordable computing devices with networking technologies that connect them all together. Thus, ubiquitous computing has brought a new generation of service-oriented architectures (SOA) based on context-aware services. These architectures provide users with personalised and adapted behaviours by composing multiple services according to their contexts. In this context, the objective of this paper is to propose an approach for context-aware semantic-based services composition. Our contributions are built around following axes: 1) a semantic-based context modelling and context-aware semantic composite service specification; 2) an architecture for context-aware semantic-based services composition using artificial intelligence planning; 3) an intelligent mechanism based on reinforcement learning for context-aware selection in order to deal with dynamicity and uncertain character of modern ubiquitous environment. Copyright {\textcopyright} 2021 Inderscience Enterprises Ltd.},
	doi = {10.1504/IJDATS.2021.114673},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105363779\&doi=10.1504\%2fIJDATS.2021.114673\&partnerID=40\&md5=a71041d2e85a5cf3407180fb5cff29b2},
	author = {Fissaa, T. and El Hamlaoui, M. and Guermah, H. and Hafiddi, H. and Nassar, M.}
}
@conference {Choukri2021,
	title = {Context aware Hidden Markov Model for Intention process mining},
	booktitle = {5th International Conference on Intelligent Computing in Data Sciences, ICDS 2021},
	year = {2021},
	note = {cited By 0},
	abstract = {Nowadays, the omnipresence of digital devices and solutions in daily life made the digital footprints of individuals and the traces of their activities widely available. Smart devices generate a tremendous amount of data that and enables tracking their users{\textquoteright} activity. Extensive research has been conducted to produce generic process models based on the analysis of user{\textquoteright}s activity recorded during the enactment of operational processes. Unfortunately, these approaches considered only the relation between the observed activities and their sequences to infer the underlying process. Thus, ignoring the goal conditioning the user{\textquoteright}s behavior when triggering the actual process. Nevertheless, the same activity traces could serve to unhide the intention behind each process. This article will focus on presenting our approach to Contextual intention mining using Hidden Markov Model (HMM). This approach explores the close relationships between intention and context to construct the process model while ensuring consistency between observed context and actual intentions. {\textcopyright} 2021 IEEE.},
	keywords = {Behavioral research, Context, Data mining, Digital devices, Event logs, Goal mining, Goal models, Hidden Markov models, Information systems, Information use, Intention-oriented process modeling, Intentional process modeling, Machine learning, Markov modeling, Oriented process, Process Discovery, Process engineering, Process mining, Process recommendation, Process-aware information systems, Process-models, Supervised learning, Unsupervised learning, User profile},
	doi = {10.1109/ICDS53782.2021.9626765},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123483088\&doi=10.1109\%2fICDS53782.2021.9626765\&partnerID=40\&md5=d61fab7bfc96ede4658a8984da68ef3f},
	author = {Choukri, I. and Guermah, H. and Daosabah, A. and Nassar, M.}
}
@article {Ettazi202040,
	title = {CATS-CAE reflective middleware framework for adapting context-aware transactional services: Using a hybrid policy-based approach},
	journal = {International Journal of Web Services Research},
	volume = {17},
	number = {2},
	year = {2020},
	note = {cited By 2},
	pages = {40-58},
	abstract = {Pervasive environments are characterized by limited computing resources and wireless connectivity. In parallel, current application domains have variable transactional requirements that do not fit the traditional ACID model. As a result, the pervasive environment characteristics are compelling and cannot be supported by conventional solutions that are typically dedicated to a specific application domain and support a limited set of context parameters. This article aims at providing a complete solution that addresses the challenges of the adaptability of context-aware transactional services {\textquotedblleft}CATS{\textquotedblright} in pervasive environments. Thus, a new framework CATS-CAE was designed, which offers a comprehensive structure of multiple component chains. The adaptation strategy in CATS-CAE is based on a hybrid approach combining the use of adaptation policies, alternative strategy and behavioral adaptation of composite services through the {\textquotedblleft}Profiled Task Class{\textquotedblright} concept. A probabilistic model is also presented to support the efficiency of the proposed approach. Copyright {\textcopyright} 2020, IGI Global.},
	keywords = {ACID properties, Alternative strategy, Applications domains, Behavioral adaptation, Behavioral research, Context- awareness, Context-Aware, Middleware, Middleware frameworks, Pervasive environments, Reflective middleware, Transactional service},
	doi = {10.4018/IJWSR.2020040103},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116856218\&doi=10.4018\%2fIJWSR.2020040103\&partnerID=40\&md5=c368678a98b60e19ddac01a758494320},
	author = {Ettazi, W. and Nassar, M. and Hafiddi, H.}
}
@conference {Bouroumi2020,
	title = {CBPM: An Approach for Contextual Business Process Modeling},
	booktitle = {4th International Conference on Intelligent Computing in Data Sciences, ICDS 2020},
	year = {2020},
	note = {cited By 0},
	abstract = {With the evolution of ubiquitous computing, the consideration of the context in applications became indispensable and this is the case for business process modeling. In recent years, several approaches have been proposed to introduce context into BP modeling. In this article, we will present an approach which allows to model business process while integrating context notion, we will propose a meta-model of context that allows to model the context in a generic way, and also a framework to gather this context meta-model with the BPMN model in order to elaborate a contextual business process. {\textcopyright} 2020 IEEE.},
	keywords = {BP model, Business Process, Business process model, Data Science, Intelligent computing, Meta model, Process engineering, Ubiquitous computing},
	doi = {10.1109/ICDS50568.2020.9268681},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098655208\&doi=10.1109\%2fICDS50568.2020.9268681\&partnerID=40\&md5=0e9a0eeb9719d90ae6d359faa5759165},
	author = {Bouroumi, J.E.L. and Guermah, H. and Nassar, M. and Kriouile, A.}
}
@conference {Boumahdi2020603,
	title = {Crisis management systems: Big data and machine learning approach},
	booktitle = {ENASE 2020 - Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering},
	year = {2020},
	note = {cited By 0},
	pages = {603-610},
	abstract = {A crisis is defined as an event that, by its nature or its consequences, poses a threat to the vital national interests or the basic needs of the population, encourages rapid decision making, and requires coordination between the various departments and agencies. Hence the need and importance of crisis and disaster management systems. These crisis and disaster management systems have several phases and techniques, and require many resources and tactics and needs. Among the needs of these systems are useful and necessary information that can be used to improve the making of good decisions, such as data on past and current crises. The application of machine learning and big data technologies in data processing of crises and disasters can yield important results in this area. In this document, we address in the first part the crisis management systems, and the tools of big data and machine learning that can be used. Then in the second part, we have established a literature review that includes a state of the art, and a discussion. Then we established a machine learning and big data approach for crisis management systems, with a description and experimentation, as well as a discussion of results and future work. {\textcopyright} Copyright 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.},
	keywords = {Big data, Crisis management systems, Data handling, Data technologies, Decision making, Disaster management, Disaster prevention, Disasters, Information management, Literature reviews, Machine learning, Machine learning approaches, Software engineering, State of the art},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088381343\&partnerID=40\&md5=b90b38f94e34455a12781472d1aba3ab},
	author = {Boumahdi, A. and El Hamlaoui, M. and Nassar, M.}
}
@conference {Bennani2018152,
	title = {Collaborative model-based matching of heterogeneous models},
	booktitle = {Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2018},
	year = {2018},
	pages = {152-157},
	doi = {10.1109/CSCWD.2018.8465228},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054369642\&doi=10.1109\%2fCSCWD.2018.8465228\&partnerID=40\&md5=5c36a33efe1da810c568cd8ac7f96749},
	author = {Bennani, S. and El Hamlaoui, M. and Nassar, M. and Ebersold, S. and Coulette, B.}
}
@conference {ElHoussaini2015,
	title = {A cloud service template for enabling accurate cloud adoption and migration},
	booktitle = {Proceedings of 2015 International Conference on Cloud Computing Technologies and Applications, CloudTech 2015},
	year = {2015},
	note = {cited By 0},
	abstract = {Since the cloud services are increasing exponentially, discover, filter, select and eventually migrate to the appropriate service is becoming a challenging mission. In the literature, several Service Template initiatives are proposed to deal with service description, topology and even portability, but they fail to emphasis the integration of consumer concerns and the migration feasibility checking before decision making. To fill the gap, the paper introduce a cloud service template that structured in three parts with a dedicated migration one. Also, a weight concept is added to service parameters in order to enforce importance and priority. Additionally, a context awareness mechanism is introduced for enabling automated service template adaptation. {\textcopyright} 2015 IEEE.},
	doi = {10.1109/CloudTech.2015.7337018},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962784529\&doi=10.1109\%2fCloudTech.2015.7337018\&partnerID=40\&md5=c3c5532dba0fd60abea784ec21a685a7},
	author = {El Houssaini, C. and Nassar, M. and Kriouile, A.}
}
@conference {Hafiddi2011176,
	title = {A context-aware service centric approach for service oriented architectures},
	booktitle = {ICEIS 2011 - Proceedings of the 13th International Conference on Enterprise Information Systems},
	volume = {3 ISAS},
	year = {2011},
	note = {cited By 4},
	pages = {176-183},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84865112643\&partnerID=40\&md5=2dc55c40ac4f93da3355ed544dba2d19},
	author = {Hafiddi, H. and Nassar, M. and Baidouri, H. and El Asri, B. and Kriouile, A.}
}
@conference {Nassar2009412,
	title = {Code generation in VUML profile: A model driven approach},
	booktitle = {2009 IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2009},
	year = {2009},
	note = {cited By 4},
	pages = {412-419},
	doi = {10.1109/AICCSA.2009.5069358},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349911813\&doi=10.1109\%2fAICCSA.2009.5069358\&partnerID=40\&md5=e4b947f9a88ea213061c85e81691b486},
	author = {Nassar, M. and Anwar, A. and Ebersold, S. and Elasri, B. and Coulette, B. and Kriouile, A.}
}
