Our Scientific Contributions

• Jihane Mali, Ahvar Shohreh, Faten Atigui, Ahmed Azough & Nicolas Travers (2026). DaMoOp: A global approach for optimizing denormalized schemas through a multidimensional cost model. Information Systems, 136, 102598- [Abstract]
  The complexity of database systems has increased alongside the exponential growth of data, necessitating Information Systems (IS) architects to continuously refine data models and meticulously select storage and management options that align with requirements. While existing solutions focus on data model transformation, none offer guidance in selecting the most suitable data model for a given use case. In this context, we propose DaMoOp, an automated approach for leading data model selection process. DaMoOp starts from a conceptual model and associated use case comprising queries, settings and infrastructure constraints, to generate relevant logical data models. A cost model, considering environmental, financial, and temporal factors, facilitates comparison and selection of the most suitable data model. Our cost model incorporates both data model and queries costs. Additionally, we suggest a data model selection process that enhances the ability to choose the optimal data model(s) for a specific use case, while also adapting to rapidly evolving use cases. We provide a strategic optimization approach designed to identify the most cost-efficient and stable data model as use case scenarios evolve. Moreover, we offer a simulation tool for the entire process, which enables visualizing the impact of use case variations on data model costs, thus empowering IS architects to make informed decisions.

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• Daniel Wladdimiro, Nicolás Hidalgo, Alessio Pagliari, Luciana Arantes, Pierre Sens, Erika Rosas & Victor Reyes (2026). A Multi-Model predictive framework for adaptive resource management in stream processing systems. Future Generation Computer Systems, 182, 108461

• Wladdimiro, Daniel, Pagliari, Alessio & Brum, Rafaela C. (2025). Toward Stream Processing Efficiency Leveraging Cloud Burstable Instances. In 2025 IEEE International Conference on Cloud Engineering (IC2E) (pp. 217-224)

• Farah Ait Salaht & Nora Izri (2025). Optimisation Conjointe de la Réplication et du Placement des Microservices pour les Applications IoT dans le Fog Computing. ALGOTEL 2025 - 27èmes Rencontres Francophones sur les Aspects Algorithmiques des Télécommunications, . Saint Valery-sur-Somme, France- [Abstract]
  Le Fog Computing étend le Cloud Computing à la périphérie du réseau, répondant ainsi aux besoins croissants des applications de l'Internet des Objets (IoT) nécessitant des analyses en temps réel. Ces applications, généralement structurées en graphes de microservices interconnectés, traitent en continu des volumes de données très variables, ce qui peut, en cas de surcharge, dégrader significativement la qualité de service. Dans ce contexte, la réplication des microservices, en créant plusieurs instances d'un service, permet de paralléliser le traitement, de répartir la charge, de réduire la latence et d'améliorer la réactivité face aux variations des flux de données. Dans ce travail, nous proposons un modèle conjoint de réplication et de placement pour les microservices d'applications IoT, formulé comme un problème de satisfaction de contraintes (CSP). Ce modèle générique et évolutif considère la diversité des exigences des applications modernes (latence, ressource) et l'hétérogénéité des infrastructures Fog (calcul, stockage, réseau) pour déterminer le degré de réplication et les emplacements optimisés des microservices, tout en s'adaptant aux variations de charge. Les expérimentations numériques montrent des performances et une scalabilité supérieures par rapport aux méthodes existantes, et met en évidence les avantages du CSP et de l'optimisation conjointe pour réduire les temps de réponse et les coûts de déploiement des applications IoT.

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• Aït-Salaht, Farah, Maestra, Laëtitia Della & Wladdimiro, Daniel (2025). ACADS: A Framework for Adaptive Cost-Aware Deployment of Stream Processing System in the Cloud. In 2025 IEEE International Conference on Cloud Engineering (IC2E) (pp. 209-216)

• Jihane Mali, Ahvar Shohreh, Faten Atigui, Ahmed Azough & Nicolas Travers (2024). FACT-DM : A Framework for Automated Cost-Based Data Model Transformation. In Extending Database Technology (pp. 822-825). Paestum, Italy: OpenProceedings.org- [Abstract]
  As data continues to grow at an unprecedented rate, the com- plexity of database systems also increase significantly, requiring information systems (IS) architects to constantly adapt their data model and carefully select the optimal solution(s) for storing and managing data that align with new queries, settings, and con- straints. Having a tool to visually show the impact of different changes, will help IS architects in their decision making. In this paper, we propose a framework to demonstrate the impact of denormalization of data models on their cost and consequently IS architects can choose the best trade-off.

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• Jihane Mali, Ahvar Shohreh, Ahmed Azough & Nicolas Travers (2024). How to Simulate Data Model Denormalisation using a Distributed Cost-Model?. 40ème conférence sur la gestion des données, . Orléans, France- [Abstract]
  As data continues to grow at an unprecedented rate, the com- plexity of database systems also increase significantly, requiring information systems (IS) architects to constantly adapt their data model and carefully select the optimal solution(s) for storing and managing data that align with new queries, settings, and con- straints. Having a tool to visually show the impact of different changes, will help IS architects in their decision making. In this paper, we propose a framework to demonstrate the impact of denormalization of data models on their cost and consequently IS architects can choose the best trade-off.

  Fermer

• Farah Ait-Salaht, Maher Rebai & Nora Izri (2024). Optimizing Service Replication and Placement for IoT Applications in Fog Computing Systems. In Euro-Par 2024: Parallel Processing - 30th European Conference on Parallel and Distributed Processing, Madrid, Spain, August 26-30, 2024, Proceedings, Part I (pp. 283-297)

• Daniel Wladdimiro, Luciana Arantes, Pierre Sens & Nicolás Hidalgo (2024). PA-SPS: A predictive adaptive approach for an elastic stream processing system. Journal of Parallel and Distributed Computing, 192, 104940- [Abstract]
  Stream Processing Systems (SPSs) dynamically process input events. Since the input is usually not a constant flow, presenting rate fluctuations, many works in the literature propose to dynamically replicate SPS operators, aiming at reducing the processing bottleneck induced by such fluctuations. However, these SPSs do not consider the problem of load balancing of the replicas or the cost involved in reconfiguring the system whenever the number of replicas changes. We present in this paper a predictive model which, based on input rate variation, execution time of operators, and queued events, dynamically defines the necessary current number of replicas of each operator. A predictor, composed of different models (i.e., mathematical and Machine Learning ones), predicts the input rate. We also propose a Storm-based SPS, named PA-SPS, which uses such a predictive model, not requiring reboot reconfiguration when the number of operators replica change. PA-SPS also implements a load balancer that distributes incoming events evenly among replicas of an operator. We have conducted experiments on Google Cloud Platform (GCP) for evaluation PA-SPS using real traffic traces of different applications and also compared it with Storm and other existing SPSs.

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• Jihane Mali, Faten Atigui, Ahmed Azough, Nicolas Travers & Ahvar Shohreh (2023). A Multidimensional Cost Model for Distributed Denormalized NoSQL Schemas. 39ème Conférence sur la Gestion de Données - Principes, Technologies et Applications, . montpellier, France- [Abstract]
  The complexity of database systems has increased significantly along with the continuous growth of data, forcing Information Systems (IS) administrators to constantly adapt their data models and carefully choose the best option(s) for storing and managing data. In this context, we propose an automatic global approach for leading data model's transformation process. This approach starts with the generation of all possible solutions. It then relies on a cost model that helps to compare these generated data models to finally choose the best one for the given use case. This cost model integrates both data model and queries cost. It also takes into consideration the environmental impact of a data model as well as its financial and its time cost. This work presents for the first time a multidimensional cost model encompassing time, environmental and financial constraints, which compares data models leading to the choice of the best one for a given use case and context. In addition, a simulation tool for data model's transformation and cost computation has been developed based on our approach.

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• Jihane Mali, Ahvar Shohreh, Faten Atigui, Ahmed Azough & Nicolas Travers (2022). A Global Model-Driven Denormalization Approach for Schema Migration. In International Conference on Research Challenges in Information Science (pp. 529-545). Barcelona, Spain: Springer- [Abstract]
  Abstract. With data's evolution in terms of volume, variety, and ve- locity, Information Systems (IS) administrators have to steadily adapt their data model and choose the best solution(s) to store and manage data in accordance with users' requirements. In this context, many exist- ing solutions transform a source data model into a target one, but none of them leads the administrator to choose the most suitable model by offering a limited solution space automatically calculated and adapted to his needs. We propose ModelDrivenGuide, an automatic global approach for leading the model transformation process. It starts by transforming the conceptual model into a logical model, and it defines refinement rules that help to generate all possible data models. Our approach then relies on a heuristic to reduce the search space by avoiding cycles and redun- dancies. We also propose a formalisation of the denormalization process and we discuss the completeness and the complexity of our approach.

  Fermer

• Faten Atigui, Asma Mokrani & Nicolas Travers (2020). DataGuide : une approche pour l'implantation de schémas NoSQL. Extraction et Gestion de Connaissances, . Bruxelles, Belgique

• Nicolas Travers (2018). Big Data Modelization: From Conception to Optimization. Russian-French seminar on Big Data Applications, . Paris, France

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