Nicolas Travers

@inproceedings{benjira_3263,

title = {Web Open Data to SDG Indicators: Towards an LLM-Augmented Knowledge Graph Solution},

author = {Wissal Benjira and Faten Atigui and Benedicte Bucher and Malika Grim-Yefsah and Nicolas Travers},

url = {https://wise2024-qatar.com/program/},

year  = {2024},

date = {2024-12-01},

booktitle = {International Conference on Web Information Systems Engineering},

pages = {1-10},

address = {Doha, Qatar},

abstract = {MeetingtheSustainableDevelopmentGoals(SDGs)presents a large-scale challenge for all countries. SDGs established by the United Nations provide a comprehensive framework for addressing global chal- lenges. To monitor progress towards these goals, we need to develop key performance indicators and integrate and analyze heterogeneous datasets. The definition of these indicators requires the use of existing data and metadata, in particular open data. This approach aims to high- light the positive impact of the web on the society. However, the diversity of web data sources and formats raises major issues in terms of structur- ing and integration. Despite the abundance of open data and metadata, its exploitation remains limited, leaving untapped potential for guiding urban policies towards sustainability. We have so far introduced a novel approach for SDG indicator computation, leveraging the capabilities of Large Language Models (LLMs) and Knowledge Graphs (KGs). We have proposed a method that combines rule-based filtering with LLM-powered schema mapping to establish semantic correspondences between diverse data sources and SDG indicators, including disaggregated attributes. Our approach integrated these mappings into a KG, which enables indicator computation by querying graph's topology. Finally, we have evaluated our method through a case study focusing on the SDG Indicator 11.7.1 about accessibility of public open spaces. Our experimental results show significant improvements compared to traditional schema matching tech- niques.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{foutse_2898,

title = {How to Recommend Multidimensional Data with a Multiplex Graph?},

author = {Yuehgoh Foutse and Sonia Djebali and Nicolas Travers},

url = {https://aciids.pwr.edu.pl/2024/index.php#about},

issn = {978-981-97-4984-3},

year  = {2024},

date = {2024-07-01},

booktitle = {Asian Conference on Intelligent Information and Database Systems},

volume = {LNAI 14796},

pages = {332-344},

publisher = {Springer},

address = {Ras Al Khaimah, UAE},

abstract = {Network Science has become a flourishing interest in the last decades with the Big Data explosion. To improve multidimensional data Recommendation Systems, multiplex graph structures are useful to capture various types of user interactions. We propose a graph database approach to compute multiplex graphs which helps both manipulating dimensions in a flexible way and enhancing expressiveness with algebra to express manipulations on the multiplex graph. Applied operations rely on graph algorithms to predict user interactions. We compare our approach with the traditional matrix approach with Random Walk with Restart. The study shows that combination of scores from layers of multiplex graphs provide important insights into user preferences, with most configurations outperforming traditional matrix methods.

This approach provides a comprehensive analysis of multidimensional recommendation strategies in multiplex graphs, which provides capabilities of managing different dimensions for queries, paving the way for more sophisticated and customized recommendation systems and its explicability.},

note = {16th Asian Conference on Intelligent Information and Database Systems

15-18 April 2024, Ras Al Khaimah, UAE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mali_2896,

title = {FACT-DM : A Framework for Automated Cost-Based Data Model Transformation},

author = {Jihane Mali and Ahvar Shohreh and Faten Atigui and Ahmed Azough and Nicolas Travers},

url = {https://doi.org/10.48786/edbt.2024.79},

year  = {2024},

date = {2024-03-01},

booktitle = {Extending Database Technology},

pages = {822--825},

publisher = {OpenProceedings.org},

address = {Paestum, Italy},

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.},

note = {25-28/03/2024},

keywords = {},

pubstate = {online},

tppubtype = {inproceedings}

}

@inproceedings{lefebvre_2897,

title = {NeoSGG: A Scene Graph Generation Framework for Video-Surveillance Tasks},

author = {Pierre Lefebvre and Steven Le Moal and Ahmed Azough and Nicolas Travers},

url = {https://openproceedings.org/2024/conf/edbt/paper-244.pdf},

year  = {2024},

date = {2024-03-01},

booktitle = {Extending Database Technology},

pages = {838--841},

publisher = {OpenProceedings.org},

address = {Paestum, Italie},

abstract = {Video surveillance has developed considerably in the recent years. Analyzing the data generated by such systems has become a major challenge. To address this issue, we propose a framework for the creation of rich Labeled Property Graphs from video surveillance streams. It is based on 1) a Deep Learning pipeline architecture for video data extraction, 2) a graph database module to efficiently structure and store detections, and 3) a querying module to interact with generated graphs, enhancing the automatic analysis of scenes. Its modular architecture enables the feature extraction steps from the videos to be easily maintained, modified or interchanged. Our demonstration scenario shows the process of generating scene graphs from videos of several benchmark datasets. The audience will assist to an end-to-end execution of the pipeline showing the generation process and visualize generated graphs. They will have the opportunity to formulate queries using an interface illustrating several use case scenarios involving person re-identification and abandoned objects matching with their former owners.},

note = {25-28/03/2024},

keywords = {},

pubstate = {online},

tppubtype = {inproceedings}

}

@inproceedings{benjira_2895,

title = {Modélisation des données ouvertes au service des ODD : application aux événements sportifs en ville},

author = {Wissal Benjira and Nicolas Travers and Malika Grim-Yefsah and Faten Atigui and Benedicte Bucher},

url = {https://iutdijon.u-bourgogne.fr/egc2024/articles-acceptes/},

year  = {2024},

date = {2024-01-01},

booktitle = {24ème conférence francophone sur l'Extraction et la Gestion des Connaissances},

volume = {40},

pages = {255-262},

publisher = {RNTI},

address = {Dijon, France},

abstract = {Atteindre les Objectifs de Développement Durable (ODD) est un enjeu majeur auquel nous devons faire face à l'horizon de 2030. Pour cela, de nombreux indicateurs sont indispensable et requièrent de les comparer aussi bien dans le temps et dans l'espace afin d'évaluer ces progrès. La définition de ces indicateurs nécessite l'exploitation de données existantes notamment les données ouvertes. Toutefois, la diversité des sources de données et des formats pose des défis majeurs en termes de structuration et d'intégration. Malgré l'abondance des données ouvertes, leur exploitation reste limitée, laissant un potentiel inexploité pour orienter les politiques urbaines vers la durabilité. Cet article présente une approche de modélisation dirigée par les données pour les représenter. Nous illustrons l'application de cette méthode aux ODD, et aux événements sportifs ouvrant ainsi de nouvelles perspectives pour établir le rapprochement entre données et calcul d'indicateurs.},

note = {22-26/01/2024},

keywords = {},

pubstate = {online},

tppubtype = {inproceedings}

}

@inproceedings{david_2413,

title = {NeoMaPy: a Parametric Framework for Reasoning with MAP Inference on Temporal Markov Logic Networks},

author = {Victor David and Raphaël Fournier-S'niehotta and Nicolas Travers},

editor = {ACM},

url = {https://dl.acm.org/doi/10.1145/3583780.3614757},

year  = {2023},

date = {2023-10-01},

booktitle = {ACM International Conference on Information and Knowledge Management},

address = {Birmingham, UK},

abstract = {Reasoning on inconsistent and uncertain data is challenging, especially for Knowledge-Graphs (KG) to abide temporal consistency. Our goal is to enhance inference with more general time interval semantics that specify their validity, as regularly found in historical sciences. We propose a new Temporal Markov Logic Networks (TMLN) model which extends the Markov Logic Networks (MLN) model with uncertain temporal facts and rules. Total and partial temporal (in)consistency relations between sets of temporal formulae are examined. We then propose a new Temporal Parametric Semantics (TPS) which allows combining several sub-functions leading to different assessment strategies. Finally, we present the new NeoMaPy tool, to compute the MAP inference on MLNs and TMLNs with several TPS. We compare our performances with state-of-the-art inference tools and exhibit faster and higher quality results.},

keywords = {},

pubstate = {online},

tppubtype = {inproceedings}

}

@inproceedings{david_2307,

title = {NeoMaPy: a Framework for Computing MAP inference on Temporal Knowledge Graphs},

author = {Victor David and Raphaël Fournier-S'niehotta and Nicolas Travers},

url = {https://www.ijcai.org/proceedings/2023/831},

year  = {2023},

date = {2023-08-01},

booktitle = {International Joint Conference on Artificial Intelligence},

pages = {7123-7126},

address = {Macao, China},

abstract = {Markov Logic Networks (MLN) are used for rea- soning on uncertain and inconsistent temporal data. We proposed the TMLN (Temporal Markov Logic Network) which extends them with sorts/types, weights on rules and facts, and various temporal consistencies. The NeoMaPy framework integrates it in a knowledge graph based on conflict graphs, which offers flexibility for reasoning with parame- tric Maximum A Posteriori (MAP) inferences, effi- ciency thanks to an optimistic heuristic and interac- tive graph visualization for results explanation.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{martin-prin_1890,

title = {A Distributed SAT-based Framework for Closed Frequent Itemset Mining},

author = {Julien Martin-Prin and Imen Ouled Dlala and Nicolas Travers and Said Jabbour},

url = {https://adma2022.uqcloud.net/important_date.html},

year  = {2022},

date = {2022-11-01},

booktitle = {International Conference on Advanced Data Mining and Applications},

publisher = {Springer},

address = {Brisbane, Australia},

abstract = {Frequent Itemset Mining is an essential part of data mining. SAT-based approaches that extract frequent itemsets in big data encounter significant challenges when computing power and storage capacity are limited. This paper proposes an efficient distributed SAT-based framework for the Closed Frequent Itemset Mining problem (CFIM) which minimizes communications throughout the distributed architecture and reduces bottlenecks due to shared memory. Moreover, it enhances scalability and fault tolerance. This approach makes use of a Computation-Distributed Paradigm to enumerate efficiently the set of all closed itemsets, by reducing the processing time. To the best of our knowledge, this paper presents the first attempt towards a distributed SAT-based approach for CFIM. An extensive empirical evaluation on various real-word datasets shows the efficiency of the approach.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{prevoteau_1921,

title = {Propagation Measure on Circulation Graphs for Tourism Behavior Analysis},

author = {Hugo Prevoteau and Sonia Djebali and Zhao Laiping and Nicolas Travers},

url = {https://bda2022.sciencesconf.org/resource/page/id/13},

year  = {2022},

date = {2022-10-01},

booktitle = {Bases de Données Avancées},

address = {Clermont-Ferrand, France},

abstract = {Social network analysis has widespread in recent years, especially in digital tourism. Indeed the large amount of data that tourists produce during their travels represents an effective source to un- derstand their behavior and is of great importance for tourism stakeholders. This paper studies the propagation effect of tourists on the territory thanks to geotagged circulation graphs.

A new weighted measure is introduced for circulation charac- terization based on both topologies and distances. This measure helps to determine the behavior of tourists on local and global areas. An optimization strategy based on spanning trees is applied to reduce the computation on the whole graph while keeping a good approximation of the behavior.},

keywords = {},

pubstate = {online},

tppubtype = {inproceedings}

}

@inproceedings{mali_1804,

title = {A Global Model-Driven Denormalization Approach for Schema Migration},

author = {Jihane Mali and Ahvar Shohreh and Faten Atigui and Ahmed Azough and Nicolas Travers},

url = {https://link.springer.com/chapter/10.1007/978-3-031-05760-1_31},

year  = {2022},

date = {2022-05-01},

booktitle = {International Conference on Research Challenges in Information Science},

volume = {446},

pages = {529-545},

publisher = {Springer},

address = {Barcelona, Spain},

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.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{chatzopoulou_2883,

title = {Authenticity Goes Digital: A Big Data Analysis of the Influence of the Country of Origin and Authenticity Perceptions on TripAdvisor Ethnic Restaurant Reviews},

author = {Elena Chatzopoulou and Nicolas Travers},

url = {https://link.springer.com/chapter/10.1007/978-3-030-89883-0_2},

year  = {2022},

date = {2022-05-01},

booktitle = {Academy of Marketing Science Annual Conference},

pages = {3-15},

publisher = {Springer},

address = {online},

abstract = {Authenticity perceptions are subjectively driven and rely on social constructions making the concept hard to be defined. The current study is following a big data approach to capture perceptions and beliefs concerning the authenticity of ethnic restaurants and also when online positive reviews are given about authenticity under the influence of a visit to the country of origin. The key idea of our method relies on the analysis of a 3-step characterization of a big data repository extracted from TripAdvisor. Step 0 concerns reviews made for Italian restaurants before consumers visit Italy, step 1 concerns the reviews made while consumers were in Italy and step 2 concerns reviews made after they visited Italy. This characterization exploits both sentiment analysis and graph data models. Our findings propose a depiction of authenticity for ethnic restaurants via e-word of mouth. With a big data analysis on TripAdvisor, we provided an analysis on both ratings and comments which showed the impact of authenticity. As such, consumers, after visiting the country of origin, were more critical while they provided lower ratings and they were also focusing more on authentic atmosphere and service, showing evolution of their online reviews.},

note = {25-27/05/2022},

keywords = {},

pubstate = {online},

tppubtype = {inproceedings}

}

@inproceedings{prevoteau_1749,

title = {Propagation Measure on Circulation Graphs for Tourism Behavior Analysis},

author = {Hugo Prevoteau and Sonia Djebali and Zhao Laiping and Nicolas Travers},

url = {https://www.sigapp.org/sac/sac2022/index.html},

year  = {2022},

date = {2022-04-01},

booktitle = {ACM Symposium on Applied Computing},

publisher = {ACM},

address = {Brno, Czec Republic},

abstract = {Social network analysis has widespread in recent years, especially in digital tourism. Indeed the large amount of data that tourists produce during their travels represents an effective source to understand their behavior and is of great importance for tourism stakeholders. This paper studies the propagation effect of tourists on the territory thanks to geotagged circulation graphs. Those graphs reflect traffic flows which need to be analyzed over time and space. A new weighted measure is introduced for circulation characterization based on both topologies and distances. This measure helps to determine the behavior of tourists on local and global areas. An optimization strategy based on spanning trees is applied to reduce the computation on the whole graph while keeping a good approximation of the behavior. The approach is simulated on various graphs and evaluated experimentaly over a real dataset at various geographic zones, scales, communities, and time.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{djebali_1273,

title = {Indicators for Measuring Tourist Mobility},

author = {Sonia Djebali and Nicolas Loas and Nicolas Travers},

url = {http://wasp.cs.vu.nl/WISE2020/accept.html},

issn = {978-3-030-62004-2},

year  = {2020},

date = {2020-10-01},

booktitle = {International Conference on Web Information Systems Engineering},

address = {Amsterdam and Leiden, Netherlands},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mali_1381,

title = {How to Implement NoSQL Schemas with ModelDrivenGuide?},

author = {Jihane Mali and Faten Atigui and Ahmed Azough and Nicolas Travers},

url = {https://bda.lip6.fr/programme/},

year  = {2020},

date = {2020-10-01},

booktitle = {36ème conférence sur la Gestion de données},

address = {virtual},

abstract = {With the evolution of data in terms of volume, variety and velocity, designing and developing an Information Systems (IS) requires studying the best solutions to store and manipulate data while respecting the user's requirements. In this demonstration, we show how to implement an IS using the ModelDrivenGuide, which is a semi-automated approach based on transformation rules starting from a conceptual model, then going from one logical model to an other by re?nement to finally the chosen physical model.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mali_1274,

title = {ModelDrivenGuide: An Approach for Implementing NoSQL Schemas},

author = {Jihane Mali and Faten Atigui and Ahmed Azough and Nicolas Travers},

url = {https://www.dexa.org/previous/dexa2020/dexa2020.html},

year  = {2020},

date = {2020-09-01},

booktitle = {The 31st International Conference on Database and Expert Systems Applications DEXA 2020},

address = {Bratislava, Slovakia},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{chareyron_1070,

title = {Tourism Analysis on Graphs with Neo4Tourism},

author = {Gaël Chareyron and Ugo Quelhas and Nicolas Travers},

url = {https://wise2019.comp.polyu.edu.hk/

https://link.springer.com/chapter/10.1007%2F978-981-15-3281-8_4},

issn = {978-981-15-3280-1},

year  = {2020},

date = {2020-02-01},

booktitle = {International Conference on Web Information Systems Engineering},

address = {Hong Kong, China},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rigaux_888,

title = {Scalable Searching and Ranking for Melodic Pattern Queries},

author = {Philippe Rigaux and Nicolas Travers},

url = {https://ismir2019.ewi.tudelft.nl/},

year  = {2019},

date = {2019-11-01},

booktitle = {20th annual conference of the International Society for Music Information Retrieval},

address = {Delft, Netherlands},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{dupuis_942,

title = {RTIM: a Real-Time Influence Maximization Strategy},

author = {David Dupuis and Cédric Du Mouza and Nicolas Travers and Gaël Chareyron},

url = {https://link.springer.com/book/10.1007/978-3-030-34223-4},

issn = {ISBN : 978-3-030-34222-7},

year  = {2019},

date = {2019-11-01},

booktitle = {International Conference on Web Information Systems Engineering},

volume = {11881},

pages = {pp 277-292},

address = {Hong-Kong, China},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{grossetti_944,

title = {Community-based Recommendations on Twitter: Avoiding The Filter Bubble},

author = {Quentin Grossetti and Cédric Du Mouza and Nicolas Travers},

url = {https://link.springer.com/chapter/10.1007/978-3-030-34223-4_14},

issn = {ISBN : 978-3-030-34222-7},

year  = {2019},

date = {2019-11-01},

booktitle = {Web Information Systems Engineering - WISE 2019},

volume = {11881},

pages = {pp 212-227},

address = {Hong-Kong, China},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{dupuis_941,

title = {Influence Maximization in a Real-Time Bidding Environment},

author = {David Dupuis and Cédric Du Mouza and Nicolas Travers and Gaël Chareyron},

url = {https://bda.liris.cnrs.fr/list.html},

year  = {2019},

date = {2019-10-01},

booktitle = {35ème Conférence sur la Gestion de Données},

address = {Lyon, France},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{grossetti_563,

title = {Reducing Filter Bubbles With a Community Aware Model},

author = {Quentin Grossetti and Camélia Constantin and Cédric Du Mouza and Nicolas Travers},

url = {https://bda2018.ensea.fr/},

year  = {2018},

date = {2018-10-01},

booktitle = {34ème Conférence sur la Gestion de Données},

address = {Bucarest, Romania},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}