Sonia Djebali

@inproceedings{guerard_3074,

title = {Omics Challenges in Health and Biology},

author = {Guillaume Guérard and Sonia Djebali},

url = {https://roadef2024.sciencesconf.org/},

year  = {2024},

date = {2024-03-01},

booktitle = {ROADEF 2024},

address = {Amiens, France},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{djebali_3062,

title = {Clustering Mixed Data Comprising Time Series},

author = {Sonia Djebali and Guillaume Guérard and Loup-Noé Levy},

url = {https://doi.org/10.1145/3628797.3628968},

issn = {9798400708916},

year  = {2023},

date = {2023-12-01},

booktitle = {SOICT '23: Proceedings of the 12th International Symposium on Information and Communication Technology},

pages = {110-117},

address = {Ho Chi Minh, Vietnam},

abstract = {The health and medicine sector is currently experiencing significant transformations, such as the integration of artificial intelligence in the decision-making process. In this complex system, there is a continuous data flow consisting of quantitative, qualitative, ordinal types, and time series. Hierarchical clustering is a useful tool to handle this complexity. However, clustering mixed data containing time series without distorting the inherent nature of the data poses a challenge. Although there are existing clustering techniques for mixed data or time series, the literature does not address the clustering of mixed data and time series. This paper presents several methodologies for such data clustering, including a novel algorithm based on pretopology. This hierarchical algorithm allows for customizable logical clustering, enabling health experts to better interpret and utilize the results for classification and recommendation by analyzing the hierarchy of clusters.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{djebali_1936,

title = {Hierarchical Clustering and Measure for Tourism Profiling},

author = {Sonia Djebali and Quentin Gabot and Guillaume Guérard},

url = {https://link.springer.com/chapter/10.1007/978-3-031-25198-6_12},

year  = {2023},

date = {2023-02-01},

booktitle = {6th APWeb-WAIM International Joint Conference on Web and Big Data},

volume = {13422},

pages = {158-165},

address = {Nanjing, China},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{deseure-charron_1935,

title = {Clustering Method for Touristic Photographic Spots Recommendation},

author = {Flavien Deseure-Charron and Sonia Djebali and Guillaume Guérard},

url = {https://link.springer.com/chapter/10.1007/978-3-031-22137-8_17},

year  = {2022},

date = {2022-11-01},

booktitle = {18th International Conference on Advanced Data Mining and Applications},

address = {Brisbane, Australia},

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{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{guerard_1662,

title = {Tourists Profiling by Interest Analysis},

author = {Guillaume Guérard and Sonia Djebali and Quentin Gabot},

url = {https://link.springer.com/chapter/10.1007/978-3-030-95408-6_4},

issn = {978-3-030-95407-9},

year  = {2022},

date = {2022-01-01},

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

volume = {13088},

pages = {42-53},

publisher = {Springer, Cham},

address = {Virtual},

abstract = {With the recent digital revolution, analyzing of tourists' behaviors

and research elds associated with it have changed profoundly.

It is now easier to examine behaviors of tourists using digital traces they

leave during their travels. The studies conducted on diverse aspects of

tourism focus on quantitative aspects of digital traces to reach its conclusions.

In this paper, we suggest a study focused on both qualitative and

quantitative aspect of digital traces to understand the dynamics governing

tourist behavior, especially those concerning attractions networks.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{guerard_1601,

title = {Hidden Markov Model to Predict Tourists Visited Places},

author = {Guillaume Guérard and Sonia Djebali and Bi Chongke and Theo Demessance},

editor = {Vana Kalogeraki, Nick Koudas, Jianliang Xu},

url = {https://ieeexplore.ieee.org/abstract/document/9474900},

issn = {978-1-6654-2845-3},

year  = {2021},

date = {2021-06-01},

booktitle = {22nd IEEE International Conference on Mobile Data Management},

pages = {209-216},

publisher = {IEEE},

address = {virtual},

abstract = {Nowadays, social networks is become a popular means of analyzing tourist behavior, thanks to the digital traces left by travelers during their stays on these networks. 

The massive amount of data generated by the propensity of tourists to share comments and photos during their trip makes it possible to model their journeys and analyze their behavior. 

Predicting the next movement of tourist plays a key role in tourism marketing to understand demand and improve decision support. 



In this paper, we propose a method to understand and to learn tourists' movements based on social network data analysis to predict the future movements. The method relies on a machine learning grammatical inference algorithm. 

A major contribution in this paper is to adapt the grammatical inference algorithm to the context of big data. 

Our method produces a hidden Markov model representing the movements of a group of tourists. The hidden Markov model is flexible and editable with new data. 

The capital city of France, Paris is selected to demonstrate the efficiency of the proposed methodology.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{djebali_1602,

title = {Bidding strategy of a seasonal storage and pump storage hydropower plants in reserve markets},

author = {Sonia Djebali and Guillaume Guérard and Laureen Deman and Quentin Boucher and Cedric Clastres},

url = {https://iaee2021online.org/download/contribution/fullpaper/1380/1380_fullpaper_20210614_063233.pdf},

year  = {2021},

date = {2021-06-01},

booktitle = {International Association for Energy Economics},

address = {virtual},

abstract = {The increasing share of intermittent sources of energy will increase the need for frequency-control reserves. However, the  current  supply  of  reserves  might  decrease  in  the  following  years.  The  share  of  gas-  and  coal-fuel  plants  in  the power mix is expected to decline in order to reduce greenhouse gas emissions. Hydropower technologies are often put forward as mature and low-carbon technologies able to contribute to cover this increasing need for reserves.  Their flexibility and storage capability allow them to support the stable operation of the grid. The procurement of reserves being  mostly  market-based  in  Europe,  the  market  design  should  send  the  correct  price  signals  to  encourage  the participation in these markets.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{guerard_1600,

title = {Tourism Management through the Big Data Paradigm},

author = {Guillaume Guérard and Sonia Djebali},

editor = {David Chavalarias, Hocine Cherifi},

url = {https://easychair.org/smart-program/FRCCS2021/},

year  = {2021},

date = {2021-05-01},

booktitle = {French Regional Conference on Complex Systems FRCCS},

publisher = {Springer - Applied Network Science Journal},

address = {virtual},

abstract = {This paper highlights the two-ways relationship between data and entities producing or generating these data in the context of tourism management. Indeed,  tourists leave various traces of their planning and trip on social media and networks. Moreover, studying those data to feed tourism marketing also affects the future data left by tourists. Understanding the intrinsic and extrinsic relationship between entities should significantly improve the knowledge in this research field.},

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{djebali_1226,

title = {Prédiction des comportements touristiques par minage des motifs et des règles séquentielles},

author = {Sonia Djebali and Guillaume Guérard},

url = {https://roadef2020.sciencesconf.org/},

year  = {2020},

date = {2020-02-01},

booktitle = {21ème édition du congrès annuel de la Société Française de Recherche Opérationnelle et d'Aide à la Décision},

address = {Montpellier, France},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ben_baccar_989,

title = {Tourist's Tour Prediction by Sequential Data Mining Approach},

author = {Lillia Ben Baccar and Sonia Djebali and Guillaume Guérard},

url = {https://link.springer.com/chapter/10.1007/978-3-030-35231-8_50},

issn = {ISBN 978-3-030-35230-1},

year  = {2019},

date = {2019-11-01},

booktitle = {Advanced Data Mining and Applications, 15th International Conference, ADMA 2019},

pages = {pp. 681-695},

address = {Dalian, China},

abstract = {This paper answers the problem of predicting future behaviour tourist based on past behaviour of an individual tourist. The individual behaviour is naturally an indicator of the behaviour of other tourists. The prediction of tourists movement has a crucial role in tourism marketing to create demand and assist tourists in decision-making. With advances in information and communication technology, social media platforms generate data from millions of people from different countries during their travel. The main objective of this paper is to consider sequential data-mining methods to predict tourist movement based on Instagram data. Rules emerge from those ones are exploited to predict future behaviors. The originality of this approach is a combination between pattern mining to reduce the size of data and the automata to condense the rules. The capital city of France, Paris is selected to demonstrate the utility of the proposed methodology.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{raimbault_503,

title = {Universal-endpoint.com : une plateforme d'accès simple au Web des Données},

author = {Thomas Raimbault and Abdellah Sabry and Sonia Djebali},

url = {https://editions-rnti.fr/?procid=100174},

issn = {ISBN : 979-10-96289-07-3},

year  = {2018},

date = {2018-01-01},

booktitle = {EGC 2018},

volume = {RNTI-E-34},

pages = {475-478},

address = {Paris, France},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{loiseau_276,

title = {Characterization of daily tourism behaviors based on place sequence analysis from photo sharing websites},

author = {Stéphane Loiseau and Sonia Djebali and Thomas Raimbault and Bérangère Branchet and Gaël Chareyron},

url = {https://ieeexplore.ieee.org/document/8258241},

issn = {ISBN: 978-1-5386-2714-3},

year  = {2017},

date = {2017-12-01},

booktitle = {2017 IEEE International Conference on Big Data},

pages = {2760 - 2765},

address = {Boston, USA},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{raimbault_286,

title = {Interroger intuitivement le Web des Données avec SimplePARQL},

author = {Thomas Raimbault and Sonia Djebali},

url = {https://pfia2017.greyc.fr/apia/programme},

year  = {2017},

date = {2017-07-01},

booktitle = {Conférence Nationale sur les Applications Pratiques de l'Intelligence Artificielle},

address = {Caen, France},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{djebali_280,

title = {Regrouper des résultats SPARQL par comparaison de leurs contenus tels qu'ils sont agencés dans la base RDF interrogée},

author = {Sonia Djebali and Thomas Raimbault},

url = {http://www.asso-aria.org/coria/2017/papers/coria.2017.9/},

year  = {2017},

date = {2017-03-01},

booktitle = {CORIA},

address = {Marseille, France},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{guerard_272,

title = {Rendre 'intelligent' le Smart Grid},

author = {Guillaume Guérard and Sonia Djebali},

url = {http://roadef2017.event.univ-lorraine.fr/index.html},

year  = {2017},

date = {2017-02-01},

booktitle = {18ème conférence annuelle de la Société Française de Recherche Opérationnelle et d'Aide à la Décision},

address = {Metz, France},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{djebali_388,

title = {SimplePARQL: a new approach using keywords over SPARQL to query the web of data},

author = {Sonia Djebali and Thomas Raimbault},

url = {http://ceur-ws.org/Vol-1481/},

year  = {2015},

date = {2015-09-01},

booktitle = {Proceedings of the Posters and Demos Track of 11th International Conference on Semantic Systems - SEMANTiCS2015},

volume = {1481},

pages = {p43},

address = {Vienna, Austria},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}