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199 results on '"Masseglia, Florent"'

1. SoftED: Metrics for Soft Evaluation of Time Series Event Detection

Author

Salles, Rebecca, Lima, Janio, Coutinho, Rafaelli, Pacitti, Esther, Masseglia, Florent, Akbarinia, Reza, Chen, Chao, Garibaldi, Jonathan, Porto, Fabio, and Ogasawara, Eduardo

Subjects
Computer Science - Machine Learning
Abstract

Time series event detection methods are evaluated mainly by standard classification metrics that focus solely on detection accuracy. However, inaccuracy in detecting an event can often result from its preceding or delayed effects reflected in neighboring detections. These detections are valuable to trigger necessary actions or help mitigate unwelcome consequences. In this context, current metrics are insufficient and inadequate for the context of event detection. There is a demand for metrics that incorporate both the concept of time and temporal tolerance for neighboring detections. This paper introduces SoftED metrics, a new set of metrics designed for soft evaluating event detection methods. They enable the evaluation of both detection accuracy and the degree to which their detections represent events. They improved event detection evaluation by associating events and their representative detections, incorporating temporal tolerance in over 36\% of experiments compared to the usual classification metrics. SoftED metrics were validated by domain specialists that indicated their contribution to detection evaluation and method selection., Comment: 19 pages

Published
2023

3. Life Science Workflow Services (LifeSWS): Motivations and Architecture

Author

Akbarinia, Reza, Botella, Christophe, Joly, Alexis, Masseglia, Florent, Mattoso, Marta, Ogasawara, Eduardo, de Oliveira, Daniel, Pacitti, Esther, Porto, Fabio, Pradal, Christophe, Shasha, Dennis, Valduriez, Patrick, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hameurlain, Abdelkader, editor, and Tjoa, A Min, editor

Published
2023
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4. Variable-Size Segmentation for Time Series Representation

Author

Djebour, Lamia, Akbarinia, Reza, Masseglia, Florent, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hameurlain, Abdelkader, editor, and Tjoa, A Min, editor

Published
2023
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5. Feedback Clustering for Online Travel Agencies Searches: a Case Study

Author

Scaramuccia, Sara, Nanty, Simon, and Masseglia, Florent

Subjects
Computer Science - Information Retrieval, G.2.3, I.5.3
Abstract

Understanding choices performed by online customers is a growing need in the travel industry. In many practical situations, the only available information is the flight search query performed by the customer with no additional profile knowledge. In general, customer flight bookings are driven by prices, duration, number of connections, and so on. However, not all customers might assign the same importance to each of those criteria. Here comes the need of grouping together all flight searches performed by the same kind of customer, that is having the same booking criteria. The effectiveness of some set of recommendations, for a single cluster, can be measured in terms of the number of bookings historically performed. This effectiveness measure plays the role of a feedback, that is an external knowledge which can be recombined to iteratively obtain a final segmentation. In this paper, we describe our Online Travel Agencies (OTA) flight search use case and highlight its specific features. We address the flight search segmentation problem motivated above by proposing a novel algorithm called Split-or-Merge (S/M). This algorithm is a variation of the Split-Merge-Evolve (SME) method. The SME method has already been introduced in the community as an iterative process updating a clustering given by the K-means algorithm by splitting and merging clusters subject to feedback independent evaluations. No previous application of the SME method to the real-word data is reported in literature to the best of our knowledge. Here, we provide experimental evaluations over real-world data to the SME and the S/M methods. The impact on our domain-specific metrics obtained under the SME and the S/M methods suggests that feedback clustering techniques can be very promising in the handling of the domain of OTA flight searches.

Published
2020

6. Apprentissage de la pens\'ee informatique : de la formation des enseignant$\cdot$e$\cdot$s \`a la formation de tou$\cdot$te$\cdot$s les citoyen$\cdot$ne$\cdot$s

Author

Atlan, Corinne, Archambault, Jean-Pierre, Banus, Olivier, Bardeau, Frédéric, Blandeau, Amélie, Cois, Antonin, Courbin, Martine, Giraudon, Gérard, Lefèvre, Saint-Clair, Letard, Valérie, Masse, Bastien, Masseglia, Florent, Ninassi, Benjamin, de Quatrebarbes, Sophie, Romero, Margarida, Roy, Didier, and Vieville, Thierry

Subjects
Computer Science - Computers and Society
Abstract

In recent years, in France, computer learning (under the term of code) has entered the school curriculum, in primary and high school. This learning is also aimed at developing computer thinking to enable students, girls and boys, to start master all aspects of the digital world (science, technology, industry, culture). However, neither teachers, nor parents are trained to teach or educate on these topics. Furthermore, if the educational system progresses progressively towards these objectives, in everyday life and in professional context there is also a need for lifelong training in computer thinking. Large-scale projects on coding initiation are now quite successful in supporting the training of professionals in education on these topics. However, they require an infrastructure of people and important resources to maintain their level of efficiency. In order to further develop the objectives ofhelping people to demystify IT thinking, we aim to question here the way by which it is possible to conceive a concrete and operational initiative that addresses this issue. A huge challenge: Let's share a proposal here and discuss it., Comment: in French. Revue de l'EPI (Enseignement Public et Informatique), EPI, 2019

Published
2019

8. Parallel Techniques for Variable Size Segmentation of Time Series Datasets

Author

Djebour, Lamia, Akbarinia, Reza, Masseglia, Florent, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Chiusano, Silvia, editor, Cerquitelli, Tania, editor, and Wrembel, Robert, editor

Published
2022
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9. A Distributed Collaborative Filtering Algorithm Using Multiple Data Sources

Author

Bouadjenek, Mohamed Reda, Pacitti, Esther, Servajean, Maximilien, Masseglia, Florent, and Abbadi, Amr El

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence
Abstract

Collaborative Filtering (CF) is one of the most commonly used recommendation methods. CF consists in predicting whether, or how much, a user will like (or dislike) an item by leveraging the knowledge of the user's preferences as well as that of other users. In practice, users interact and express their opinion on only a small subset of items, which makes the corresponding user-item rating matrix very sparse. Such data sparsity yields two main problems for recommender systems: (1) the lack of data to effectively model users' preferences, and (2) the lack of data to effectively model item characteristics. However, there are often many other data sources that are available to a recommender system provider, which can describe user interests and item characteristics (e.g., users' social network, tags associated to items, etc.). These valuable data sources may supply useful information to enhance a recommendation system in modeling users' preferences and item characteristics more accurately and thus, hopefully, to make recommenders more precise. For various reasons, these data sources may be managed by clusters of different data centers, thus requiring the development of distributed solutions. In this paper, we propose a new distributed collaborative filtering algorithm, which exploits and combines multiple and diverse data sources to improve recommendation quality. Our experimental evaluation using real datasets shows the effectiveness of our algorithm compared to state-of-the-art recommendation algorithms., Comment: The Tenth International Conference on Advances in Databases, Knowledge, and Data Applications, DBKDA 2018 May 20, 2018 to May 24, 2018 - Nice, France

Published
2018

10. Anomaly Detection in Time Series

Author

Borges, Heraldo, Akbarinia, Reza, Masseglia, Florent, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hameurlain, Abdelkader, editor, and Tjoa, A Min, editor

Published
2021
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11. Massively Distributed Clustering via Dirichlet Process Mixture

Author

Meguelati, Khadidja, Fontez, Benedicte, Hilgert, Nadine, Masseglia, Florent, Sanchez, Isabelle, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Dong, Yuxiao, editor, Ifrim, Georgiana, editor, Mladenić, Dunja, editor, Saunders, Craig, editor, and Van Hoecke, Sofie, editor

Published
2021
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12. Distributed Algorithms to Find Similar Time Series

Author

Levchenko, Oleksandra, Kolev, Boyan, Yagoubi, Djamel-Edine, Shasha, Dennis, Palpanas, Themis, Valduriez, Patrick, Akbarinia, Reza, Masseglia, Florent, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Brefeld, Ulf, editor, Fromont, Elisa, editor, Hotho, Andreas, editor, Knobbe, Arno, editor, Maathuis, Marloes, editor, and Robardet, Céline, editor

Published
2020
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17. Discovering Tight Space-Time Sequences

Author

Campisano, Riccardo, Borges, Heraldo, Porto, Fabio, Perosi, Fabio, Pacitti, Esther, Masseglia, Florent, Ogasawara, Eduardo, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Ordonez, Carlos, editor, and Bellatreche, Ladjel, editor

Published
2018
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20. Massively Distributed Environments and Closed Itemset Mining: The DCIM Approach

Author

Zitouni, Mehdi, Akbarinia, Reza, Yahia, Sadok Ben, Masseglia, Florent, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Dubois, Eric, editor, and Pohl, Klaus, editor

Published
2017
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21. Data Partitioning for Fast Mining of Frequent Itemsets in Massively Distributed Environments

Author

Salah, Saber, Akbarinia, Reza, Masseglia, Florent, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Chen, Qiming, editor, Hameurlain, Abdelkader, editor, Toumani, Farouk, editor, Wagner, Roland, editor, and Decker, Hendrik, editor

Published
2015
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22. A Prime Number Based Approach for Closed Frequent Itemset Mining in Big Data

Author

Zitouni, Mehdi, Akbarinia, Reza, Yahia, Sadok Ben, Masseglia, Florent, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Chen, Qiming, editor, Hameurlain, Abdelkader, editor, Toumani, Farouk, editor, Wagner, Roland, editor, and Decker, Hendrik, editor

Published
2015
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26. Discovering Tight Space-Time Sequences

Author

Campisano, Riccardo, primary, Borges, Heraldo, additional, Porto, Fabio, additional, Perosi, Fabio, additional, Pacitti, Esther, additional, Masseglia, Florent, additional, and Ogasawara, Eduardo, additional

Published
2018
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29. Fast and Exact Mining of Probabilistic Data Streams

Author

Akbarinia, Reza, Masseglia, Florent, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Blockeel, Hendrik, editor, Kersting, Kristian, editor, Nijssen, Siegfried, editor, and Železný, Filip, editor

Published
2013
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30. A Density-Based Backward Approach to Isolate Rare Events in Large-Scale Applications

Author

Székely, Enikö, Poncelet, Pascal, Masseglia, Florent, Teisseire, Maguelonne, Cezar, Renaud, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Fürnkranz, Johannes, editor, Hüllermeier, Eyke, editor, and Higuchi, Tomoyuki, editor

Published
2013
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32. Mining Common Outliers for Intrusion Detection

Author

Singh, Goverdhan, Masseglia, Florent, Fiot, Céline, Marascu, Alice, Poncelet, Pascal, Kacprzyk, Janusz, editor, Guillet, Fabrice, editor, Ritschard, Gilbert, editor, Zighed, Djamel Abdelkader, editor, and Briand, Henri, editor

Published
2010
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34. Analysing Satellite Image Time Series by Means of Pattern Mining

Author

Petitjean, François, Gançarski, Pierre, Masseglia, Florent, Forestier, Germain, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Fyfe, Colin, editor, Tino, Peter, editor, Charles, Darryl, editor, Garcia-Osorio, Cesar, editor, and Yin, Hujun, editor

Published
2010
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35. Discovering Highly Informative Feature Sets from Data Streams

Author

Zhang, Chongsheng, Masseglia, Florent, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Bringas, Pablo García, editor, Hameurlain, Abdelkader, editor, and Quirchmayr, Gerald, editor

Published
2010
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37. Data Mining for Intrusion Detection: From Outliers to True Intrusions

Author

Singh, Goverdhan, Masseglia, Florent, Fiot, Céline, Marascu, Alice, Poncelet, Pascal, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Theeramunkong, Thanaruk, editor, Kijsirikul, Boonserm, editor, Cercone, Nick, editor, and Ho, Tu-Bao, editor

Published
2009
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38. A Multi-resolution Approach for Atypical Behaviour Mining

Author

Marascu, Alice, Masseglia, Florent, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Theeramunkong, Thanaruk, editor, Kijsirikul, Boonserm, editor, Cercone, Nick, editor, and Ho, Tu-Bao, editor

Published
2009
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40. Sequential Pattern Mining for Structure-Based XML Document Classification

Author

Garboni, Calin, Masseglia, Florent, Trousse, Brigitte, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Dough, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Fuhr, Norbert, editor, Lalmas, Mounia, editor, Malik, Saadia, editor, and Kazai, Gabriella, editor

Published
2006
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41. Variable size segmentation for efficient representation and querying of non-uniform time series datasets

Author

Djebour, Lamia, Akbarinia, Reza, Masseglia, Florent, Scientific Data Management (ZENITH), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)

Subjects
Nearest-neighbor search Time Series, Representations, Time series, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Spatial-temporal systems, Information Retrieval, Nearest-neighbor search, Data mining
Abstract

International audience; Existing approaches for time series similarity computing are the core of many data analytics tasks. Given the considered data volumes, or simply the need for fast response times, they often rely on shorter representations, usually with information loss. This incurs approximate comparisons where precision is a major issue. We present and experimentally evaluate ASAX, a new approach for segmenting time series before their transformation into symbolic representations. ASAX reduces significantly the information loss incurred by possible splittings at different steps of the representation calculation, particularly for datasets with unbalanced (nonuniform) distributions. We provide theoretical guarantees on the lower bound of similarity measures, and our experiments illustrate that our method outperforms the state of the art, with significant gain in precision for datasets with unbalanced distributions.

Published
2022

42. The 6th international workshop on Multimedia Data Mining (MDM/KDD2005)

Author

Bouali, Fatma, Khan, Latifur, and Masseglia, Florent

Abstract

In this report, we provide a summary of the issues and research directions on Multimedia Data Mining and the outcomes of the MDM/KDD'05 workshop that was held in conjunction with the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2005), August 21--24 in Chicago IL.

Published
2024
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43. gstsm: Generalized Spatial-Time Sequence Miner

Author

Castro, Antonio, primary, Souza, Cássio, additional, Rodrigues, Jorge, additional, Pacitti, Esther, additional, Porto, Fábio, additional, Masseglia, Florent, additional, Coutinho, Rafaelli, additional, and Ogasawara, Eduardo, additional

Published
2021
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44. ASAX : Segmentation adaptative basée sur la quantité d'information pour SAX

Author

Djebour, Lamia, Akbarinia, Reza, Masseglia, Florent, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]
Abstract

International audience; Les approches existantes pour le calcul de similitude entre séries temporelles sont au coeur de nombreuses tâches d'analyse de données. Étant donné les volumes de données considérés, ou simplement le besoin de les traiter rapidement, ces approches s'appuient souvent sur des représentations alternatives, plus courtes, qui résument les séries d'origine avec une perte d'information acceptable. Les comparaisons de séries temporelles qui se basent sur ces représentations sont alors approximatives, ce qui fait de la précision un enjeu majeur. Nous présentons et évaluons expérimentalement ASAX, une nouvelle approche pour la segmentation de séries temporelles avant qu'elles soient transformées en représentations symboliques. ASAX réduit de manière significative la perte d'information due aux fractionnements dans les différentes étapes du calcul de la représentation. Nous fournissons des garanties théoriques sur la borne inférieure des mesures de similitude entre séries temporelles, et nos expériences illustrent l'intérêt de notre méthode sur l'état de l'art, en particulier avec un gain de précision significatif.

Published
2021

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