Florent Masseglia

Oleksandra Levchenko, Boyan Kolev, Djamel-Edine Yagoubi, Reza Akbarinia, Florent Masseglia, et al.. BestNeighbor: Efficient Evaluation of kNN Queries on Large Time Series Databases. Knowledge and Information Systems (KAIS), Springer, In press, ⟨10.1007/s10115-020-01518-4⟩. ⟨lirmm-02973633v2⟩

Djamel-Edine Yagoubi, Reza Akbarinia, Florent Masseglia, Themis Palpanas. Massively Distributed Time Series Indexing and Querying. IEEE Transactions on Knowledge and Data Engineering, Institute of Electrical and Electronics Engineers, 2020, 32 (1), pp.108-120. ⟨10.1109/TKDE.2018.2880215⟩. ⟨lirmm-02197618⟩

Heraldo Borges, Murillo Dutra, Amin Bazaz, Rafaelli Coutinho, Fábio Perosi, et al.. Spatial-Time Motifs Discovery. Intelligent Data Analysis, IOS Press, 2020. ⟨lirmm-02984969⟩

Maxime Metz, Matthieu Lesnoff, Florent Abdelghafour, Reza Akbarinia, Florent Masseglia, et al.. A “big-data” algorithm for KNN-PLS. Chemometrics and Intelligent Laboratory Systems, Elsevier, 2020, 203, pp.104076. ⟨10.1016/j.chemolab.2020.104076⟩. ⟨hal-02899789⟩

Asma Belhadi, Youcef Djenouri, Kjetil Nørvåg, Heri Ramampiaro, Florent Masseglia, et al.. Space Time Series Clustering: Algorithms, Taxonomy, and Case Study on Urban Smart Cities. Engineering Applications of Artificial Intelligence, Elsevier, 2020, 95, pp.#103857. ⟨10.1016/j.engappai.2020.103857⟩. ⟨lirmm-03036868⟩

Corinne Atlan, Jean-Pierre Archambault, Olivier Banus, Frédéric Bardeau, Amélie Blandeau, et al.. Apprentissage de la pensée informatique : de la formation des enseignant·e·s à la formation de tou·te·s les citoyen·ne·s. Revue de l'EPI (Enseignement Public et Informatique), EPI, 2019. ⟨hal-02145478⟩

Djamel-Edine Yagoubi, Reza Akbarinia, Boyan Kolev, Oleksandra Levchenko, Florent Masseglia, et al.. ParCorr: efficient parallel methods to identify similar time series pairs across sliding windows. Data Mining and Knowledge Discovery, Springer, 2018, 32 (5), pp.1481-1507. ⟨10.1007/s10618-018-0580-z⟩. ⟨lirmm-01886794⟩

Saber Salah, Reza Akbarinia, Florent Masseglia. A Highly Scalable Parallel Algorithm for Maximally Informative k-Itemset Mining. Knowledge and Information Systems (KAIS), Springer, 2017, 50 (1), pp.1-26. ⟨10.1007/s10115-016-0931-2⟩. ⟨lirmm-01288571⟩

Saber Salah, Reza Akbarinia, Florent Masseglia. Data placement in massively distributed environments for fast parallel mining of frequent itemsets. Knowledge and Information Systems (KAIS), Springer, 2017, 53 (1), pp.207-237. ⟨10.1007/s10115-017-1041-5⟩. ⟨lirmm-01620383⟩

Maxime Amblard, Victoria Denys, Joanna Jongwane, Florent Masseglia. Où se trouve la science informatique ?. Interstices, INRIA, 2015. ⟨hal-01352666⟩

Chongsheng Zhang, Florent Masseglia, Yves Lechevallier. The anti-bouncing data stream model for web usage streams with intralinkings. Information Sciences, Elsevier, 2014, 278, pp.757-772. ⟨10.1016/j.ins.2014.03.089⟩. ⟨lirmm-01091868⟩

Wei Wang, Thomas Guyet, René Quiniou, Marie-Odile Cordier, Florent Masseglia, et al.. Autonomic Intrusion Detection: Adaptively Detecting Anomalies over Unlabeled Audit Data Streams in Computer Networks. Knowledge-Based Systems, Elsevier, 2014, 70, pp.103-117. ⟨10.1016/j.knosys.2014.06.018⟩. ⟨hal-01052810⟩

Florent Masseglia. Person of Interest : la surveillance des données individuelles pour anticiper les crimes. Interstices, INRIA, 2013. ⟨hal-01350299⟩

Alice Marascu, Florent Masseglia. Atypicity Detection in Data Streams: a Self-Adjusting Approach. Intelligent Data Analysis, IOS Press, 2011, 15 (1), pp.89-105. ⟨10.3233/IDA-2010-0457⟩. ⟨hal-00789034⟩

Bashar Saleh, Florent Masseglia. Discovering Frequent Behaviors: Time is an Essential Element of the Context. Knowledge and Information Systems (KAIS), Springer, 2011, 28 (2), pp.311-331. ⟨hal-00640213⟩

François Petitjean, Florent Masseglia, Pierre Gancarski, Germain Forestier. Discovering Significant Evolution Patterns from Satelllite Image Time Series. International Journal of Neural Systems, World Scientific Publishing, 2011, 21 (6), pp.15. ⟨lirmm-00639480⟩

Florent Masseglia. Idée reçue : Les premiers résultats de Google sont les meilleurs. Interstices, INRIA, 2010. ⟨hal-01350222⟩

Florent Masseglia, Joanna Jongwane. À propos de la fouille de données. Interstices, INRIA, 2010. ⟨hal-01350298⟩

Florent Masseglia, Pascal Poncelet, Maguelonne Teisseire. Efficient Mining of Sequential Patterns with Time Constraints: Reducing the Combinations. Expert Systems with Applications, Elsevier, 2009, 36 (2), pp.2677-2690. ⟨10.1016/j.eswa.2008.01.021⟩. ⟨lirmm-00272632⟩

Florent Masseglia, Pascal Poncelet, Maguelonne Teisseire, Alice Marascu. Web Usage Mining: Extracting Unexpected Periods from Web Logs. Data Mining and Knowledge Discovery, Springer, 2008, 16 (1), pp.039-065. ⟨10.1007/s10618-007-0080-z⟩. ⟨lirmm-00204872v2⟩

Alice Marascu, Florent Masseglia. Mining Sequential Patterns from Data Streams: a Centroid Approach. Journal of Intelligent Information Systems, Springer Verlag, 2006, 27 (3), pp.291-307. ⟨10.1007/s10844-006-9954-6⟩. ⟨inria-00461296⟩

Florent Masseglia, Maguelonne Teisseire, Pascal Poncelet. Extraction de motifs séquentiels. Problèmes et méthodes. Revue des Sciences et Technologies de l'Information - Série ISI : Ingénierie des Systèmes d'Information, Lavoisier, 2004, 9 (3/4), pp.183-210. ⟨10.3166/isi.9.3-4.183-210⟩. ⟨lirmm-00108563⟩

Florent Masseglia, Maguelonne Teisseire, Pascal Poncelet. HDM: A Client/Server/Engine Architecture for Real Time Web Usage Mining. Knowledge and Information Systems (KAIS), Springer, 2003, 5 (4), pp.439-465. ⟨10.1007/s10115-003-0097-6⟩. ⟨lirmm-00191952⟩

Florent Masseglia, Pascal Poncelet, Maguelonne Teisseire. Incremental Mining of Sequential Patterns in Large Databases. Data and Knowledge Engineering, Elsevier, 2003, 46 (1), pp.97-121. ⟨10.1016/S0169-023X(02)00209-4⟩. ⟨lirmm-00269547⟩

Khadidja Meguelati, Benedicte Fontez, Nadine Hilgert, Florent Masseglia, Isabelle Sanchez. Massively Distributed Clustering via Dirichlet Process Mixture. ECML PKDD 2020 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2020, Ghent (virtual), Belgium. ⟨lirmm-03036910⟩

Boyan Kolev, Reza Akbarinia, Ricardo Jimenez-Peris, Oleksandra Levchenko, Florent Masseglia, et al.. Parallel Streaming Implementation of Online Time Series Correlation Discovery on Sliding Windows with Regression Capabilities. CLOSER 2019 - 9th International Conference on Cloud Computing and Services Science, May 2019, Heraklion, Greece. pp.681-687. ⟨lirmm-02265729⟩

Oleksandra Levchenko, Boyan Kolev, Djamel-Edine Yagoubi, Dennis Shasha, Themis Palpanas, et al.. Distributed Algorithms to Find Similar Time Series. ECML-PKDD 2019 - European Conference on Machine Learning and Knowledge Discovery in Databases, Sep 2019, Wurtzbourg, Germany. ⟨lirmm-02265726⟩

Khadidja Meguelati, Bénédicte Fontez, Nadine Hilgert, Florent Masseglia. High Dimensional Data Clustering by means of Distributed Dirichlet Process Mixture Models. IEEE Big Data 2019 - IEEE International Conference on Big Data, Dec 2019, Los-Angeles, United States. ⟨lirmm-02364411⟩

Corinne Atlan, Jean-Pierre Archambault, Olivier Banus, Frédéric Bardeau, Amélie Blandeau, et al.. Apprentissage de la pensée informatique : de la formation des enseignant·e·s à la formation de tou·te·s les citoyen·ne·s. EIAH Wokshop 2019 - Apprentissage de la pensée informatique de la maternelle à l’Université : retours d’expériences et passage à l’échelle, Jun 2019, Paris, France. ⟨hal-02145480⟩

Khadidja Meguelati, Bénédicte Fontez, Nadine Hilgert, Florent Masseglia. Dirichlet Process Mixture Models made Scalable and Effective by means of Massive Distribution. SAC '19 - 34th ACM/SIGAPP symposium on applied computing, Apr 2019, Limassol, Cyprus. pp.502-509, ⟨10.1145/3297280.3297327⟩. ⟨hal-01999453⟩

Patrick Valduriez, Marta Mattoso, Reza Akbarinia, Heraldo Borges, José Camata, et al.. Scientific Data Analysis Using Data-Intensive Scalable Computing: the SciDISC Project. LADaS: Latin America Data Science Workshop, Aug 2018, Rio de Janeiro, Brazil. ⟨lirmm-01867804⟩

Mohamed Bouadjenek, Esther Pacitti, Maximilien Servajean, Florent Masseglia, Amr Abbadi. A Distributed Collaborative Filtering Algorithm Using Multiple Data Sources. DBKDA: Advances in Databases, Knowledge, and Data Applications, May 2018, Nice, France. ⟨hal-01911684⟩

Oleksandra Levchenko, Djamel-Edine Yagoubi, Reza Akbarinia, Florent Masseglia, Boyan Kolev, et al.. Spark-parSketch: A Massively Distributed Indexing of Time Series Datasets. CIKM: Conference on Information and Knowledge Management, Oct 2018, Turin, Italy. pp.1951-1954, ⟨10.1145/3269206.3269226⟩. ⟨lirmm-01886760⟩

Mehdi Zitouni, Reza Akbarinia, Sadok Ben Yahia, Florent Masseglia. Maximally Informative k-Itemset Mining from Massively Distributed Data Streams. SAC: Symposium on Applied Computing, Apr 2018, Pau, France. pp.502-509, ⟨10.1145/3167132.3167187⟩. ⟨hal-01711990⟩

Riccardo Campisano, Heraldo Borges, Fábio Porto, Fabio Perosi, Esther Pacitti, et al.. Discovering Tight Space-Time Sequences. DaWaK: Data Warehousing and Knowledge Discovery, Sep 2018, Regensburg, Germany. pp.247-257, ⟨10.1007/978-3-319-98539-8_19⟩. ⟨hal-01925965⟩

Djamel-Edine Yagoubi, Reza Akbarinia, Florent Masseglia, Themis Palpanas. DPiSAX: Massively Distributed Partitioned iSAX. ICDM: International Conference on Data Mining, Nov 2017, New Orleans, United States. pp.1135-1140, ⟨10.1109/ICDM.2017.151⟩. ⟨lirmm-01620125⟩

Djamel-Edine Yagoubi, Reza Akbarinia, Florent Masseglia, Dennis Shasha. RadiusSketch: Massively Distributed Indexing of Time Series. DSAA: Data Science and Advanced Analytics, Oct 2017, Tokyo, Japan. pp.1-10. ⟨lirmm-01620154⟩

Mehdi Zitouni, Reza Akbarinia, Sadok Ben Yahia, Florent Masseglia. Massively Distributed Environments and Closed Itemset Mining: The DCIM Approach. CAiSE: Advanced Information Systems Engineering, Jun 2017, Essen, Germany. pp.231-246, ⟨10.1007/978-3-319-59536-8_15⟩. ⟨lirmm-01620238⟩

Mehdi Zitouni, Reza Akbarinia, Sadok Ben Yahia, Florent Masseglia. Massively Distributed Environments and Closed Itemset Mining: The DCIM Approach. BDA: Gestion de Données — Principes, Technologies et Applications, Nov 2017, Nancy, France. pp.1-15. ⟨lirmm-01620354⟩

Saber Salah, Reza Akbarinia, Florent Masseglia. Mining Maximally Informative k-Itemsets in Massively Distributed Environments. BDA: Gestion de Données — Principes, Technologies et Applications, Nov 2016, Poitiers, France. ⟨lirmm-01411190⟩

Tristan Allard, Georges Hébrail, Florent Masseglia, Esther Pacitti. A New Privacy-Preserving Solution for Clustering Massively Distributed Personal Times-Series. ICDE: International Conference on Data Engineering, May 2016, Helsinki, Finland. pp.1370-1373, ⟨10.1109/ICDE.2016.7498347⟩. ⟨lirmm-01270268⟩

Saber Salah, Reza Akbarinia, Florent Masseglia. Data Partitioning for Fast Mining of Frequent Itemsets in Massively Distributed Environments. DEXA: Database and Expert Systems Applications, Sep 2015, Valencia, Spain. pp.303-318, ⟨10.1007/978-3-319-22849-5_21⟩. ⟨lirmm-01169603⟩

Saber Salah, Reza Akbarinia, Florent Masseglia. Optimizing the Data-Process Relationship for Fast Mining of Frequent Itemsets in MapReduce. MLDM: Machine Learning and Data Mining, Jul 2015, Hamburg, Germany. pp.217-231, ⟨10.1007/978-3-319-21024-7_15⟩. ⟨lirmm-01171555⟩

Reza Akbarinia, Florent Masseglia. Aggregation-Aware Compression of Probabilistic Streaming Time Series. MLDM: Machine Learning and Data Mining, Jul 2015, Hamburg, Germany. pp.232-247, ⟨10.1007/978-3-319-21024-7_16⟩. ⟨lirmm-01162366⟩

Marie Duflot, Martin Quinson, Florent Masseglia, Didier Roy, Julien Vaubourg, et al.. When sharing computer science with everyone also helps avoiding digital prejudices: Escape computer dirty magic: learn Scratch !. SCRATCH, Aug 2015, Amsterdam, Netherlands. ⟨hal-01154767⟩

Tristan Allard, Georges Hébrail, Florent Masseglia, Esther Pacitti. Chiaroscuro: Transparency and Privacy for Massive Personal Time-Series Clustering. SIGMOD: International Conference on Management of Data, May 2015, Melbourne, Australia. pp.779-794, ⟨10.1145/2723372.2749453⟩. ⟨hal-01136686⟩

Saber Salah, Reza Akbarinia, Florent Masseglia. Fast Parallel Mining of Maximally Informative k-Itemsets in Big Data. ICDM: International Conference on Data Mining, Aug 2015, Atlantic city, United States. pp.359-368, ⟨10.1109/ICDM.2015.86⟩. ⟨lirmm-01187275⟩

Mehdi Zitouni, Reza Akbarinia, Sadok Ben Yahia, Florent Masseglia. A Prime Number Based Approach for Closed Frequent Itemset Mining in Big Data. DEXA: Database and Expert Systems Applications, Sep 2015, Valencia, Spain. pp.509-516, ⟨10.1007/978-3-319-22849-5_35⟩. ⟨lirmm-01169606⟩

Reza Akbarinia, Florent Masseglia. Compression de flux de données probabilistes attentive à l'agrégation. BDA: Gestion de Données — Principes, Technologies et Applications, Oct 2014, Autrans, France. ⟨lirmm-01091870⟩

Chongsheng Zhang, Yuan Hao, Mirjana Mazuran, Carlo Zaniolo, Hamid Mousavi, et al.. Mining frequent itemsets over tuple-evolving data streams. SAC: Symposium on Applied Computing, Mar 2013, Coimbra, Portugal. pp.267-274, ⟨10.1145/2480362.2480419⟩. ⟨lirmm-00830923⟩

Reza Akbarinia, Florent Masseglia. Fast and Exact Mining of Probabilistic Data Streams. ECML-PKDD: Machine Learning and Knowledge Discovery in Databases, Sep 2013, Prague, Czech Republic. pp.493-508, ⟨10.1007/978-3-642-40988-2_32⟩. ⟨lirmm-00838618⟩

Enikö Székely, Pascal Poncelet, Florent Masseglia, Maguelonne Teisseire, Renaud Cezar. A Density-Based Backward Approach to Isolate Rare Events in Large-Scale Applications. DS: Discovery Science, Oct 2013, Singapore, Singapore. pp.249-264, ⟨10.1007/978-3-642-40897-7_17⟩. ⟨lirmm-00907893⟩

Chongsheng Zhang, Florent Masseglia, Xiangliang Zhang. Modeling and Clustering Users with Evolving Profiles in Usage Streams. TIME'2012: 19th International Symposium on Temporal Representation and Reasoning, Sep 2012, United Kingdom. pp.133-140. ⟨lirmm-00753791⟩

Chongsheng Zhang, Florent Masseglia, Xiangliang Zhang. Discovering Highly Informative Feature Set Over High Dimensions. ICTAI: International Conference on Tools with Artificial Intelligence, Nov 2012, Athens, Greece. pp.1059-1064, ⟨10.1109/ICTAI.2012.149⟩. ⟨lirmm-00753807⟩

Reza Akbarinia, Florent Masseglia. FMU: Fast Mining of Probabilistic Frequent Itemsets in Uncertain Data Streams. BDA: Bases de Données Avancées, 2012, Clermont-Ferrand, France. ⟨lirmm-00748605⟩

François Petitjean, Florent Masseglia, Pierre Gancarski. Découverte de motifs d'évolution significatifs dans les séries temporelles d'images satellites. EGC: Extraction et Gestion des Connaissances, Jan 2011, Brest, France. pp.665-676. ⟨hal-00640214⟩

Alice Marascu, Florent Masseglia, Yves Lechevallier. A Fast Approximation Strategy for Summarizing a Set of Streaming Time Series. ACM Symposium on Applied Computing, Mar 2010, Sierre, Switzerland. ⟨inria-00461781⟩

Alice Marascu, Florent Masseglia, Yves Lechevallier. REGLO: une nouvelle stratégie pour résumer un flux de séries temporelles. Extraction et Gestion des Connaissances, Jan 2010, Hammamet, Tunisie. ⟨inria-00461834⟩

Chongsheng Zhang, Florent Masseglia. Extraction d'itemsets distinctifs dans les flux de données. Extraction et gestion des connaissances, Jan 2010, France. pp.187-198. ⟨hal-00504877⟩

Chongsheng Zhang, Florent Masseglia, Yves Lechevallier. ABS: The Anti Bouncing Model for Usage Data Streams. ICDM'10 : The 10th IEEE International Conference on Data Mining, Dec 2010, Sydney, Australia. pp.1169-1174, ⟨10.1109/ICDM.2010.91⟩. ⟨lirmm-00653732⟩

Alice Marascu, Florent Masseglia. A Multi-Resolution Approach for Atypical Behaviour Mining. Pacific-Asia Conference on Knowledge Discovery and Data Mining, Apr 2009, Bangkok, Thailand. pp.899-906, ⟨10.1007/978-3-642-01307-2⟩. ⟨inria-00461831⟩

Alice Marascu, Florent Masseglia. Parameterless Outlier Detection in Data Streams. ACM symposium on Applied Computing, Mar 2009, Honolulu, United States. pp.1491-1495. ⟨inria-00461827⟩

Alice Marascu, Florent Masseglia. Détection d'enregistrements atypiques dans un flot de données: une approche multi-résolution. Extraction et Gestion des Connaissances, Jan 2009, Strasbourg, France. ⟨inria-00461838⟩

Nischal Verma, François Trousset, Pascal Poncelet, Florent Masseglia. Détection d'intrusions dans un environnement collaboratif sécurisé. EGC: Extraction et Gestion des Connaissances, Jan 2009, Strasbourg, France. pp.301-312. ⟨lirmm-00345566⟩

Goverdhan Singh, Florent Masseglia, Céline Fiot, Alice Marascu, Pascal Poncelet. Collaborative Outlier Mining for Intrusion Detection. EGC: Extraction et Gestion des Connaissances, Jan 2009, Strasbourg, France. pp.313-323. ⟨lirmm-00345574⟩

Goverdhan Singh, Florent Masseglia, Céline Fiot, Alice Marascu, Pascal Poncelet. Data Mining for Intrusion Detection: from Outliers to True Intrusions. The 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-09), Apr 2009, Bankok, Thailand. pp.891-898, ⟨10.1007/978-3-642-01307-2_93⟩. ⟨inria-00359206v2⟩

François Trousset, Pascal Poncelet, Florent Masseglia. SAX: A Privacy Preserving General Purpose Method applied to Detection of Intrusions. ACM First International Workshop on Privacy and Anonymity for Very Large Datasets, join with CIKM 09, Nov 2009, Hong Kong, China. pp.17-24. ⟨lirmm-00430646⟩

Wei Wang, Thomas Guyet, René Quiniou, Marie-Odile Cordier, Florent Masseglia, et al.. Online and Adaptive anomaly Detection: detecting intrusions in unlabelled audit data streams. EGC 2009, 2009, Strasbourg, France. ⟨inria-00460723⟩

Wei Wang, Florent Masseglia, Thomas Guyet, René Quiniou, Marie-Odile Cordier. A General Framework for Adaptive and Online Detection of Web attacks. 18th International World Wide Web Conference - WWW 2009, Apr 2009, Madrid, Spain. ⟨inria-00461391⟩

Bashar Saleh, Florent Masseglia. Time Aware Mining of Itemsets. TIME, Jun 2008, Montreal, Canada. pp.93-97, ⟨10.1109/TIME.2008.12⟩. ⟨inria-00359182⟩

Céline Fiot, Florent Masseglia, Anne Laurent, Maguelonne Teisseire. Des séquences aux tendances. INFORSID: INFormatique des ORganisations et Systèmes d’Information et de Décision, May 2008, Fontainebleau, France. ⟨lirmm-00273920⟩

Céline Fiot, Florent Masseglia, Anne Laurent, Maguelonne Teisseire. Gradual Trends in Fuzzy Sequential Patterns. IPMU: Information Processing and Management of Uncertainty in Knowledge-Based Systems, Jun 2008, Malaga, Spain. pp.456-463. ⟨lirmm-00273910⟩

Florent Masseglia, Maguelonne Teisseire, Pascal Poncelet. Real Time Web Usage Mining with a Distributed Navigation Analysis. RIDE'02: International Workshop on Research Issues on Data Engineering, San Jose, USA, pp.6. ⟨lirmm-00268631⟩

Céline Fiot, Florent Masseglia, Anne Laurent, Maguelonne Teisseire. TED and EVA : Expressing Temporal Tendencies Among Quantitative Variables Using Fuzzy Sequential Patterns. WCCI4: World Congress on Computational Intelligence, Jun 2008, Hong Kong, China. pp.1861-1868, ⟨10.1109/FUZZY.2008.4630623⟩. ⟨lirmm-00273907⟩

Alice Marascu, Florent Masseglia. Limites d'une approche incrémentale pour la segmentation de séquences dans les flux. Fouille de données complexes dans un processus d'extraction de connaissances, Jan 2007, Namur, Belgique. ⟨inria-00461878⟩

Alice Marascu, Florent Masseglia. Classification de flots de séquences basée sur une approche centroïde. INFORSID, Informatique des organisations et systèmes d'information et de décision, Jun 2006, Hammamet, Tunisie. ⟨inria-00461839⟩

Alice Marascu, Florent Masseglia. Extraction de motifs séquentiels dans les flots de données d'usage du Web. Extraction et Gestion des Connaissances, Jan 2006, Lille, France. pp.627-638. ⟨inria-00461841⟩

Alice Marascu, Florent Masseglia. Classification de flots de séquences basée sur une approche centroïde. Fouille de données complexes dans un processus d'extraction de connaissances, Jan 2006, Lille, France. pp.131-139. ⟨inria-00461880⟩

Florent Masseglia, Pascal Poncelet, Maguelonne Teisseire. Peer-to-Peer Usage Analysis: a Distributed Mining Approach. AINA: Advanced Information Networking and Applications, 2006, Vienna, Austria. ⟨hal-00106798⟩

Florent Masseglia, Pascal Poncelet, Maguelonne Teisseire, Alice Marascu. Web Usage Mining : extraction de périodes denses à partir des logs. EGC: Extraction et Gestion des Connaissances, Jan 2006, Lille, France. pp.403-408. ⟨inria-00461840⟩

Alice Marascu, Florent Masseglia. Mining Sequential Patterns from Temporal Streaming Data. First ECML/PKDD Workshop on Mining Spatio-Temporal Data (MSTD'05), held in conjunction with the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'05), Nov 2005, Porto, Portugal. ⟨inria-00461843⟩

Alice Marascu, Florent Masseglia. Mining Data Streams for Frequent Sequences Extraction. IEEE first Workshop on Mining Complex Data (MCD'05). Held in conjunction with ICDM'05, Nov 2005, Houston, United States. ⟨inria-00461876⟩

Florent Masseglia, Pascal Poncelet, Maguelonne Teisseire, Alice Marascu. Web Usage Mining: Extracting Unexpected Periods from Web Logs. IEEE 2nd Workshop on Temporal Data Mining (TDM'05). Held in conjunction with ICDM'05, Nov 2005, Houston, United States. ⟨inria-00461877⟩

Florent Masseglia, Doru Tanasa, Brigitte Trousse. Web Usage Mining: Sequential Pattern Extraction with a Very Low Support. Advanced Web Technologies and Applications: 6th Asia-Pacific Web Conference, APWeb 2004, Apr 2004, Hangzhou, China. pp.513--522, ⟨10.1007/978-3-540-24655-8_56⟩. ⟨hal-00950768⟩

Florent Masseglia, Maguelonne Teisseire, Pascal Poncelet. Pre-Processing Time Constraints for Efficiently Mining Generalized Sequential Patterns. TIME'04: 11th International Symposium on Temporal Representation and Reasoning, Jul 2004, Tatihou, Basse-Normandie (France), pp.87-95. ⟨lirmm-00108888⟩

Florent Masseglia, Maguelonne Teisseire, Pascal Poncelet. HDM, un Module de Fouille de Données Distribué et Temps Réel. EGC: Extraction et Gestion des Connaissances, Jan 2002, Montpellier, France. pp.393-398. ⟨lirmm-00268518⟩

Florent Masseglia, Pascal Poncelet, Maguelonne Teisseire. Web Usage Mining: How to Efficiently Manage New transactions and New Customers. 4th European Conference on Principles of Data Mining and Knowledge Discovery, 2000, Lyon, France. ⟨hal-00008926⟩

Boyan Kolev, Oleksandra Levchenko, Florent Masseglia, Reza Akbarinia, Esther Pacitti, et al.. Highly Scalable Real-Time Analytics with CloudDBAppliance. XLDB: Extremely Large Databases Conference, Oct 2017, Clermont-Ferrand, France. 10th Extremely Large Databases Conference, 2017. ⟨lirmm-01632355⟩

Florent Masseglia, Pascal Poncelet, Maguelonne Teisseire. Successes and New Directions in Data Mining. IDEA Group, pp.1-369, 2007, 13 978-1599046457. ⟨lirmm-00365422⟩

Pascal Poncelet, Florent Masseglia, Maguelonne Teisseire. Data Mining Patterns: New Methods and Applications. IDEA Group, pp.307, 2007, 13 978-1599041629. ⟨lirmm-00365419⟩

Verma Nischal, François Trousset, Pascal Poncelet, Florent Masseglia. Intrusion Detections in Collaborative Organizations by Preserving Privacy. Fabrice Guillet and Gilbert Ritschard and Djamel Abdelkader Zighed and Henri Briand. Advances in Knowledge Discovery and Management, 292, Springer, pp.235-247, 2010, Studies in Computational Intelligence, 978-3-642-00579-4. ⟨10.1007/978-3-642-00580-0_14⟩. ⟨lirmm-00430642⟩

Goverdhan Singh, Florent Masseglia, Céline Fiot, Alice Marascu, Pascal Poncelet. Mining Common Outliers for Intrusion Detection. Fabrice Guillet; Gilbert Ritschard; Djamel Abdelkader Zighed; Henri Briand. Advances in Knowledge Discovery and Management, 292, Springer, pp.217-234, 2010, Studies in Computational Intelligence, 978-3-642-00579-4. ⟨10.1007/978-3-642-00580-0_13⟩. ⟨lirmm-00798705⟩

Brigitte Trousse, Marie-Aude Aufaure, Bénédicte Le Grand, Yves Lechevallier, Florent Masseglia. Web Usage Mining for Ontology Management. Data Mining with Ontologies: Implementation, Findings and Framework, Idea Group Publishing, pp.37-64, 2007. ⟨hal-00256574⟩

Florent Masseglia, Maguelonne Teisseire, Pascal Poncelet. Sequential Pattern Mining. John Wang. Encyclopedia of Data Warehousing and Mining, Idea Group Reference, pp.1028-1032, 2005, 1-59140-557-2. ⟨lirmm-00106576⟩

Tristan Allard, Georges Hébrail, Florent Masseglia, Esther Pacitti. Procédé et installation de comparaison de consommation d'effluents sans divulgation de données de consommations mesurées . France, N° de brevet: EP 2930471 A1. 2015. ⟨hal-01274207⟩

Renaud Cezar, Dino Ienco, André Mas, Florent Masseglia, Pascal Poncelet, et al.. Process for identifying rare events. France, Patent n° : WO/2014/118343. 2014. ⟨hal-02608923⟩

Renaud Cezar, Dino Ienco, André Mas, Florent Masseglia, Pascal Poncelet, et al.. Process for identifying rare events. United States, Patent n° : US 20150363551 A1, WO 2014118343 A2, PCT/EP2014/051963. 2014. ⟨lirmm-00913008⟩

Thierry Viéville, Sylvie Boldo, Florent Masseglia, Pierre Bernhard. « Structures : organisation, complexité, dynamique » des mot-clés au sens inattendu. 2015. ⟨hal-01238442⟩

Antoine Rousseau, Aurélie Darnaud, Brice Goglin, Céline Acharian, Christine Leininger, et al.. Médiation Scientifique : une facette de nos métiers de la recherche. [Interne] none. 2013, pp.34. ⟨hal-00804915⟩

Enikö Székely, Pascal Poncelet, Florent Masseglia, Maguelonne Teisseire, Renaud Cezar. Isolating rare events in large-scale applications using a backward approach. [Research Report] RR-13003, LIRMM. 2013. ⟨lirmm-00798074⟩

Florent Masseglia. Extraction de connaissances : réunir volumes de données et motifs significatifs. Base de données [cs.DB]. Université Nice Sophia Antipolis, 2009. ⟨tel-00788309⟩

Florent Masseglia, Julien Diener. LogMagnet. 2019. ⟨hal-02098365⟩

Oleksandra Levchenko, Djamel-Edine Yagoubi, Reza Akbarinia, Florent Masseglia, Boyan Kolev, et al.. Imitates. 2019. ⟨hal-02095640⟩