Author: "Sandra Bringay" / Topic: 02 engineering and technology - Searchworks@Jio Institute Digital Library Search Results

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33 results on '"Sandra Bringay"'

1. Divide to Better Classify

Author: Yves Mercadier, Jérôme Azé, and Sandra Bringay
Subjects: 0209 industrial biotechnology, Information retrieval, Computer science, Medical record, Document classification, Context (computing), Medical information, 02 engineering and technology, computer.software_genre, Task (project management), 020901 industrial engineering & automation, 0202 electrical engineering, electronic engineering, information engineering, Training phase, 020201 artificial intelligence & image processing, Social media, computer
Abstract: Medical information is present in various text-based resources such as electronic medical records, biomedical literature, social media, etc. Using all these sources to extract useful information is a real challenge. In this context, the single-label classification of texts is an important task. Recently, in-depth classifiers have shown their ability to achieve very good results. However, their results generally depend on the amount of data used during the training phase. In this article, we propose a new approach to increase text data. We have compared this approach for 5 real data sets with the main approaches in the literature and our proposal outperforms in all configurations.
Published: 2020
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2. LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification

Author: Maximilien Servajean, Waleed Ragheb, Sandra Bringay, Jérôme Azé, ADVanced Analytics for data SciencE (ADVANSE), 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: Focus (computing), Word embedding, Computer science, business.industry, media_common.quotation_subject, Emotion detection, 02 engineering and technology, computer.software_genre, SemEval, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Task (project management), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Conversation, [INFO.EIAH]Computer Science [cs]/Technology for Human Learning, Artificial intelligence, Transfer of learning, business, computer, Natural language processing, media_common
Abstract: This paper addresses the problem of modeling textual conversations and detecting emotions. Our proposed model makes use of 1) deep transfer learning rather than the classical shallow methods of word embedding; 2) self-attention mechanisms to focus on the most important parts of the texts and 3) turn-based conversational modeling for classifying the emotions. The approach does not rely on any hand-crafted features or lexicons. Our model was evaluated on the data provided by the SemEval-2019 shared task on contextual emotion detection in text. The model shows very competitive results.
Published: 2019

3. Expertise in French health forums

Author: Jérôme Azé, Pascal Poncelet, Amine Abdaoui, Natalia Grabar, and Sandra Bringay
Subjects: Internet, 020205 medical informatics, Computer science, Health Informatics, 02 engineering and technology, Task (project management), Random forest, World Wide Web, 03 medical and health sciences, 0302 clinical medicine, Text categorization, Work (electrical), Order (business), 0202 electrical engineering, electronic engineering, information engineering, Humans, Interpersonal Relations, 030212 general & internal medicine, Clinical Competence, France, Social Media
Abstract: More and more health websites hire medical experts (physicians, medical students, experienced volunteers, etc.) and indicate explicitly their medical role in order to notify that they provide high-quality answers. However, medical experts may participate in forum discussions even when their role is not officially indicated. Detecting posts written by medical experts facilitates the quick access to posts that have more chances of being correct and informative. The main objective of this work is to learn classification models that can be used to detect posts written by medical experts in any health forum discussions. Two French health forums have been used to discover the best features and methods for this text categorization task. The obtained results confirm that models learned on appropriate websites may be used efficiently on other websites (more than 98% of F1-measure has been obtained using a Random Forest classifier). A study of misclassified posts highlights the participation of medical experts in forum discussions even if their role is not explicitly indicated.
Published: 2019

4. Language Modeling in Temporal Mood Variation Models for Early Risk Detection on the Internet

Author: Jérôme Azé, Waleed Ragheb, Sandra Bringay, and Maximilien Servajean
Subjects: Computer science, business.industry, 02 engineering and technology, 010501 environmental sciences, computer.software_genre, 01 natural sciences, Clef, Variation (linguistics), Mood, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, The Internet, Word2vec, Risk detection, Language model, Artificial intelligence, Architecture, business, computer, Natural language processing, 0105 earth and related environmental sciences
Abstract: Early risk detection can be useful in different areas, particularly those related to health and safety. Two tasks are proposed at CLEF eRisk-2018 for predicting mental disorder using users posts on Reddit. Depression and anorexia disorders must be detected as early as possible. In this paper, we extend the participation of LIRMM (Laboratoire d’Informatique, de Robotique et de Microelectronique de Montpellier) in both tasks. The proposed model addresses this problem by modeling the temporal mood variation detected from user posts. The proposed architectures use only textual information without any hand-crafted features or dictionaries. The basic architecture uses two learning phases through exploration of state-of-the-art text vectorizations and deep language models. The proposed models perform comparably to other contributions while further experiments shows that attentive based deep language models outperformed the shallow learning text vectorizations.
Published: 2019
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5. Spatio-sequential patterns mining: Beyond the boundaries

Author: Frédéric Flouvat, Nazha Selmaoui-Folcher, Hugo Alatrista-Salas, Sandra Bringay, Maguelonne Teisseire, Pontificia Universidad Católica del Perú (PUCP), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Département de Mathématiques et Informatique Appliquées (MIAP), Université Paul-Valéry - Montpellier 3 (UPVM), ADVanced Analytics for data SciencE (ADVANSE), 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), Pôle Pluridisciplinaire de la Matière et de l'Environnement (PPME), Université de la Nouvelle-Calédonie (UNC), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UM3), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Pôle Pluridisciplinaire de la Matière et de l'Environnement (PPME - EA 3325), and Université de la Nouvelle Calédonie (UNC)
Subjects: Measure (data warehouse), Computer science, Event (computing), 02 engineering and technology, Type (model theory), computer.software_genre, Synthetic data, Theoretical Computer Science, Artificial Intelligence, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, A priori and a posteriori, [INFO]Computer Science [cs], 020201 artificial intelligence & image processing, Mobile technology, Computer Vision and Pattern Recognition, Data mining, Sequential patterns, Focus (optics), health risk management, Spatial analysis, computer, spatiotemporal data mining
Abstract: International audience; Data mining methods extract knowledge from huge amounts of data. Recently with the explosion of mobile technologies, a new type of data appeared. The resulting databases can be described as spatiotemporal data in which spatial information (e.g., the location of an event) and temporal information (e.g., the date of the event) are included. In this article, we focus on spatiotemporal patterns extraction from this kind of databases. These patterns can be considered as sequences representing changes of events localized in areas and its near surrounding over time. Two algorithms are proposed to tackle this problem: the first one uses \emph{a priori} strategy and the second one is based on pattern-growth approach. We have applied our generic method on two different real datasets related to: 1) pollution of rivers in France; and 2) monitoring of dengue epidemics in New Caledonia. Additionally, experiments on synthetic data have been conducted to measure the performance of the proposed algorithms.
Published: 2016
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6. Reconciliation of patient/doctor vocabulary in a structured resource

Author: Mike Donald Tapi Nzali, Jérôme Azé, Sandra Bringay, Thomas Optiz, Caroline Mollevi, Christian Lavergne, Université Paul-Valéry - Montpellier 3 ( UM3 ), ADVanced Analytics for data SciencE ( ADVANSE ), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier ( LIRMM ), Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire d'Informatique, de Modélisation et d'optimisation des Systèmes ( LIMOS ), Centre National de la Recherche Scientifique ( CNRS ) -Sigma CLERMONT ( Sigma CLERMONT ) -Université d'Auvergne - Clermont-Ferrand I ( UdA ) -Université Blaise Pascal - Clermont-Ferrand 2 ( UBP ), Institut de Mathématiques et de Modélisation de Montpellier ( I3M ), Université Montpellier 2 - Sciences et Techniques ( UM2 ) -Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de recherche en cancérologie de Montpellier ( IRCM ), Université Montpellier 1 ( UM1 ) -CRLCC Val d'Aurelle - Paul Lamarque-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Université de Montpellier ( UM ), ADVanced Analytics for data SciencE (ADVANSE), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Mathématiques et de Modélisation de Montpellier (I3M), Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche en Cancérologie de Montpellier (IRCM - U1194 Inserm - UM), CRLCC Val d'Aurelle - Paul Lamarque-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Institut National de la Recherche Agronomique (INRA), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM), ANR SIFR (Semantic Indexing of French Biomedical Data Resources), and French Public Health Research Institute under the 2012 call for projects as part of the 2009–2013 Cancer Plan
Subjects: Vocabulary, [ INFO ] Computer Science [cs], 020205 medical informatics, Text mining, Information extraction, Computer science, media_common.quotation_subject, Breast Neoplasms, Health Informatics, 02 engineering and technology, Ontology (information science), computer.software_genre, Task (project management), World Wide Web, Social media, 03 medical and health sciences, 0302 clinical medicine, Resource (project management), 0202 electrical engineering, electronic engineering, information engineering, Data Mining, Humans, [INFO]Computer Science [cs], 030212 general & internal medicine, ComputingMilieux_MISCELLANEOUS, media_common, Physician-Patient Relations, Consumer Health Information, business.industry, Ontology, 3. Good health, Vocabulary, Controlled, France, business, Consumer health vocabulary, computer, Algorithms
Abstract: International audience; Today, social media is increasingly used by patients to openly discuss their health. Mining automatically such data is a challenging task because of the non-structured nature of the text and the use of many abbreviations and the slang terms. Our goal is to use Patient Authored Text to build a French Consumer Health Vocabulary on breast cancer field, by collecting various kinds of non-experts' expressions that are related to their diseases and then compare them to biomedical terms used by health care professionals. We combine several methods of the literature based on linguistic and statistical approaches to extract candidate terms used by non-experts and to link them to expert terms. We use messages extracted from the forum on 'cancerdusein.org' and a vocabulary dedicated to breast cancer elaborated by the Institut National Du Cancer. We have built an efficient vocabulary composed of 192 validated relationships and formalized in Simple Knowledge Organization System ontology.
Published: 2018
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7. FEEL: a French Expanded Emotion Lexicon

Author: Pascal Poncelet, Jérôme Azé, Sandra Bringay, Amine Abdaoui, ADVanced Analytics for data SciencE (ADVANSE), 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), Département de Mathématiques et Informatique Appliquées (MIAP), and Université Paul-Valéry - Montpellier 3 (UPVM)
Subjects: Linguistics and Language, Computer science, Polarity (physics), Synonym, media_common.quotation_subject, Emotion classification, 02 engineering and technology, Library and Information Sciences, Anger, computer.software_genre, Lexicon, Language and Linguistics, Education, Opinion mining, Sentiment analysis, Semi-automatic translation, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], Association (psychology), media_common, business.industry, Polarity detection, Linguistics, Sadness, Sentiment lexicon, Surprise, 020201 artificial intelligence & image processing, Artificial intelligence, Computational linguistics, business, computer, Natural language processing
Abstract: International audience; Sentiment analysis allows the semantic evaluation of pieces of text according to the expressed sentiments and opinions. While considerable attention has been given to the polarity (positive, negative) of English words, only few studies were interested in the conveyed emotions (joy, anger, surprise, sadness, etc.) especially in other languages. In this paper, we present the elaboration and the evaluation of a new French lexicon considering both polarity and emotion. The elaboration method is based on the semi-automatic translation and expansion to synonyms of the English NRC Word Emotion Association Lexicon (NRC-EmoLex). First, online translators have been automatically queried in order to create a first version of our new French Expanded Emotion Lexicon (FEEL). Then, a human professional translator manually validated the automatically obtained entries and the associated emotions. She agreed with more than 94 % of the pre-validated entries (those found by a majority of translators) and less than 18 % of the remaining entries (those found by very few translators). This result highlights that online tools can be used to get high quality resources with low cost. Annotating a subset of terms by three different annotators shows that the associated sentiments and emotions are consistent. Finally, extensive experiments have been conducted to compare the final version of FEEL with other existing French lexicons. Various French benchmarks for polarity and emotion classifications have been used in these evaluations. Experiments have shown that FEEL obtains competitive results for polarity, and significantly better results for basic emotions.
Published: 2017
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8. Raising Awareness About Cervical Cancer Using Twitter: Content Analysis of the 2015 #SmearForSmear Campaign

Author: Sandra Bringay, Jérôme Azé, Gregoire Mercier, Bilel Moulahi, Philippe Lenoir, François Carbonnel, Université de Montpellier (UM), ADVanced Analytics for data SciencE (ADVANSE), 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), Université Paul-Valéry - Montpellier 3 (UPVM), Dynamique des capacités humaines et des conduites de santé (EPSYLON), Université de Montpellier (UM)-Université Paul-Valéry - Montpellier 3 (UPVM)-Université Montpellier 1 (UM1), and Université Montpellier 1 (UM1)-Université Paul-Valéry - Montpellier 3 (UPVM)-Université de Montpellier (UM)
Subjects: medicine.medical_specialty, 020205 medical informatics, health promotion, social media, Twitter, Uterine Cervical Neoplasms, Health Informatics, 02 engineering and technology, History, 21st Century, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], World Wide Web, 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, medicine, Humans, Social media, Cervical cancer, Original Paper, Marketing buzz, business.industry, Public health, medicine.disease, Papanicolaou test, Social marketing, 3. Good health, early detection of cancer, Health promotion, 030220 oncology & carcinogenesis, Family medicine, Female, Thematic analysis, Selfie, business
Abstract: Background: Cervical cancer is the second most common cancer among women under 45 years of age. To deal with the decrease of smear test coverage in the United Kingdom, a Twitter campaign called #SmearForSmear has been launched in 2015 for the European Cervical Cancer Prevention Week. Its aim was to encourage women to take a selfie showing their lipstick going over the edge and post it on Twitter with a raising awareness message promoting cervical cancer screening. The estimated audience was 500 million people. Other public health campaigns have been launched on social media such as Movember to encourage participation and self-engagement. Their result was unsatisfactory as their aim had been diluted to become mainly a social buzz. Objective: The objectives of this study were to identify the tweets delivering a raising awareness message promoting cervical cancer screening (sensitizing tweets) and to understand the characteristics of Twitter users posting about this campaign. Methods: We conducted a 3-step content analysis of the English tweets tagged #SmearForSmear posted on Twitter for the 2015 European Cervical Cancer Prevention Week. Data were collected using the Twitter application programming interface. Their extraction was based on an analysis grid generated by 2 independent researchers using a thematic analysis, validated by a strong Cohen kappa coefficient. A total of 7 themes were coded for sensitizing tweets and 14 for Twitter users’ status. Verbatims were thematically and then statistically analyzed. Results: A total of 3019 tweets were collected and 1881 were analyzed. Moreover, 69.96% of tweets had been posted by people living in the United Kingdom. A total of 57.36% of users were women, and sex was unknown in 35.99% of cases. In addition, 54.44% of the users had posted at least one selfie with smeared lipstick. Furthermore, 32.32% of tweets were sensitizing. Independent factors associated with posting sensitizing tweets were women who experienced an abnormal smear test (OR [odds ratio] 13.456, 95% CI 3.101-58.378, P
Published: 2017
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9. What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer

Author: Mike Donald Tapi Nzali, Sandra Bringay, Christian Lavergne, Caroline Mollevi, Thomas Opitz, ADVanced Analytics for data SciencE (ADVANSE), 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), Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM), Université Paul-Valéry - Montpellier 3 (UPVM), UNICANCER - Institut régional du Cancer Montpellier Val d'Aurelle (ICM), CRLCC Val d'Aurelle - Paul Lamarque, Biostatistique et Processus Spatiaux (BioSP), Institut National de la Recherche Agronomique (INRA), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UM3), UNICANCER - Institut régional du Cancer [Montpellier] (ICM), and Biostatistique et Processus Spatiaux (BIOSP)
Subjects: Original Paper, 020205 medical informatics, social media, 05 social sciences, 050109 social psychology, 02 engineering and technology, text mining, unsupervised learning, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], 3. Good health, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, breast cancer, 0202 electrical engineering, electronic engineering, information engineering, 0501 psychology and cognitive sciences, [SDV.IB]Life Sciences [q-bio]/Bioengineering
Abstract: Article number: e23; International audience; Background: Social media dedicated to health are increasingly used by patients and health professionals. They are rich textual resources with content generated through free exchange between patients. We are proposing a method to tackle the problem of retrieving clinically relevant information from such social media in order to analyze the quality of life of patients with breast cancer.Objective: Our aim was to detect the different topics discussed by patients on social media and to relate them to functional and symptomatic dimensions assessed in the internationally standardized self-administered questionnaires used in cancer clinical trials (European Organization for Research and Treatment of Cancer [EORTC] Quality of Life Questionnaire Core 30 [QLQ-C30] and breast cancer module [QLQ-BR23]).Methods: First, we applied a classic text mining technique, latent Dirichlet allocation (LDA), to detect the different topics discussed on social media dealing with breast cancer. We applied the LDA model to 2 datasets composed of messages extracted from public Facebook groups and from a public health forum (cancerdusein.org, a French breast cancer forum) with relevant preprocessing. Second, we applied a customized Jaccard coefficient to automatically compute similarity distance between the topics detected with LDA and the questions in the self-administered questionnaires used to study quality of life.Results: Among the 23 topics present in the self-administered questionnaires, 22 matched with the topics discussed by patients on social media. Interestingly, these topics corresponded to 95% (22/23) of the forum and 86% (20/23) of the Facebook group topics. These figures underline that topics related to quality of life are an important concern for patients. However, 5 social media topics had no corresponding topic in the questionnaires, which do not cover all of the patients’ concerns. Of these 5 topics, 2 could potentially be used in the questionnaires, and these 2 topics corresponded to a total of 3.10% (523/16,868) of topics in the cancerdusein.org corpus and 4.30% (3014/70,092) of the Facebook corpus.Conclusions: We found a good correspondence between detected topics on social media and topics covered by the self-administered questionnaires, which substantiates the sound construction of such questionnaires. We detected new emerging topics from social media that can be used to complete current self-administered questionnaires. Moreover, we confirmed that social media mining is an important source of information for complementary analysis of quality of life.
Published: 2017
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10. DARE to Care: A Context-Aware Framework to Track Suicidal Ideation on Social Media

Author: Sandra Bringay, Bilel Moulahi, Jérôme Azé, ADVanced Analytics for data SciencE (ADVANSE), 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), and Université Paul-Valéry - Montpellier 3 (UPVM)
Subjects: Conditional random field, Recall, Computer science, Microblogging, 05 social sciences, Applied psychology, Context, 020207 software engineering, Context (language use), 02 engineering and technology, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Suicide, CRFs, Emotional states, 0202 electrical engineering, electronic engineering, information engineering, medicine, 0501 psychology and cognitive sciences, Social media, Tracking (education), medicine.symptom, CRFS, Social Media, Suicidal ideation, 050107 human factors
Abstract: International audience; The abundance and growing usage of social media has given an unprecedented access to users' social accounts for studying people's thoughts and sentiments. In this work, we are interested in tracking in-dividual's emotional states and more specifically suicidal ideation in mi-croblogging services. We propose a probabilistic framework that models user's online activities as a sequence of psychological states over time and predicts the emotional states by incorporating the context history. Based on Conditional Random Fields, our model is able to provide comprehensive interpretations of the relationship between the risk factors and psychological states. We evaluated our approach within real case studies of Twitter' users that have demonstrated a serious change in their emotional states and online behaviour. Our experiments show that the model is able to identify suicidal ideation with high precision and good recall with substantial improvements on state-of-the-art methods.
Published: 2017
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11. Detection of suicide-related posts in Twitter data streams

Author: Sandra Bringay, M. Johnson Vioulès, Jérôme Azé, Bilel Moulahi, ADVanced Analytics for data SciencE (ADVANSE), 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), Département de Mathématiques et Informatique Appliquées (MIAP), and Université Paul-Valéry - Montpellier 3 (UPVM)
Subjects: General Computer Science, Computer science, Event (computing), Data stream mining, 02 engineering and technology, Martingale (betting system), Data science, Suicide prevention, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 030227 psychiatry, 03 medical and health sciences, Identification (information), 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, medicine, 020201 artificial intelligence & image processing, Social media, medicine.symptom, Suicidal ideation, Change detection
Abstract: International audience; Suicidal ideation detection in online social networks is an emerging research area with major challenges. Recent research has shown that the publicly available information spread across social media platforms holds valuable indicators to effectively detecting individuals with suicidal intentions. The key challenge of suicide prevention is understanding and detecting the complex risk factors and warning signs that may precipitate the event. In this paper, we present a new approach that uses the social media platform Twitter to quantify suicide-warning signs for individuals and to detect posts containing suicide-related content. The main originality of this approach is the automatic identification of sudden changes in a user's online behavior. To detect such changes, we combine natural language processing techniques to aggregate behavioral and textual features and pass these features through a martingale framework, which is widely used for change detection in data streams. Experiments show that our text-scoring approach effectively captures warning signs in text compared to traditional machine learning classifiers. Additionally, the application of the martingale framework highlights changes in online behavior and shows promise for detecting behavioral changes in at-risk individuals.
Published: 2018
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12. Collaborative Content-Based Method for Estimating User Reputation in Online Forums

Author: Sandra Bringay, Pascal Poncelet, Jérôme Azé, Amine Abdaoui, ADVanced Analytics for data SciencE (ADVANSE), 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), Université Paul-Valéry - Montpellier 3 (UPVM), and ANR-12-JS02-0010,SIFR,Indexation sémantique de ressources biomédicales francophones(2012)
Subjects: social networks, business.industry, Computer science, media_common.quotation_subject, [INFO.INFO-WB]Computer Science [cs]/Web, Internet privacy, reputation, 02 engineering and technology, Trust, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, World Wide Web, online forums, Order (business), 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, business, Content (Freudian dream analysis), Reputation, media_common
Abstract: International audience; Collaborative ratings of forum posts have been successfully applied in order to infer the reputations of forum users. Famous websites such as Slashdot or Stack Exchange allow their users to score messages in order to evaluate their content. These scores can be aggregated for each user in order to compute a reputation value in the forum. However, explicit rating functionalities are rarely used in many online communities such as health forums. At the same time, the textual content of the messages can reveal a lot of information regarding the trust that users have in the posted information. In this work, we propose to use these hidden expressions of trust in order to estimate user reputation in online forums.
Published: 2015
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13. HydroQual: Visual analysis of river water quality

Author: Pascal Poncelet, Nathalie Lalande, Agnès Braud, Flavie Cernesson, Pierre Accorsi, Sandra Bringay, Florence Le Ber, Maguelonne Teisseire, Mickaël Fabrègue, Arnaud Sallaberry, Université de Montpellier (UM), ADVanced Analytics for data SciencE (ADVANSE), 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), Université Paul-Valéry - Montpellier 3 (UPVM), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), ANR-11-MONU-0014,Fresqueau,Fouille de données pour l'évaluation et le suivi de la qualité hydrobiologique des cours d'eau(2011), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UM3), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, and Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0106 biological sciences, Visual analytics, Computer science, media_common.quotation_subject, 02 engineering and technology, computer.software_genre, 01 natural sciences, Environmental issue, 0202 electrical engineering, electronic engineering, information engineering, Quality (business), [INFO]Computer Science [cs], Environmental planning, media_common, Creative visualization, Intensive farming, 010604 marine biology & hydrobiology, 020207 software engineering, 15. Life on land, 6. Clean water, Water resources, Industrialisation, Water quality, Spatiotemporal data mining and visualization, 13. Climate action, Data mining, computer
Abstract: International audience; Economic development based on industrialization, intensive agriculture expansion and population growth places greater pressure on water resources through increased water abstraction and water quality degradation [40]. River pollution is now a visible issue, with emblematic ecological disasters following industrial accidents such as the pollution of the Rhine river in 1986 [31]. River water quality is a pivotal public health and environmental issue that has prompted governments to plan initiatives for preserving or restoring aquatic ecosystems and water resources [56]. Water managers require operational tools to help interpret the complex range of information available on river water quality functioning. Tools based on statistical approaches often fail to resolve some tasks due to the sparse nature of the data. Here we describe HydroQual, a tool to facilitate visual analysis of river water quality. This tool combines spatiotem-poral data mining and visualization techniques to perform tasks defined by water experts. We illustrate the approach with a case study that illustrates how the tool helps experts analyze water quality. We also perform a qualitative evaluation with these experts.
Published: 2014
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14. Mining Representative Frequent Patterns in a Hierarchy of Contexts

Author: Julien Rabatel, Pascal Poncelet, Sandra Bringay, Itesoft R&D, ITESOFT, ADVanced Analytics for data SciencE (ADVANSE), 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), and Université Paul-Valéry - Montpellier 3 (UPVM)
Subjects: Hierarchy, Pattern language, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Process (engineering), business.industry, Computer science, Context (language use), 02 engineering and technology, Pattern Mining, Machine learning, computer.software_genre, Variety (cybernetics), 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Contextual information, 020201 artificial intelligence & image processing, Use case, Artificial intelligence, Data mining, business, computer, ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.8: Database Applications/H.2.8.0: Data mining
Abstract: International audience; More and more data come with contextual information describing the circumstances of their acquisition. While the frequent pattern mining literature offers a lot of approaches to handle and extract interesting patterns in data, little effort has been dedicated to relevantly handling such contextual information during the mining process. In this paper we propose a generic formulation of the contextual frequent pattern mining problem and provide the CFPM algorithm to mine frequent patterns that are representative of a context. This approach is generic w.r.t. the pattern language (e.g., itemsets, sequential patterns, subgraphs, etc.) and therefore is applicable in a wide variety of use cases. The CFPM method is experimented on real datasets with three different pattern languages to assess its performances and genericity.
Published: 2014
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15. Steganalysis with cover-source mismatch and a small learning database

Author: Jérôme Pasquet, Sandra Bringay, Marc CHAUMONT, Image & Interaction (ICAR), 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), ADVanced Analytics for data SciencE (ADVANSE), and Université Paul-Valéry - Montpellier 3 (UPVM)
Subjects: 021110 strategic, defence & security studies, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Ensemble Average Perceptron, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Cover-Source Mismatch, Steganalysis, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 02 engineering and technology, Clustering, Ensemble Classifiers with Post-Selection of Features
Abstract: International audience; Many different hypotheses may be chosen for modeling a steganography/steganalysis problem. In this paper, we look closer into the case in which Eve, the steganalyst, has partial or erroneous knowledge of the cover distribution. More precisely we suppose that Eve knows the algorithms and the payload size that has been used by Alice, the steganographer, but she ignores the images distribution. In this source-cover mismatch scenario, we demonstrate that an Ensemble Classifier with Features Selection (EC-FS) allows the steganalyst to obtain the best state-of-the-art performances, while requiring 100 times smaller training database compared to the previous state-of-the art approach. Moreover, we propose the islet approach in order to increase the classification performances.
Published: 2014

16. Identifying the Targets of the Emotions Expressed in Health Forums

Author: Pascal Poncelet, Eric Kergosien, Pierre Pompidor, Sandra Bringay, ADVanced Analytics for data SciencE (ADVANSE), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Département de Mathématiques et Informatique Appliquées (MIAP), Université Paul-Valéry - Montpellier 3 (UM3), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Université Paul-Valéry - Montpellier 3 (UPVM), and Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)
Subjects: Focus (computing), Knowledge management, Health professionals, business.industry, Computer science, Emotion analysis, Internet privacy, Sentiment analysis, education, 020207 software engineering, 02 engineering and technology, humanities, Opinion mining, Semantic role labeling, Resource (project management), 0202 electrical engineering, electronic engineering, information engineering, Health system applications, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], Personal experience, FrameNet, business, health care economics and organizations, Anonymity
Abstract: International audience; In the framework of the French project Patients’ Mind, we focus on the semi-automatic analysis of online health forums. Online health forums are areas of exchange where patients, on condition of anonymity, can talk about their personal experiences freely. These resources are a gold mine for health professionals, giving them access to patient to patient exchanges, patient to health professional exchanges and even health professional to health professional exchanges. In this paper, we focus on the emotions expressed by the authors of the messages and more precisely on the targets of these emotions. We suggest an innovative method to identify these targets, based on the notion of semantic roles and using the FrameNet resource. Our method has been successfully validated on real data set.
Published: 2014
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17. Predicting Medical Roles in Online Health Fora

Author: Sandra Bringay, Jérôme Azé, Natalia Grabar, Pascal Poncelet, Amine Abdaoui, ADVanced Analytics for data SciencE (ADVANSE), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UPVM), Savoirs, Textes, Langage (STL) - UMR 8163 (STL), Université de Lille-Centre National de la Recherche Scientifique (CNRS), Patients' Mind, and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)
Subjects: Medical information, text mining, 02 engineering and technology, computer.software_genre, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, Text categorization, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 0202 electrical engineering, electronic engineering, information engineering, Selection (linguistics), Medicine, 030212 general & internal medicine, Multimedia, Health professionals, business.industry, Interrogative, Data science, 3. Good health, Categorization, Order (business), online health fora, 020201 artificial intelligence & image processing, business, computer
Abstract: International audience; Online health fora are increasingly visited by patients to get help and information related to their health. However, these fora are not limited to patients: a significant number of health professionals actively participate in many discussions. As experts their posted information are very important since, they are able to well explain the problems, the symptoms, correct false affirmations and give useful advices, etc. For someone interested in trusty medical information , obtaining only these kinds of posts can be very useful and informative. Unfortunately, extracting such knowledge needs to navigate over the fora in order to evaluate the information. Navigation and selection are time consuming, tedious, difficult and error-prone activities when done manually. It is thus important to propose a new method for automatically categorize information proposed both by non-experts as well as by professionals in online health fora. In this paper, we propose to use a supervised approach to evaluate what are the most representative components of a post considering vocabularies, uncertainty markers, emotions, misspellings and interrogative forms to perform efficiently this categorization. Experiments have been conducted on two real fora and shown that our approach is efficient for extracting posts done by professionals.
Published: 2014
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18. Spatio-temporal patterns. Challenges and application to environment

Author: Frédéric Flouvat, Hugo Alatrista Salas, Sandra Bringay, Nazha Selmaoui-Folcher, Pôle Pluridisciplinaire de la Matière et de l'Environnement (PPME), Université de la Nouvelle-Calédonie (UNC), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), ADVanced Analytics for data SciencE (ADVANSE), 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), Département de Mathématiques et Informatique Appliquées (MIAP), and Université Paul-Valéry - Montpellier 3 (UPVM)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Artificial Intelligence, colocalisations, 0202 electrical engineering, electronic engineering, information engineering, sequential patterns, motifs séquentiels, spatio-temporal data mining, 020201 artificial intelligence & image processing, fouille de données spatio-temporelles, 02 engineering and technology, colocations, Software
Abstract: International audience; Technological advances in terms of data acquisition enable to better monitor dynamic phenomena in various domains including environment. The collected data are more and more complex (spatial, temporal, heterogeneous and multi-scale). The exploitation of this data requires new methods of data analysis and knowledge discovery. In this context, approaches for discovering spatio-temporal patterns are particularly relevant. This paper proposes to make a detailed review of these works. We focus on two examples of patterns : colocation and spatiosequential patterns. These patterns have been used to study real applications in the ﬁeld of environment.; Les avancées technologiques en termes d’acquisition de données permettent de mieux surveiller les phénomènes évolutifs dans divers domaines dont l’environnement. Les données collectées sont de plus en plus complexes (spatiales, temporelles, hétérogènes et multi-échelles). L’exploitation de ces données par leurs propriétaires (experts du domaine) nécessite de concevoir de nouvelles méthodes d’analyse de données et de découverte de connaissances. Dans ce contexte, les approches de découverte de motifs spatio-temporels se révèlent particulièrement pertinentes. Ce papier propose de faire une revue détaillée de ces travaux. Nous nous focalisons sur deux exemples de motifs : les colocalisations et les motifs spatio-séquentiels. Ces motifs ont été utilisés pour étudier deux applications réelles dans le domaine de l’environnement.
Published: 2013
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19. How to Extract Relevant Knowledge from Tweets?

Author: Sandra Bringay, Maguelonne Teisseire, Pascal Poncelet, Flavien Bouillot, Nicolas Béchet, Dino Ienco, Phan Nhat Hai, Mathieu Roche, Stan Matwin, ADVanced Analytics for data SciencE (ADVANSE), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Equipe CODAG - Laboratoire GREYC - UMR6072, Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen (GREYC), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UM3), University of Ottawa [Ottawa] (uOttawa), Exploration et exploitation de données textuelles (TEXTE), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU), Université Paul-Valéry - Montpellier 3 (UPVM), University of Ottawa [Ottawa], Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), UNIVERSITE DE CAEN BASSE NORMANDIE FRA, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), UNIVERSITY OF OTTAWA CAN, and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Computer science, 02 engineering and technology, DATA MINING, Set (abstract data type), MOVING OBJECTS, SPATIO-TEMPORAL MININGS, NOISY DATA, Multidimensional Characteristic, 020204 information systems, Left Party, 0202 electrical engineering, electronic engineering, information engineering, Noisy data, TWEET, SPATIO-TEMPORAL DATA MINING, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], business.industry, ALGORITHMS, [INFO.INFO-WB]Computer Science [cs]/Web, Pattern Mining, Object (computer science), Data science, INFORMATION RETRIEVAL, Positioning technology, REAL APPLICATIONS, Move Object Database, Data Mining Approach, POSITIONING TECHNOLOGIES, [SDE]Environmental Sciences, 020201 artificial intelligence & image processing, The Internet, business
Abstract: International audience; Tweets exchanged over the Internet are an important source of information even if their characteristics make them difficult to analyze (e.g., a maximum of 140 characters; noisy data). In this paper, we investigate two different problems. The first one is related to the extraction of representative terms from a set of tweets. More precisely we address the following question: are traditional information retrieval measures appropriate when dealing with tweets?. The second problem is related to the evolution of tweets over time for a set of users. With the development of data mining approaches, lots of very efficient methods have been defined to extract patterns hidden in the huge amount of data available. More recently new spatio-temporal data mining approaches have specifically been defined for dealing with the huge amount of moving object data that can be obtained from the improvement in positioning technology. Due to particularity of tweets, the second question we investigate is the following: are spatio-temporal mining algorithms appropriate for better understanding the behavior of communities over time? These two problems are illustrated through real applications concerning both health and political tweets. © Springer-Verlag Berlin Heidelberg 2013.
Published: 2013
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20. OrderSpan: Mining Closed Partially Ordered Patterns

Author: Agnès Braud, Maguelonne Teisseire, Florence Le Ber, Sandra Bringay, Mickaël Fabrègue, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), ADVanced Analytics for data SciencE (ADVANSE), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UM3), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UPVM), Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Université de Strasbourg (UNISTRA), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)
Subjects: Computer science, SEQUENTIAL PATTERNS, 02 engineering and technology, computer.software_genre, ORDERSPAN, DATA MINING, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Prefix, Task (computing), Simple (abstract algebra), 020204 information systems, PARTIAL ORDERS, [SDE]Environmental Sciences, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Sequential data, Data mining, computer
Abstract: [Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-SISO; International audience; Due to the complexity of the task, partially ordered pattern mining of sequential data has not been subject to much study, despite its usefulness. This paper investigates this data mining challenge by describing OrderSpan, a new algorithm that extracts such patterns from sequential databases and overcomes some of the drawbacks of existing methods. Our work consists in providing a simple and flexible framework to directly mine complex sequences of itemsets, by combining well-known properties on prefixes and suffixes. Experiments were performed on different real datasets to show the benefit of partially ordered patterns.
Published: 2013
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21. Mining Sequential Patterns: a Context-Aware Approach

Author: Sandra Bringay, Pascal Poncelet, Julien Rabatel, Poncelet, Pascal, Springer, ADVanced Analytics for data SciencE (ADVANSE), 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), and Université Paul-Valéry - Montpellier 3 (UPVM)
Subjects: education.field_of_study, Sequential Patterns, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Computer science, Population, Extraction algorithm, Context (language use), Of the form, 02 engineering and technology, computer.software_genre, Data science, Focus (linguistics), 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Contextual information, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], 020201 artificial intelligence & image processing, Sequential data, Product (category theory), Data mining, education, Contextual Patterns, computer
Abstract: International audience; Traditional sequential patterns do not take into account contextual infor- mation associated with sequential data. For instance, when studying purchases of customers in a shop, a sequential pattern could be "frequently, customers buy prod- ucts A and B at the same time, and then buy product C". Such a pattern does not con- sider the age, the gender or the socio-professional category of customers. However, by taking into account contextual information, a decision expert can adapt his/her strategy according to the type of customers. In this paper, we focus on the analysis of a given context (e.g., a category of customers) by extracting context-dependent sequential patterns within this context. For instance, given the context correspond- ing to young customers, we propose to mine patterns of the form "buying products A and B then product C is a general behavior in this population" or "buying products B and D is frequent for young customers only". We formally define such context- dependent sequential patterns and highlight relevant properties that lead to an effi- cient extraction algorithm. We conduct our experimental evaluation on real-world data and demonstrate performance issues.
Published: 2013

22. Including spatial relations and scales within sequential pattern extraction

Author: Sandra Bringay, Maguelonne Teisseire, Florence Le Ber, Agnès Braud, Mickaël Fabrègue, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Laboratoire d'Hydrologie et de Géochimie de Strasbourg (LHyGeS), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Ecole et Observatoire des Sciences de la Terre (EOST), Université de Strasbourg (UNISTRA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection (LSIIT), Centre National de la Recherche Scientifique (CNRS), Fouille de données environnementales (TATOO), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UM3), Knowledge representation, reasonning (ORPAILLEUR), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut national des sciences de l'Univers (INSU - CNRS)-Ecole et Observatoire des Sciences de la Terre (EOST), Institut national des sciences de l'Univers (INSU - CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Département de Mathématiques et Informatique Appliquées (MIAP), Université Paul-Valéry - Montpellier 3 (UPVM), Université de Strasbourg (UNISTRA), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Ecole et Observatoire des Sciences de la Terre (EOST), and Université de Strasbourg (UNISTRA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Watershed, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Computer science, 02 engineering and technology, Aquatic ecosystem, computer.software_genre, Spatial relation, 020204 information systems, Georeference, [SDE]Environmental Sciences, 0202 electrical engineering, electronic engineering, information engineering, Data Mining, 020201 artificial intelligence & image processing, Extraction (military), Spatio-temporal, Data mining, Sequential patterns, Focus (optics), computer, Spatial analysis, Volume (compression)
Abstract: [Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE; International audience; Georeferenced databases contain a huge volume of temporal and spatial data. They are notably used in environmental analysis. Sev- eral works address the problem of mining those data, but none are able to take into account the richness of the data and especially their spatial and temporal dimensions. In this paper, we focus on the extraction of a new kind of spatio-temporal pattern, considering the relationship be- tween spatial objects and geographical scales. We propose an algorithm, STR PrefixGrowth, which can be applied to a huge amount of data. The proposed method is evaluated on hydrological data collected on the Saône watershed during the last 19 years. Our experiments emphasize the contribution of our approach toward the existing methods.
Published: 2012
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23. Des motifs voisins : vers la découverte de motifs spatio-séquentiels

Author: Hugo Alatrista Salas, Frédéric Flouvat, Maguelonne Teisseire, Nazha Selmaoui-Folcher, Sandra Bringay, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Pôle Pluridisciplinaire de la Matière et de l'Environnement (PPME - EA 3325), Université de la Nouvelle Calédonie (UNC), Fouille de données environnementales (TATOO), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université de la Nouvelle-Calédonie (UNC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Pôle Pluridisciplinaire de la Matière et de l'Environnement (PPME), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), and Université Paul-Valéry - Montpellier 3 (UPVM)
Subjects: Measure (data warehouse), [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Computer science, business.industry, 02 engineering and technology, Machine learning, computer.software_genre, 020204 information systems, [SDE]Environmental Sciences, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Relevance (information retrieval), Artificial intelligence, Data mining, business, computer
Abstract: International audience; Health risks management such as epidemics study produces large quantity of spatio-temporal data. The development of new methods able to manage such specific characteristics becomes crucial. To tackle this problem, we define a theoretical framework for extracting spatio-temporal patterns (sequences representing evolution of locations and their neighborhoods over time). Classical frequency support doesn't consider the pattern neighbor neither its evolution over time. We thus propose a new interestingness measure taking into account both spatial and temporal aspects. An algorithm based on pattern-growth approach with efficient successive projections over the database is proposed. Experiments conducted on real datasets highlight the relevance of our method.
Published: 2012
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24. Towards an On-Line Analysis of Tweets Processing

Author: Maguelonne Teisseire, Flavien Bouillot, Pascal Poncelet, Sandra Bringay, Nicolas Béchet, Mathieu Roche, Fouille de données environnementales (TATOO), 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), Université Paul-Valéry - Montpellier 3 (UPVM), Usage-centered design, analysis and improvement of information systems (AxIS), 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)-Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Exploration et exploitation de données textuelles (TEXTE), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Abdelkader Hameurlain, Xiaofang Zhou, Stephen W. Liddle, Klaus-Dieter Schewe, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SPI.OTHER]Engineering Sciences [physics]/Other, Computer science, Context (language use), 02 engineering and technology, DATA MINING, Knowledge extraction, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Relevance (information retrieval), ANALYSE DE DONNEES, TWEET, Hierarchy, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Probabilistic latent semantic analysis, business.industry, [INFO.INFO-WB]Computer Science [cs]/Web, Data science, Data warehouse, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, INFORMATIQUE, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], [SDE]Environmental Sciences, 020201 artificial intelligence & image processing, The Internet, Line (text file), business, ENTREPOT DE DONNEES
Abstract: [Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE; International audience; Tweets exchanged over the Internet represent an important source of information, even if their characteristics make them dicult to analyze (a maximum of 140 characters, etc.). In this paper, we define a data warehouse model to analyze large volumes of tweets by proposing measures relevant in the context of knowledge discovery. The use of data warehouses as a tool for the storage and analysis of textual documents is not new but current measures are not well-suited to the specificities of the manipulated data. We also propose a new way for extracting the context of a concept in a hierarchy. Experiments carried out on real data underline the relevance of our proposal.
Published: 2011
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25. Anomaly Detection in Monitoring Sensor Data for Preventive Maintenance

Author: Julien Rabatel, Sandra Bringay, Pascal Poncelet, Fouille de données environnementales (TATOO), 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), and Université Paul-Valéry - Montpellier 3 (UPVM)
Subjects: Measure (data warehouse), Focus (computing), Behavior characterization, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Computer science, Anomaly (natural sciences), General Engineering, 02 engineering and technology, Anomaly detection, computer.software_genre, Preventive maintenance, Computer Science Applications, Task (project management), Artificial Intelligence, Order (business), 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Data mining, Sequential patterns, computer
Abstract: Research highlights? Data representation in train preventive maintenance data. ? Sequential pattern mining in sensor monitoring data. ? Anomaly detection based on sequential patterns. ? Considering contextual information for precise description of train monitoring data. Today, many industrial companies must face problems raised by maintenance. In particular, the anomaly detection problem is probably one of the most challenging. In this paper we focus on the railway maintenance task and propose to automatically detect anomalies in order to predict in advance potential failures. We first address the problem of characterizing normal behavior. In order to extract interesting patterns, we have developed a method to take into account the contextual criteria associated to railway data (itinerary, weather conditions, etc.). We then measure the compliance of new data, according to extracted knowledge, and provide information about the seriousness and the exact localization of a detected anomaly.
Published: 2011
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26. Contextual Sequential Pattern Mining

Author: Pascal Poncelet, Julien Rabatel, Sandra Bringay, Fouille de données environnementales (TATOO), 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), and Université Paul-Valéry - Montpellier 3 (UPVM)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Information retrieval, Computer science, Context (language use), 02 engineering and technology, computer.software_genre, Contextual design, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Product (category theory), Data mining, Sequential Pattern Mining, computer, Consumer behaviour
Abstract: International audience; Traditional sequential patterns do not take into account additional contextual information since patterns extracted from data are usually general. By considering the fact that a pattern is associated with one specific context the decision expert can then adapt his strategy considering the type of customers. In this paper we propose to mine more precise patterns of the form "young users buy products A and B then product C, while old users do not follow this same behavior". By highlighting relevant properties of such contexts, we show how contextual sequential patterns can be extracted by mining the database in a concise manner. We conduct our experimental evaluation on real-world data and demonstrate performance issues.
Published: 2010
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27. Fuzzy Anomaly Detection in Monitoring Sensor Data

Author: Sandra Bringay, Pascal Poncelet, Julien Rabatel, Fouille de données environnementales (TATOO), 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), and Université Paul-Valéry - Montpellier 3 (UPVM)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Computer science, media_common.quotation_subject, 02 engineering and technology, Anomaly detection, computer.software_genre, Preventive maintenance, Fuzzy logic, 020204 information systems, Rail transportation, Face (geometry), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Quality (business), Data mining, Sequential patterns, computer, media_common
Abstract: International audience; Today, many industrial companies must face challenges raised by maintenance. In particular, the anomaly detection problem is probably one of the most investigated. In this paper we address anomaly detection in new train data by comparing them to a source of normal train behavior knowledge, expressed as sequential patterns. To this end, fuzzy logic allows our approach to be both finer and easier to interpret for experts. In order to show the quality of our approach, experiments have been conducted on real and simulated anomalies.
Published: 2010
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28. Using OWA Operators for Gene Sequential Pattern Clustering

Author: Maguelonne Teisseire, Paola Salle, Jordi Nin, Sandra Bringay, Équipe Tolérance aux fautes et Sûreté de Fonctionnement informatique (LAAS-TSF), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Fouille de données environnementales (TATOO), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Université Paul-Valéry - Montpellier 3 (UPVM), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), CNFTSV CORBAS FRA, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Informàtica::Aplicacions de la informàtica::Bioinformàtica [Àrees temàtiques de la UPC], Computer science, media_common.quotation_subject, 02 engineering and technology, computer.software_genre, Domain (software engineering), Task (project management), Medical computing, 03 medical and health sciences, 0302 clinical medicine, Similarity (network science), Knowledge extraction, Common knowledge, 0202 electrical engineering, electronic engineering, information engineering, Mathematical operators, Cluster analysis, Function (engineering), Data mining, media_common, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], CLUSTER, Pattern clustering, Automatic summarization, Medical administrative data processing, [SDE]Environmental Sciences, 020201 artificial intelligence & image processing, Mineria de dades, computer, 030217 neurology & neurosurgery
Abstract: International audience; Nowadays, the management of sequential patterns data is an increasing need in many biological data mining and knowledge discovery processes. Indeed, due to the large number of sequential patterns extracted, an efﬁcient interpretation of the results is difﬁcult. Thus, biologists are waiting for new approaches in order to help them during this analysis. Therefore, the development of new data mining techniques for sequential patterns becomes a crucial need. One of the most common data mining and knowledge discovery processes are clustering algorithms. However, there is not too much literature about the application of clustering algorithms to gene sequential patterns due to the difﬁculty of applying such algorithms to this kind of data. In this paper, we introduce a new gene sequential patterns similarity function and summarization algorithm. We illustrate the feasibility of both contributions combining them into an hierarchical clustering algorithm.
Published: 2009
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29. SO_MAD: SensOr Mining for Anomaly Detection in Railway Data

Author: Julien Rabatel, Pascal Poncelet, Sandra Bringay, Fouille de données environnementales (TATOO), 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), and Université Paul-Valéry - Montpellier 3 (UPVM)
Subjects: Measure (data warehouse), Focus (computing), Behavior characterization, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Computer science, Anomaly (natural sciences), 020206 networking & telecommunications, 02 engineering and technology, Anomaly detection, computer.software_genre, Task (project management), Order (business), Face (geometry), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Data mining, Sequential patterns, computer
Abstract: International audience; Today, many industrial companies must face problems raised by maintenance. In particular, the anomaly detection problem is probably one of the most challenging. In this paper we focus on the railway maintenance task and propose to automatically detect anomalies in order to predict in advance potential failures. We ﬁrst address the problem of characterizing normal behavior. In order to extract interesting patterns, we have developed a method to take into account the contextual criteria associated to railway data (itinerary, weather conditions, etc.). We then measure the compliance of new data, according to extracted knowledge, and provide information about the seriousness and possible causes of a detected anomaly.
Published: 2009
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30. Mining Discriminant Sequential Patterns for Aging Brain

Author: Paola Salle, Maguelonne Teisseire, Sandra Bringay, Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Fouille de données environnementales (TATOO), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Département de Mathématiques et Informatique Appliquées (MIAP), Université Paul-Valéry - Montpellier 3 (UPVM), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF)
Subjects: 0303 health sciences, Biological data, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Association rule learning, Computer science, 02 engineering and technology, computer.software_genre, Expression (mathematics), 03 medical and health sciences, ComputingMethodologies_PATTERNRECOGNITION, Discriminant, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Relevance (information retrieval), Data mining, DNA microarray, computer, 030304 developmental biology
Abstract: International audience; Discovering new information about groups of genes implied in a disease is still challenging. Microarrays are a powerful tool to analyse gene expression. They provide an expression level for genes under given biological situations. In this paper, we propose a new approach outlining relationships between genes based on their ordered expressions. Our contribution is twofold. First, we propose to use a new material, called sequential patterns, to be investigated by biologists. But, due to the expression matrice density, extracting sequential patterns from microarray datasets is far away from being easy. Secondly, we propose to introduce a knowledge support during the mining task. In this way, the search space is reduced and more relevant results (from a biological point of view) are obtained. Results of various experiments on real biological data highlight the relevance of our proposal.
Published: 2009
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31. Sequential Patterns for Maintaining Ontologies Over Time

Author: Maguelonne Teisseire, Anne Laurent, Sandra Bringay, Lisa Di-Jorio, C. Fiot, Fouille de données environnementales (TATOO), 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), Université Paul-Valéry - Montpellier 3 (UPVM), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Association rule learning, Process (engineering), business.industry, media_common.quotation_subject, 02 engineering and technology, Machine learning, computer.software_genre, Metadata, Identification (information), 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Relevance (information retrieval), Quality (business), Artificial intelligence, Representation (mathematics), business, computer, Natural language processing, Mathematics, media_common
Abstract: International audience; Ontologies are known as a quality and functional model, allowing meta data representation and reasoning. However, their maintenance plays a crucial role as ontologies may be misleading if they are not up to date. Currently, this work is done manually, and raises the problem of expert subjectivity. Therefore, some works have developed maintenance tools but none has allowed a precise identification of the relations that could link concepts. In this paper, we propose a new fully generic approach combining sequential patterns extraction and equivalence classes. Our method allows to identify terms from textual documents and to define labelized association rules from sequential patterns according to relevance and neighborhood measures. Moreover, this process proposes the placement of the found elements refined by the use of equivalence classes. Results of various experiments on real data highlight the relevance of our proposal.
Published: 2008
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32. Sequential patterns mining and gene sequence visualization to discover novelty from microarray data

Author: Maguelonne Teisseire, Sandra Bringay, Arnaud Sallaberry, Mathieu Roche, N. Pecheur, Graph Visualization and Interactive Exploration (GRAVITE), Université Sciences et Technologies - Bordeaux 1 (UB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Fouille de données environnementales (TATOO), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UPVM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Exploration et exploitation de données textuelles (TEXTE), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Sciences et Technologies - Bordeaux 1, Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), and Université Sciences et Technologies - Bordeaux 1-Inria Bordeaux - Sud-Ouest
Subjects: Computer science, Bioinformatics, media_common.quotation_subject, Health Informatics, 02 engineering and technology, computer.software_genre, Alzheimer Disease, Neoplasms, Databases, Genetic, 0202 electrical engineering, electronic engineering, information engineering, Sequential Pattern Mining, Data mining, media_common, Visualization, Microarray data, Focus (computing), Creative visualization, End user, Microarray analysis techniques, Gene Expression Profiling, Novelty, 020207 software engineering, Microarray Analysis, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, Gene data, 020201 artificial intelligence & image processing, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Gene sequence, Sequential patterns, computer, Algorithms
Abstract: [Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE; International audience; Data mining allow users to discover novelty in huge amounts of data. Frequent pattern methods have proved to be efficient, but the extracted patterns are often too numerous and thus difficult to analyze by end users. In this paper, we focus on sequential pattern mining and propose a new visualization system to help end users analyze the extracted knowledge and to highlight novelty according to databases of referenced biological documents. Our system is based on three visualization techniques: clouds, solar systems, and treemaps. We show that these techniques are very helpful for identifying associations and hierarchical relationships between patterns among related documents. Sequential patterns extracted from gene data using our system were successfully evaluated by two biology laboratories working on Alzheimer's disease and cancer.
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33. Discovering Novelty in Gene Data : From Sequential Patterns to Visualization

Author: Maguelonne Teisseire, Arnaud Sallaberry, Nicolas Pecheur, Mathieu Roche, Sandra Bringay, Graph Visualization and Interactive Exploration (GRAVITE), Université Sciences et Technologies - Bordeaux 1 (UB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Fouille de données environnementales (TATOO), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UPVM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Exploration et exploitation de données textuelles (TEXTE), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Sallaberry, Arnaud, Université Sciences et Technologies - Bordeaux 1-Inria Bordeaux - Sud-Ouest, Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Sciences et Technologies - Bordeaux 1, and Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: Creative visualization, Focus (computing), [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 020205 medical informatics, Computer science, media_common.quotation_subject, Novelty, Point cloud, 020207 software engineering, 02 engineering and technology, computer.software_genre, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Visualization, [SDE]Environmental Sciences, 0202 electrical engineering, electronic engineering, information engineering, Data mining, Sequential Pattern Mining, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, media_common, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract: [Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE; International audience; Data mining techniques allow users to discover novelty in huge amounts of data. Frequent pattern methods have proved to be efficient, but the extracted patterns are often too numerous and thus difficult to analyse by end-users. In this paper, we focus on sequential pattern mining and propose a new visualization system, which aims at helping end-users to analyse extracted nowledge and to highlight the novelty according to referenced biological document databases. Our system is based on two visualization techniques: Clouds and solar systems. We show that these techniques are very helpful for identifying associations and hierarchical relationships between patterns among related documents. Sequential patterns extracted from gene data using our system were successfully evaluated by two biology laboratories working on Alzheimers disease and cancer.

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33 results on '"Sandra Bringay"'

1. Divide to Better Classify

2. LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification

3. Expertise in French health forums

4. Language Modeling in Temporal Mood Variation Models for Early Risk Detection on the Internet

5. Spatio-sequential patterns mining: Beyond the boundaries

6. Reconciliation of patient/doctor vocabulary in a structured resource

7. FEEL: a French Expanded Emotion Lexicon

8. Raising Awareness About Cervical Cancer Using Twitter: Content Analysis of the 2015 #SmearForSmear Campaign

9. What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer

10. DARE to Care: A Context-Aware Framework to Track Suicidal Ideation on Social Media

11. Detection of suicide-related posts in Twitter data streams

12. Collaborative Content-Based Method for Estimating User Reputation in Online Forums

13. HydroQual: Visual analysis of river water quality

14. Mining Representative Frequent Patterns in a Hierarchy of Contexts

15. Steganalysis with cover-source mismatch and a small learning database

16. Identifying the Targets of the Emotions Expressed in Health Forums

17. Predicting Medical Roles in Online Health Fora

18. Spatio-temporal patterns. Challenges and application to environment

19. How to Extract Relevant Knowledge from Tweets?

20. OrderSpan: Mining Closed Partially Ordered Patterns

21. Mining Sequential Patterns: a Context-Aware Approach

22. Including spatial relations and scales within sequential pattern extraction

23. Des motifs voisins : vers la découverte de motifs spatio-séquentiels

24. Towards an On-Line Analysis of Tweets Processing

25. Anomaly Detection in Monitoring Sensor Data for Preventive Maintenance

26. Contextual Sequential Pattern Mining

27. Fuzzy Anomaly Detection in Monitoring Sensor Data

28. Using OWA Operators for Gene Sequential Pattern Clustering

29. SO_MAD: SensOr Mining for Anomaly Detection in Railway Data

30. Mining Discriminant Sequential Patterns for Aging Brain

31. Sequential Patterns for Maintaining Ontologies Over Time

32. Sequential patterns mining and gene sequence visualization to discover novelty from microarray data

33. Discovering Novelty in Gene Data : From Sequential Patterns to Visualization

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33 results on '"Sandra Bringay"'

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