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vol 20 n° 4 - juillet - août 2015 - Systèmes collaboratifs et réseaux sociaux : approches d'ingénierie (Bulletin de Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI) / Lotfi Bouzguenda
[n° ou bulletin]
est un bulletin de Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI (2001 -)
Titre : vol 20 n° 4 - juillet - août 2015 - Systèmes collaboratifs et réseaux sociaux : approches d'ingénierie Type de document : Périodique Auteurs : Lotfi Bouzguenda, Éditeur scientifique ; Samira Si-Said Cherfi, Éditeur scientifique Année de publication : 2015 Langues : Français (fre) Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information
[Termes IGN] ingénierie
[Termes IGN] organisation du travail
[Termes IGN] réseau social
[Termes IGN] système de conduite collaboratif
[Termes IGN] technologies de l'information et de la communicationNuméro de notice : 093-201504 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Numéro de périodique En ligne : http://isi.revuesonline.com/gratuit/ISI20_4_01_som_ISI.pdf Format de la ressource électronique : URL sommaire Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=25603 [n° ou bulletin]Contient
- Un environnement collaboratif pour l’acquisition de compétences en conception-développement d’applications centrées utilisateur. Application aux systèmes d'assitance à la santé et au bien-être / Maha Khemaja in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 20 n° 4 (juillet - août 2015)
- Travail collaboratif situé pour l’ingénierie. Quels aspects à prendre en compte ? / Bertrand David in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 20 n° 4 (juillet - août 2015)
- Une approche de filtrage d’opinions à base de crédibilité dans un contexte de réseaux sociaux / Lobna Azaza in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 20 n° 4 (juillet - août 2015)
- Analyse du comportement d’annotation du réseau social d’un utilisateur pour la détection des intérêts. Application sur Delicious / Manel Mezghani in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 20 n° 4 (juillet - août 2015)
- The MAIN+ method for collaboration digitalization / Imed Boughzala in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 20 n° 4 (juillet - août 2015)
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Code-barres Cote Support Localisation Section Disponibilité 093-2015041 SL Revue Centre de documentation Revues en salle Disponible A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management / João Porto de Albuquerque in International journal of geographical information science IJGIS, vol 29 n° 4 (April 2015)
[article]
Titre : A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management Type de document : Article/Communication Auteurs : João Porto de Albuquerque, Auteur ; Benjamin Herfort, Auteur ; Alexander Brenning, Auteur ; Alexander Zipf, Auteur Année de publication : 2015 Article en page(s) : pp 667 - 689 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] acquisition de données
[Termes IGN] Allemagne
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données localisées des bénévoles
[Termes IGN] Elbe (fleuve)
[Termes IGN] géopositionnement
[Termes IGN] gestion de crise
[Termes IGN] inondation
[Termes IGN] qualité des données
[Termes IGN] risque naturel
[Termes IGN] TwitterRésumé : (Auteur) In recent years, social media emerged as a potential resource to improve the management of crisis situations such as disasters triggered by natural hazards. Although there is a growing research body concerned with the analysis of the usage of social media during disasters, most previous work has concentrated on using social media as a stand-alone information source, whereas its combination with other information sources holds a still underexplored potential. This article presents an approach to enhance the identification of relevant messages from social media that relies upon the relations between georeferenced social media messages as Volunteered Geographic Information and geographic features of flood phenomena as derived from authoritative data (sensor data, hydrological data and digital elevation models). We apply this approach to examine the micro-blogging text messages of the Twitter platform (tweets) produced during the River Elbe Flood of June 2013 in Germany. This is performed by means of a statistical analysis aimed at identifying general spatial patterns in the occurrence of flood-related tweets that may be associated with proximity to and severity of flood events. The results show that messages near (up to 10 km) to severely flooded areas have a much higher probability of being related to floods. In this manner, we conclude that the geographic approach proposed here provides a reliable quantitative indicator of the usefulness of messages from social media by leveraging the existing knowledge about natural hazards such as floods, thus being valuable for disaster management in both crisis response and preventive monitoring. Numéro de notice : A2015-591 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.996567 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.996567 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77878
in International journal of geographical information science IJGIS > vol 29 n° 4 (April 2015) . - pp 667 - 689[article]The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns / Nick Malleson in Cartography and Geographic Information Science, Vol 42 n° 2 (April 2015)
[article]
Titre : The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns Type de document : Article/Communication Auteurs : Nick Malleson, Auteur ; Martin A. Andresen, Auteur Année de publication : 2015 Article en page(s) : pp 112 - 121 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] infraction
[Termes IGN] production participative
[Termes IGN] réseau social
[Termes IGN] Yorkshire (Angleterre)Résumé : (auteur) Crime rate is a statistic used to summarize the risk of criminal events. However, research has shown that choosing the appropriate denominator is non-trivial. Different crime types exhibit different spatial opportunities and so does the population at risk. The residential population is the most commonly used population at risk, but is unlikely to be suitable for crimes that involve mobile populations. In this article, we use “crowd-sourced” data in Leeds, England, to measure the population at risk, considering violent crime. These new data sources have the potential to represent mobile populations at higher spatial and temporal resolutions than other available data. Through the use of two local spatial statistics (Getis-Ord GI* and the Geographical Analysis Machine) and visualization, we show that when the volume of social media messages, as opposed to the residential population, is used as a proxy for the population at risk, criminal event hot spots shift spatially. Specifically, the results indicate a significant shift in the city center, eliminating its hot spot. Consequently, if crime reduction/prevention efforts are based on resident population based crime rates, such efforts may not only be ineffective in reducing criminal event risk, but be a waste of public resources. Numéro de notice : A2015-237 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2014.905756 En ligne : https://doi.org/10.1080/15230406.2014.905756 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76494
in Cartography and Geographic Information Science > Vol 42 n° 2 (April 2015) . - pp 112 - 121[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-2015021 RAB Revue Centre de documentation En réserve L003 Disponible Geo-located community detection in Twitter with enhanced fast-greedy optimization of modularity: the case study of typhoon Haiyan / Mohamed Bakillah in International journal of geographical information science IJGIS, vol 29 n° 2 (February 2015)
[article]
Titre : Geo-located community detection in Twitter with enhanced fast-greedy optimization of modularity: the case study of typhoon Haiyan Type de document : Article/Communication Auteurs : Mohamed Bakillah, Auteur ; Ren-Yu Li, Auteur ; Steve H.L. Liang, Auteur Année de publication : 2015 Article en page(s) : pp 258 - 279 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] communauté virtuelle
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] exploration de données géographiques
[Termes IGN] géopositionnement
[Termes IGN] risque naturel
[Termes IGN] TwitterRésumé : (Auteur) As they increase in popularity, social media are regarded as important sources of information on geographical phenomena. Studies have also shown that people rely on social media to communicate during disasters and emergency situation, and that the exchanged messages can be used to get an insight into the situation. Spatial data mining techniques are one way to extract relevant information from social media. In this article, our aim is to contribute to this field by investigating how graph clustering can be applied to support the detection of geo-located communities in Twitter in disaster situations. For this purpose, we have enhanced the fast-greedy optimization of modularity (FGM) clustering algorithm with semantic similarity so that it can deal with the complex social graphs extracted from Twitter. Then, we have coupled the enhanced FGM with the varied density-based spatial clustering of applications with noise spatial clustering algorithm to obtain spatial clusters at different temporal snapshots. The method was experimented with a case study on typhoon Haiyan in the Philippines, and Twitter’s different interaction modes were compared to create the graph of users and to detect communities. The experiments show that communities that are relevant to identify areas where disaster-related incidents were reported can be extracted, and that the enhanced algorithm outperforms the generic one in this task. Numéro de notice : A2015-579 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.964247 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.964247 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77841
in International journal of geographical information science IJGIS > vol 29 n° 2 (February 2015) . - pp 258 - 279[article]
Titre : Traffic prediction and analysis using a big data and visualisation approach Type de document : Article/Communication Auteurs : Declan McHugh, Auteur Editeur : Leeds [Royaume-Uni] : University of Leeds Année de publication : 2015 Conférence : GISRUK 2015, 23th GIS Research UK annual conference 15/04/2015 17/04/2015 Leeds Royaume-Uni open access proceedings Importance : pp 408 - 420 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle
[Termes IGN] Dublin (Irlande ; ville)
[Termes IGN] exploration de données géographiques
[Termes IGN] modèle de simulation
[Termes IGN] prévision
[Termes IGN] régression multiple
[Termes IGN] trafic routier
[Termes IGN] Twitter
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) This abstract illustrates an approach of using big data, visualisation and data mining techniques used to predict and analyse traffic. The objective is to understand Traffic patterns in Dublin City. The prediction model was used as an estimator to identify unusual traffic patterns. The generic model was designed using data mining techniques, multivariate regression algorithms, ARIMA and visually correlated with real-time traffic tweets. Using the prediction model and tweet event detection. The result is a high-performance web application containing over 500,000,000,000 traffic observations that produce analytical dashboard providing traffic prediction and analysis. Numéro de notice : C2015-049 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Communication DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83863 Documents numériques
en open access
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