ºÝºÝߣshows by User: bilelidiri / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: bilelidiri / Tue, 04 Jun 2013 05:17:00 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: bilelidiri The geographical decision-making chain: formalization and application to maritime risk analysis /slideshow/the-geographical-decision-making-chain/22426606 thegeographicaldecision-makingchain-130604051700-phpapp02
Maritime traffic monitoring needs tools for spatiotemporal decision support. The operators responsible (e.g. the Coast Guard) must monitor vessels that are represented as objects moving in space and time. Operators use maritime tracking systems to follow the evolution of traffic and make decisions about the risks of a situation. These systems are based on Geographic Information Systems (GIS) and OnLine Transaction Processing (OLTP) approaches, which are prohibitively expensive, very slow and produce operational data unsuited to decision-making. Instead, operators require summarized data that is easier for them to produce and use. Therefore, we propose the definition of a geographical decision-making chain that adds a decision-making dimension to current systems. It consists of a carefully assembled set of tools that can automate the three phases of Business Intelligence, namely data loading, modelling and analysis.]]>
Maritime traffic monitoring needs tools for spatiotemporal decision support. The operators responsible (e.g. the Coast Guard) must monitor vessels that are represented as objects moving in space and time. Operators use maritime tracking systems to follow the evolution of traffic and make decisions about the risks of a situation. These systems are based on Geographic Information Systems (GIS) and OnLine Transaction Processing (OLTP) approaches, which are prohibitively expensive, very slow and produce operational data unsuited to decision-making. Instead, operators require summarized data that is easier for them to produce and use. Therefore, we propose the definition of a geographical decision-making chain that adds a decision-making dimension to current systems. It consists of a carefully assembled set of tools that can automate the three phases of Business Intelligence, namely data loading, modelling and analysis.]]> Tue, 04 Jun 2013 05:17:00 GMT /slideshow/the-geographical-decision-making-chain/22426606 bilelidiri@slideshare.net(bilelidiri) The geographical decision-making chain: formalization and application to maritime risk analysis bilelidiri Maritime traffic monitoring needs tools for spatiotemporal decision support. The operators responsible (e.g. the Coast Guard) must monitor vessels that are represented as objects moving in space and time. Operators use maritime tracking systems to follow the evolution of traffic and make decisions about the risks of a situation. These systems are based on Geographic Information Systems (GIS) and OnLine Transaction Processing (OLTP) approaches, which are prohibitively expensive, very slow and produce operational data unsuited to decision-making. Instead, operators require summarized data that is easier for them to produce and use. Therefore, we propose the definition of a geographical decision-making chain that adds a decision-making dimension to current systems. It consists of a carefully assembled set of tools that can automate the three phases of Business Intelligence, namely data loading, modelling and analysis. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/thegeographicaldecision-makingchain-130604051700-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Maritime traffic monitoring needs tools for spatiotemporal decision support. The operators responsible (e.g. the Coast Guard) must monitor vessels that are represented as objects moving in space and time. Operators use maritime tracking systems to follow the evolution of traffic and make decisions about the risks of a situation. These systems are based on Geographic Information Systems (GIS) and OnLine Transaction Processing (OLTP) approaches, which are prohibitively expensive, very slow and produce operational data unsuited to decision-making. Instead, operators require summarized data that is easier for them to produce and use. Therefore, we propose the definition of a geographical decision-making chain that adds a decision-making dimension to current systems. It consists of a carefully assembled set of tools that can automate the three phases of Business Intelligence, namely data loading, modelling and analysis.
The geographical decision-making chain: formalization and application to maritime risk analysis from Bilal IDIRI
]]> 511 6 https://cdn.slidesharecdn.com/ss_thumbnails/thegeographicaldecision-makingchain-130604051700-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Blanc http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 The automatic identification system of maritime accident risk using rule-based reasoning? /slideshow/sose2012-idiri-13826022/13826022 sose2012idiri-120801074534-phpapp01
Pr¨¦sentation lors de la conf¨¦rence IEEE SOSE 2012. Current maritime traffic monitoring systems are not sufficiently adapted to the identification of maritime accident risk. It is very difficult for operators responsible for monitoring traffic to identify which vessels are at risk among all the shipping traffic displayed on their screen. They are overwhelmed by huge amount of kinematic ship data to be decoded. To improve this situation, this paper proposes a system for the automatic identification of maritime accident risk. The system consists of two modules. The first automates expert knowledge acquisition through the computerized exploration of historical maritime data, and the second provides a rule-based reasoning mechanism.]]>
Pr¨¦sentation lors de la conf¨¦rence IEEE SOSE 2012. Current maritime traffic monitoring systems are not sufficiently adapted to the identification of maritime accident risk. It is very difficult for operators responsible for monitoring traffic to identify which vessels are at risk among all the shipping traffic displayed on their screen. They are overwhelmed by huge amount of kinematic ship data to be decoded. To improve this situation, this paper proposes a system for the automatic identification of maritime accident risk. The system consists of two modules. The first automates expert knowledge acquisition through the computerized exploration of historical maritime data, and the second provides a rule-based reasoning mechanism.]]> Wed, 01 Aug 2012 07:45:32 GMT /slideshow/sose2012-idiri-13826022/13826022 bilelidiri@slideshare.net(bilelidiri) The automatic identification system of maritime accident risk using rule-based reasoning? bilelidiri Pr¨¦sentation lors de la conf¨¦rence IEEE SOSE 2012. Current maritime traffic monitoring systems are not sufficiently adapted to the identification of maritime accident risk. It is very difficult for operators responsible for monitoring traffic to identify which vessels are at risk among all the shipping traffic displayed on their screen. They are overwhelmed by huge amount of kinematic ship data to be decoded. To improve this situation, this paper proposes a system for the automatic identification of maritime accident risk. The system consists of two modules. The first automates expert knowledge acquisition through the computerized exploration of historical maritime data, and the second provides a rule-based reasoning mechanism. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sose2012idiri-120801074534-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Pr¨¦sentation lors de la conf¨¦rence IEEE SOSE 2012. Current maritime traffic monitoring systems are not sufficiently adapted to the identification of maritime accident risk. It is very difficult for operators responsible for monitoring traffic to identify which vessels are at risk among all the shipping traffic displayed on their screen. They are overwhelmed by huge amount of kinematic ship data to be decoded. To improve this situation, this paper proposes a system for the automatic identification of maritime accident risk. The system consists of two modules. The first automates expert knowledge acquisition through the computerized exploration of historical maritime data, and the second provides a rule-based reasoning mechanism.
The automatic identification system of maritime accident risk using rule-based reasoning from Bilal IDIRI
]]> 1551 5 https://cdn.slidesharecdn.com/ss_thumbnails/sose2012idiri-120801074534-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 D¨¦couverte de r¨¨gles d¡¯association pour la pr¨¦vision des accidents maritimes https://fr.slideshare.net/slideshow/egc2012-idiri/13825114 egc2012idiri-120801060538-phpapp01
Les syst¨¨mes de surveillance maritime permettent la r¨¦cup¨¦ration et la fusion des informations sur les navires (position, vitesse, etc) ¨¤ des fins de suivi du trafic maritime sur un dispositif d¡¯affichage. Aujourd¡¯hui, l¡¯identification des risques ¨¤ partir de ces syst¨¨mes est difficilement automatisable compte-tenu de l¡¯expertise ¨¤ formaliser, du nombre important de navires et de la multiplicit¨¦ des risques (collision, ¨¦chouage, etc). De plus, le remplacement p¨¦riodique des op¨¦rateurs de surveillance complique la reconnaissance d¡¯¨¦v¨¦nements anormaux qui sont ¨¦parses et parcellaires dans le temps et l¡¯espace. Dans l¡¯objectif de faire ¨¦voluer ces syst¨¨mes de surveillance maritime, nous proposons dans cet article, une approche originale fond¨¦e sur le data mining pour l¡¯extraction de motifs fr¨¦quents. Cette approche se focalise sur des r¨¨gles de pr¨¦vision et de ciblage pour l¡¯identification automatique des situations induisant ou constituant le cadre des accidents maritimes.]]>
Les syst¨¨mes de surveillance maritime permettent la r¨¦cup¨¦ration et la fusion des informations sur les navires (position, vitesse, etc) ¨¤ des fins de suivi du trafic maritime sur un dispositif d¡¯affichage. Aujourd¡¯hui, l¡¯identification des risques ¨¤ partir de ces syst¨¨mes est difficilement automatisable compte-tenu de l¡¯expertise ¨¤ formaliser, du nombre important de navires et de la multiplicit¨¦ des risques (collision, ¨¦chouage, etc). De plus, le remplacement p¨¦riodique des op¨¦rateurs de surveillance complique la reconnaissance d¡¯¨¦v¨¦nements anormaux qui sont ¨¦parses et parcellaires dans le temps et l¡¯espace. Dans l¡¯objectif de faire ¨¦voluer ces syst¨¨mes de surveillance maritime, nous proposons dans cet article, une approche originale fond¨¦e sur le data mining pour l¡¯extraction de motifs fr¨¦quents. Cette approche se focalise sur des r¨¨gles de pr¨¦vision et de ciblage pour l¡¯identification automatique des situations induisant ou constituant le cadre des accidents maritimes.]]> Wed, 01 Aug 2012 06:05:36 GMT https://fr.slideshare.net/slideshow/egc2012-idiri/13825114 bilelidiri@slideshare.net(bilelidiri) D¨¦couverte de r¨¨gles d¡¯association pour la pr¨¦vision des accidents maritimes bilelidiri Les syst¨¨mes de surveillance maritime permettent la r¨¦cup¨¦ration et la fusion des informations sur les navires (position, vitesse, etc) ¨¤ des fins de suivi du trafic maritime sur un dispositif d¡¯affichage. Aujourd¡¯hui, l¡¯identification des risques ¨¤ partir de ces syst¨¨mes est difficilement automatisable compte-tenu de l¡¯expertise ¨¤ formaliser, du nombre important de navires et de la multiplicit¨¦ des risques (collision, ¨¦chouage, etc). De plus, le remplacement p¨¦riodique des op¨¦rateurs de surveillance complique la reconnaissance d¡¯¨¦v¨¦nements anormaux qui sont ¨¦parses et parcellaires dans le temps et l¡¯espace. Dans l¡¯objectif de faire ¨¦voluer ces syst¨¨mes de surveillance maritime, nous proposons dans cet article, une approche originale fond¨¦e sur le data mining pour l¡¯extraction de motifs fr¨¦quents. Cette approche se focalise sur des r¨¨gles de pr¨¦vision et de ciblage pour l¡¯identification automatique des situations induisant ou constituant le cadre des accidents maritimes. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/egc2012idiri-120801060538-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Les syst¨¨mes de surveillance maritime permettent la r¨¦cup¨¦ration et la fusion des informations sur les navires (position, vitesse, etc) ¨¤ des fins de suivi du trafic maritime sur un dispositif d¡¯affichage. Aujourd¡¯hui, l¡¯identification des risques ¨¤ partir de ces syst¨¨mes est difficilement automatisable compte-tenu de l¡¯expertise ¨¤ formaliser, du nombre important de navires et de la multiplicit¨¦ des risques (collision, ¨¦chouage, etc). De plus, le remplacement p¨¦riodique des op¨¦rateurs de surveillance complique la reconnaissance d¡¯¨¦v¨¦nements anormaux qui sont ¨¦parses et parcellaires dans le temps et l¡¯espace. Dans l¡¯objectif de faire ¨¦voluer ces syst¨¨mes de surveillance maritime, nous proposons dans cet article, une approche originale fond¨¦e sur le data mining pour l¡¯extraction de motifs fr¨¦quents. Cette approche se focalise sur des r¨¨gles de pr¨¦vision et de ciblage pour l¡¯identification automatique des situations induisant ou constituant le cadre des accidents maritimes.
from Bilal IDIRI
]]> 1043 7 https://cdn.slidesharecdn.com/ss_thumbnails/egc2012idiri-120801060538-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 D¨¦couverte de motifs fr¨¦quents pour la pr¨¦vision des accidents maritimes https://fr.slideshare.net/slideshow/aideegc2012-idiri/13825105 aideegc2012idiri-120801060426-phpapp01
Les syst¨¨mes de surveillance maritime permettent la r¨¦cup¨¦ration et la fusion des informations sur les navires (position, vitesse, etc) ¨¤ des fins de suivi du trafic maritime sur un dispositif d¡¯affichage. Aujourd¡¯hui, l¡¯identification des risques ¨¤ partir de ces syst¨¨mes est difficilement automatisable compte-tenu de l¡¯expertise ¨¤ formaliser, du nombre important de navires et de la multiplicit¨¦ des risques (collision, ¨¦chouage, etc). De plus, le remplacement p¨¦riodique des op¨¦rateurs de surveillance complique la reconnaissance d¡¯¨¦v¨¦nements anormaux qui sont ¨¦parses et parcellaires dans le temps et l¡¯espace. Dans l¡¯objectif de faire ¨¦voluer ces syst¨¨mes de surveillance maritime, nous proposons dans cet article, une approche originale fond¨¦e sur le data mining pour l¡¯extraction de motifs fr¨¦quents. Cette approche se focalise sur des r¨¨gles de pr¨¦vision et de ciblage pour l¡¯identification automatique des situations induisant ou constituant le cadre des accidents maritimes.]]>
Les syst¨¨mes de surveillance maritime permettent la r¨¦cup¨¦ration et la fusion des informations sur les navires (position, vitesse, etc) ¨¤ des fins de suivi du trafic maritime sur un dispositif d¡¯affichage. Aujourd¡¯hui, l¡¯identification des risques ¨¤ partir de ces syst¨¨mes est difficilement automatisable compte-tenu de l¡¯expertise ¨¤ formaliser, du nombre important de navires et de la multiplicit¨¦ des risques (collision, ¨¦chouage, etc). De plus, le remplacement p¨¦riodique des op¨¦rateurs de surveillance complique la reconnaissance d¡¯¨¦v¨¦nements anormaux qui sont ¨¦parses et parcellaires dans le temps et l¡¯espace. Dans l¡¯objectif de faire ¨¦voluer ces syst¨¨mes de surveillance maritime, nous proposons dans cet article, une approche originale fond¨¦e sur le data mining pour l¡¯extraction de motifs fr¨¦quents. Cette approche se focalise sur des r¨¨gles de pr¨¦vision et de ciblage pour l¡¯identification automatique des situations induisant ou constituant le cadre des accidents maritimes.]]> Wed, 01 Aug 2012 06:04:24 GMT https://fr.slideshare.net/slideshow/aideegc2012-idiri/13825105 bilelidiri@slideshare.net(bilelidiri) D¨¦couverte de motifs fr¨¦quents pour la pr¨¦vision des accidents maritimes bilelidiri Les syst¨¨mes de surveillance maritime permettent la r¨¦cup¨¦ration et la fusion des informations sur les navires (position, vitesse, etc) ¨¤ des fins de suivi du trafic maritime sur un dispositif d¡¯affichage. Aujourd¡¯hui, l¡¯identification des risques ¨¤ partir de ces syst¨¨mes est difficilement automatisable compte-tenu de l¡¯expertise ¨¤ formaliser, du nombre important de navires et de la multiplicit¨¦ des risques (collision, ¨¦chouage, etc). De plus, le remplacement p¨¦riodique des op¨¦rateurs de surveillance complique la reconnaissance d¡¯¨¦v¨¦nements anormaux qui sont ¨¦parses et parcellaires dans le temps et l¡¯espace. Dans l¡¯objectif de faire ¨¦voluer ces syst¨¨mes de surveillance maritime, nous proposons dans cet article, une approche originale fond¨¦e sur le data mining pour l¡¯extraction de motifs fr¨¦quents. Cette approche se focalise sur des r¨¨gles de pr¨¦vision et de ciblage pour l¡¯identification automatique des situations induisant ou constituant le cadre des accidents maritimes. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aideegc2012idiri-120801060426-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Les syst¨¨mes de surveillance maritime permettent la r¨¦cup¨¦ration et la fusion des informations sur les navires (position, vitesse, etc) ¨¤ des fins de suivi du trafic maritime sur un dispositif d¡¯affichage. Aujourd¡¯hui, l¡¯identification des risques ¨¤ partir de ces syst¨¨mes est difficilement automatisable compte-tenu de l¡¯expertise ¨¤ formaliser, du nombre important de navires et de la multiplicit¨¦ des risques (collision, ¨¦chouage, etc). De plus, le remplacement p¨¦riodique des op¨¦rateurs de surveillance complique la reconnaissance d¡¯¨¦v¨¦nements anormaux qui sont ¨¦parses et parcellaires dans le temps et l¡¯espace. Dans l¡¯objectif de faire ¨¦voluer ces syst¨¨mes de surveillance maritime, nous proposons dans cet article, une approche originale fond¨¦e sur le data mining pour l¡¯extraction de motifs fr¨¦quents. Cette approche se focalise sur des r¨¨gles de pr¨¦vision et de ciblage pour l¡¯identification automatique des situations induisant ou constituant le cadre des accidents maritimes.
from Bilal IDIRI
]]> 736 11 https://cdn.slidesharecdn.com/ss_thumbnails/aideegc2012idiri-120801060426-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-bilelidiri-48x48.jpg?cb=1576089018 www.mines-paristech.fr/Services/Annuaire/bilal-idiri https://cdn.slidesharecdn.com/ss_thumbnails/thegeographicaldecision-makingchain-130604051700-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/the-geographical-decision-making-chain/22426606 The geographical decis... https://cdn.slidesharecdn.com/ss_thumbnails/sose2012idiri-120801074534-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/sose2012-idiri-13826022/13826022 The automatic identifi... https://cdn.slidesharecdn.com/ss_thumbnails/egc2012idiri-120801060538-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/egc2012-idiri/13825114 D¨¦couverte de r¨¨gles d...