{"id":2616,"date":"2011-11-17T10:37:38","date_gmt":"2011-11-17T09:37:38","guid":{"rendered":"http:\/\/dbdmg.polito.it\/wordpress\/?page_id=2616"},"modified":"2012-02-01T15:30:39","modified_gmt":"2012-02-01T14:30:39","slug":"geodatabase-and-spatial-data-mining","status":"publish","type":"page","link":"https:\/\/dbdmg.polito.it\/wordpress\/theses\/geodatabase-and-spatial-data-mining\/","title":{"rendered":"Geodatabase and spatial data mining"},"content":{"rendered":"<h3>Spatial data management<\/h3>\n<h4>Tutor<\/h4>\n<a href=\"https:\/\/dbdmg.polito.it\/wordpress\/people\/giulia-bruno\/\">Giulia Bruno<\/a>\n<h4>Description<\/h4>\n<p>Spatial data mining is the process of discovering interesting and useful patterns from large spatial datasets.<\/p>\n<p><em>Challenge<\/em><\/p>\n<ul>\n<li>The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) is leading to the collection of large spatio-temporal datasets and to the opportunity of discovering usable knowledge about movement behaviour, which fosters novel applications and services.<\/li>\n<li>Extracting patterns from spatial datasets is more difficult than extracting patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatial autocorrelation.<\/li>\n<\/ul>\n<p><em>Activity<\/em><\/p>\n<ul>\n<li>Study of trajectory pattern mining methods.<\/li>\n<li>Define an algorithm to extract frequent trajectories from a GPS log dataset to detect the most followed sequences of points of interest.<\/li>\n<\/ul>\n<div>\n<p><em><br \/>\n<\/em><\/p>\n<\/div>\n<p><!--nextpage--><\/p>\n<h3>Analysis of Public Transport data<a href=\"http:\/\/dbdmg.polito.it\/wordpress\/wp-content\/uploads\/2011\/11\/5t-e1321535427549.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignright size-full wp-image-2656\" title=\"5t\" src=\"http:\/\/dbdmg.polito.it\/wordpress\/wp-content\/uploads\/2011\/11\/5t-e1321535427549.png\" alt=\"\" width=\"150\" height=\"63\" \/><\/a><\/h3>\n<h4>Tutors<\/h4>\n<p><a href=\"https:\/\/dbdmg.polito.it\/wordpress\/people\/elena-baralis\/\">Elena Baralis<\/a>,\u00a0<a href=\"https:\/\/dbdmg.polito.it\/wordpress\/people\/alessandro-fiori\/\">Alessandro Fiori<\/a><\/p>\n<h4>Description<\/h4>\n<p><em>Data<a href=\"http:\/\/dbdmg.polito.it\/wordpress\/wp-content\/uploads\/2011\/11\/transport_data1.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"alignright size-full wp-image-2658\" title=\"transport_data1\" src=\"http:\/\/dbdmg.polito.it\/wordpress\/wp-content\/uploads\/2011\/11\/transport_data1-e1321535557291.jpg\" alt=\"\" width=\"150\" height=\"186\" \/><\/a><\/em><\/p>\n<ul>\n<li>Data of Turin&#8217;s Public\u00a0Transportation System<\/li>\n<\/ul>\n<p><em>Challenge<\/em><\/p>\n<ul>\n<li>Improve accuracy of vehicle travel time prediction<\/li>\n<li>Optimize stability of the forecast<\/li>\n<\/ul>\n<p><em>Activity<\/em><\/p>\n<ul>\n<li>Study of forecasting methods for buses travel time<\/li>\n<\/ul>\n<p><em>Practical applications<a href=\"http:\/\/dbdmg.polito.it\/wordpress\/wp-content\/uploads\/2011\/11\/transport_data2.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"alignright size-full wp-image-2659\" title=\"transport_data2\" src=\"http:\/\/dbdmg.polito.it\/wordpress\/wp-content\/uploads\/2011\/11\/transport_data2-e1321535587916.jpg\" alt=\"\" width=\"149\" height=\"100\" \/><\/a><\/em><\/p>\n<ul>\n<li>Arrival time prediction at bus stop<\/li>\n<li>Calculation of instantaneous headway<\/li>\n<li>Real time Journey Planner<\/li>\n<\/ul>\n<h4>External stage<\/h4>\n<div>\n<ul>\n<li>code:\u00a08452<\/li>\n<li>denomination: Sistemi previsionali dedicati al trasporto Pubblico<\/li>\n<\/ul>\n<\/div>\n<h3><!--nextpage--><\/h3>\n<h3>Analysis of Traffic Sensor data<\/h3>\n<h3><a href=\"http:\/\/dbdmg.polito.it\/wordpress\/wp-content\/uploads\/2011\/11\/5t-e1321535427549.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignright size-full wp-image-2656\" title=\"5t\" src=\"http:\/\/dbdmg.polito.it\/wordpress\/wp-content\/uploads\/2011\/11\/5t-e1321535427549.png\" alt=\"\" width=\"150\" height=\"63\" \/><\/a><\/h3>\n<h4>Tutors<\/h4>\n<p><a href=\"https:\/\/dbdmg.polito.it\/wordpress\/people\/elena-baralis\/\">Elena Baralis<\/a>,\u00a0<a href=\"https:\/\/dbdmg.polito.it\/wordpress\/people\/alessandro-fiori\/\">Alessandro Fiori<\/a><\/p>\n<h4>Description<\/h4>\n<p><em>Data<a href=\"http:\/\/dbdmg.polito.it\/wordpress\/wp-content\/uploads\/2011\/11\/traffic_sensor1.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"alignright size-full wp-image-2660\" title=\"traffic_sensor1\" src=\"http:\/\/dbdmg.polito.it\/wordpress\/wp-content\/uploads\/2011\/11\/traffic_sensor1-e1321535711985.jpg\" alt=\"\" width=\"200\" height=\"143\" \/><\/a><\/em><\/p>\n<ul>\n<li>Data of traffic control sensor network (5T System)<\/li>\n<\/ul>\n<p><em>Challenge<\/em><\/p>\n<ul>\n<li>Recognize correlations between sensors (data)<\/li>\n<li>Detect sensors failures<\/li>\n<\/ul>\n<p><em>Activity<\/em><\/p>\n<ul>\n<li>Study of methods for verification of data quality<\/li>\n<li>Study of Adaptive Clustering\u00a0Methods for data alterations<\/li>\n<\/ul>\n<p><em>Practical applications<a href=\"http:\/\/dbdmg.polito.it\/wordpress\/wp-content\/uploads\/2011\/11\/traffic_sensor2.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"alignright size-full wp-image-2661\" title=\"traffic_sensor2\" src=\"http:\/\/dbdmg.polito.it\/wordpress\/wp-content\/uploads\/2011\/11\/traffic_sensor2-e1321535744632.jpg\" alt=\"\" width=\"250\" height=\"136\" \/><\/a><\/em><\/p>\n<ul>\n<li>Optimize sensors usage<\/li>\n<\/ul>\n<h4>External stage<\/h4>\n<div>\n<ul>\n<li>code:\u00a08452<\/li>\n<li>denomination: Sistemi previsionali dedicati al trasporto Pubblico<\/li>\n<\/ul>\n<\/div>\n<br class=\"fixfloat\" \/>","protected":false},"excerpt":{"rendered":"<p>Spatial data management Tutor Description Spatial data mining is the process of discovering interesting and useful patterns from large spatial datasets. Challenge The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) is leading to the collection of large spatio-temporal datasets and to the opportunity of discovering usable knowledge about movement behaviour, which fosters novel<a href=\"https:\/\/dbdmg.polito.it\/wordpress\/theses\/geodatabase-and-spatial-data-mining\/\">[&#8230;]<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"parent":2369,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-2616","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/pages\/2616","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/comments?post=2616"}],"version-history":[{"count":16,"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/pages\/2616\/revisions"}],"predecessor-version":[{"id":7993,"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/pages\/2616\/revisions\/7993"}],"up":[{"embeddable":true,"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/pages\/2369"}],"wp:attachment":[{"href":"https:\/\/dbdmg.polito.it\/wordpress\/wp-json\/wp\/v2\/media?parent=2616"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}