Getting massive data into MELODA is not that easy as I thought.
First I tried with portals based on CKAN. Because is the most common portal for open data in Smart cities. It is easy to find portals because here there is a list of CKAN instances. Of course it is not a complete list but it works.
However Opendata Caceres works fine and this file is the result. Because you recover all the formats (rdf, csv, xls, etc) I have decided not to use all but csv, which it seems could be the most common.
Then I created a libreoffice spreadsheet to transform the data from the csv file into a set of SQL sentences in order to get this into the main database.
So you can see the pending datasets in this link Meloda->Smart cities -> Assess a registered dataset
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
# Authors : J. Félix Ontañón <email@example.com>
#CKAN_URI = “demo.ckan.org”
CKAN_URI = “datosabiertos.malaga.eu”
PACKAGE_LIST_REQ = “http://”+CKAN_URI+”/api/3/action/package_list”
PACKAGE_SHOW_REQ_BASE = “http://”+CKAN_URI+”/api/3/action/package_show?id=”
PACKAGE_ENTRY_URL_BASE = “http://”+CKAN_URI+”/dataset/”
dataset_catalogue = json.load(urllib2.urlopen(PACKAGE_LIST_REQ))
print “dataset_name, dataset_entry, resource_format, resource_url”
for dataset_name in dataset_catalogue[“result”]:
dataset = json.load(urllib2.urlopen(PACKAGE_SHOW_REQ_BASE + dataset_name))
for resource in dataset[“result”][“resources”]:
print dataset_name, ‘,’, PACKAGE_ENTRY_URL_BASE+dataset_name, ‘,’, resource[“format”], ‘,’, resource[“url”]