Open City Data Pipeline: Collecting, Integrating, and Predicting Open City Data. Bischof, S., Martin, C., Polleres, A., & Schneider, P. In 4th Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data (Know@LOD), Portoroz, Slovenia, May, 2015. ![Open City Data Pipeline: Collecting, Integrating, and Predicting Open City Data [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex Having access to high quality and recent data is crucial both for decision makers in cities as well as for informing the public; likewise, infrastructure providers could offer more tailored solutions to cities based on such data. However, even though there are many data sets containing relevant indicators about cities available as open data, it is cumbersome to integrate and analyze them, since the collection is still a manual process and the sources are not connected to each other upfront. Further, disjoint indicators and cities across the available data sources lead to a large proportion of missing values when integrating these sources. In the present paper we present a platform for collecting, integrating, and enriching open data about cities in a re-usable and comparable manner: we have integrated various open data sources and present approaches for predicting missing values: we use different standard regression methods in combination with principal component analysis to improve quality and amount of predicted values. Further, we re-publish the integrated and predicted values as linked open data.
@inproceedings{bisc-etal-2015KnowLOD,
author = {Stefan Bischof and Christoph Martin and Axel Polleres and Patrik Schneider},
booktitle = {4th Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data (Know@LOD)},
title = {{Open City Data Pipeline: Collecting, Integrating, and Predicting Open City Data}},
abstract = {Having access to high quality and recent data is crucial both for decision makers in cities as well as for informing the public; likewise, infrastructure providers could offer more tailored solutions to cities based on such data. However, even though there are many data sets containing relevant indicators about cities available as open data, it is cumbersome to integrate and analyze them, since the collection is still a manual process and the sources are not connected to each other upfront. Further, disjoint indicators and cities across the available data sources lead to a large proportion of missing values when integrating these sources. In the present paper we present a platform for collecting, integrating, and enriching open data about cities in a re-usable and comparable manner: we have integrated various open data sources and present approaches for predicting missing values: we use different standard regression methods in combination with principal component analysis to improve quality and amount of predicted values. Further, we re-publish the integrated and predicted values as linked open data.},
year = 2015,
month = may,
day = {31},
address = {Portoroz, Slovenia},
url = {http://www.polleres.net/publications/bisc-etal-2015KnowLOD.pdf}
}
Downloads: 0
{"_id":"DbwxGzwGRwcG7W2e4","bibbaseid":"bischof-martin-polleres-schneider-opencitydatapipelinecollectingintegratingandpredictingopencitydata-2015","downloads":0,"creationDate":"2015-12-08T08:58:02.554Z","title":"Open City Data Pipeline: Collecting, Integrating, and Predicting Open City Data","author_short":["Bischof, S.","Martin, C.","Polleres, A.","Schneider, P."],"year":2015,"bibtype":"inproceedings","biburl":"www.polleres.net/mypublications.bib","bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Stefan"],"propositions":[],"lastnames":["Bischof"],"suffixes":[]},{"firstnames":["Christoph"],"propositions":[],"lastnames":["Martin"],"suffixes":[]},{"firstnames":["Axel"],"propositions":[],"lastnames":["Polleres"],"suffixes":[]},{"firstnames":["Patrik"],"propositions":[],"lastnames":["Schneider"],"suffixes":[]}],"booktitle":"4th Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data (Know@LOD)","title":"Open City Data Pipeline: Collecting, Integrating, and Predicting Open City Data","abstract":"Having access to high quality and recent data is crucial both for decision makers in cities as well as for informing the public; likewise, infrastructure providers could offer more tailored solutions to cities based on such data. However, even though there are many data sets containing relevant indicators about cities available as open data, it is cumbersome to integrate and analyze them, since the collection is still a manual process and the sources are not connected to each other upfront. Further, disjoint indicators and cities across the available data sources lead to a large proportion of missing values when integrating these sources. In the present paper we present a platform for collecting, integrating, and enriching open data about cities in a re-usable and comparable manner: we have integrated various open data sources and present approaches for predicting missing values: we use different standard regression methods in combination with principal component analysis to improve quality and amount of predicted values. Further, we re-publish the integrated and predicted values as linked open data.","year":"2015","month":"May","day":"31","address":"Portoroz, Slovenia","url":"http://www.polleres.net/publications/bisc-etal-2015KnowLOD.pdf","bibtex":"@inproceedings{bisc-etal-2015KnowLOD,\n author = {Stefan Bischof and Christoph Martin and Axel Polleres and Patrik Schneider},\n booktitle = {4th Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data (Know@LOD)},\n title = {{Open City Data Pipeline: Collecting, Integrating, and Predicting Open City Data}},\n abstract = {Having access to high quality and recent data is crucial both for decision makers in cities as well as for informing the public; likewise, infrastructure providers could offer more tailored solutions to cities based on such data. However, even though there are many data sets containing relevant indicators about cities available as open data, it is cumbersome to integrate and analyze them, since the collection is still a manual process and the sources are not connected to each other upfront. Further, disjoint indicators and cities across the available data sources lead to a large proportion of missing values when integrating these sources. In the present paper we present a platform for collecting, integrating, and enriching open data about cities in a re-usable and comparable manner: we have integrated various open data sources and present approaches for predicting missing values: we use different standard regression methods in combination with principal component analysis to improve quality and amount of predicted values. Further, we re-publish the integrated and predicted values as linked open data.},\nyear = 2015,\nmonth = may,\nday = {31},\naddress = {Portoroz, Slovenia},\nurl = {http://www.polleres.net/publications/bisc-etal-2015KnowLOD.pdf}\n}\n\n\n","author_short":["Bischof, S.","Martin, C.","Polleres, A.","Schneider, P."],"key":"bisc-etal-2015KnowLOD","id":"bisc-etal-2015KnowLOD","bibbaseid":"bischof-martin-polleres-schneider-opencitydatapipelinecollectingintegratingandpredictingopencitydata-2015","role":"author","urls":{"Paper":"http://www.polleres.net/publications/bisc-etal-2015KnowLOD.pdf"},"metadata":{"authorlinks":{"polleres, a":"https://bibbase.org/show?bib=www.polleres.net/mypublications.bib"}},"downloads":0,"html":""},"search_terms":["open","city","data","pipeline","collecting","integrating","predicting","open","city","data","bischof","martin","polleres","schneider"],"keywords":[],"authorIDs":["FyLDFGg993nDS2Spf"],"dataSources":["cBfwyqsLFQQMc4Fss","gixxkiKt6rtWGoKSh","QfLT6siHZuHw9MqvK"]}