Open City Data Pipeline: Collecting, Integrating, and Predicting Open City Data

Stefan Bischof; Christoph Martin; Axel Polleres; Patrik Schneider

Open City Data Pipeline: Collecting, Integrating, and Predicting Open City Data

Stefan Bischof
, Christoph Martin
, Axel Polleres
, Patrik Schneider

Data, Process and Knowledge Management (Polleres)

Publikation: Beitrag in Buch/Konferenzband › Beitrag in Konferenzband

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 this 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, where we use 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.

Originalsprache	Englisch
Titel des Sammelwerks	Proceedings of the 4th Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data co-located with 12th Extended Semantic Web Conference (ESWC'15)
Herausgeber*innen	Johanna Völker, Heiko Paulheim, Jens Lehmann and Vojtech Svatek
Erscheinungsort	Portoroz, Slovenia
Verlag	CEUR Workshop Proceedings
Publikationsstatus	Veröffentlicht - 1 Juni 2015

Andere Dateien und Links

http://ceur-ws.org/Vol-1365/paper3.pdf

SPARQL Evaluations and Extensions (kurz: SEE): Subprojekt XSPARQL, SPARQL Update & Linked Data
Polleres, A. (Projektleitung), Bischof, S. (Forscher*in) & Steyskal, S. (Forscher*in)
1/06/14 → 31/08/15
Projekt: Forschungsförderung
Open City Data Pipeline - Studie zur Sammlung offener Stadtdaten
Polleres, A. (Projektleitung) & Martin, C. (Forscher*in)
1/04/14 → 31/03/15
Projekt: Forschungsförderung

Zitat

Bischof, S., Martin, C., Polleres, A., & Schneider, P. (2015). Open City Data Pipeline: Collecting, Integrating, and Predicting Open City Data. in Johanna Völker, Heiko Paulheim, Jens Lehmann and Vojtech Svatek (Hrsg.), Proceedings of the 4th Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data co-located with 12th Extended Semantic Web Conference (ESWC'15) CEUR Workshop Proceedings.

Bischof, Stefan ; Martin, Christoph ; Polleres, Axel et al. / Open City Data Pipeline: Collecting, Integrating, and Predicting Open City Data. Proceedings of the 4th Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data co-located with 12th Extended Semantic Web Conference (ESWC'15). Hrsg. / Johanna Völker, Heiko Paulheim, Jens Lehmann and Vojtech Svatek. Portoroz, Slovenia : CEUR Workshop Proceedings, 2015.

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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 this 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, where we use 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.",

author = "Stefan Bischof and Christoph Martin and Axel Polleres and Patrik Schneider",

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Bischof, S, Martin, C, Polleres, A & Schneider, P 2015, Open City Data Pipeline: Collecting, Integrating, and Predicting Open City Data. in Johanna Völker, Heiko Paulheim, Jens Lehmann and Vojtech Svatek (Hrsg.), Proceedings of the 4th Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data co-located with 12th Extended Semantic Web Conference (ESWC'15). CEUR Workshop Proceedings, Portoroz, Slovenia.

Open City Data Pipeline: Collecting, Integrating, and Predicting Open City Data. / Bischof, Stefan; Martin, Christoph; Polleres, Axel et al.
Proceedings of the 4th Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data co-located with 12th Extended Semantic Web Conference (ESWC'15). Hrsg. / Johanna Völker, Heiko Paulheim, Jens Lehmann and Vojtech Svatek. Portoroz, Slovenia: CEUR Workshop Proceedings, 2015.

Publikation: Beitrag in Buch/Konferenzband › Beitrag in Konferenzband

TY - GEN

T1 - Open City Data Pipeline: Collecting, Integrating, and Predicting Open City Data

AU - Bischof, Stefan

AU - Martin, Christoph

AU - Polleres, Axel

AU - Schneider, Patrik

PY - 2015/6/1

Y1 - 2015/6/1

N2 - 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 this 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, where we use 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.

AB - 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 this 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, where we use 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.

UR - http://ceur-ws.org/Vol-1365/paper3.pdf

M3 - Contribution to conference proceedings

BT - Proceedings of the 4th Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data co-located with 12th Extended Semantic Web Conference (ESWC'15)

A2 - Johanna Völker, Heiko Paulheim, Jens Lehmann and Vojtech Svatek, null

PB - CEUR Workshop Proceedings

CY - Portoroz, Slovenia

ER -

Bischof S, Martin C, Polleres A, Schneider P. Open City Data Pipeline: Collecting, Integrating, and Predicting Open City Data. in Johanna Völker, Heiko Paulheim, Jens Lehmann and Vojtech Svatek, Hrsg., Proceedings of the 4th Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data co-located with 12th Extended Semantic Web Conference (ESWC'15). Portoroz, Slovenia: CEUR Workshop Proceedings. 2015

Open City Data Pipeline: Collecting, Integrating, and Predicting Open City Data

Abstract

Andere Dateien und Links

Projekte

SPARQL Evaluations and Extensions (kurz: SEE): Subprojekt XSPARQL, SPARQL Update & Linked Data

Open City Data Pipeline - Studie zur Sammlung offener Stadtdaten

Zitat