BigGIS : a continuous refinement approach to master heterogeneity and uncertainty in spatio-temporal big data (vision paper)

Wiener, Patrick; Stein, Manuel; Seebacher, Daniel; Bruns, Julian; Frank, Matthias; Simko, Viliam; Zander, Stefan; Nimis, Jens

BigGIS : a continuous refinement approach to master heterogeneity and uncertainty in spatio-temporal big data (vision paper)

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2016

Autor:innen

Wiener, Patrick

Stein, Manuel

Seebacher, Daniel

Bruns, Julian

Frank, Matthias

Simko, Viliam

Zander, Stefan

Nimis, Jens

DOI (zitierfähiger Link)

https://doi.org/10.1145/2996913.2996931

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

ALI, Mohamed, ed., Shawn NEWSAM, ed.. GIS '16 : Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '16. New York: ACM Press, 2016, 8. ISBN 978-1-4503-4589-7. Available under: doi: 10.1145/2996913.2996931

Zusammenfassung

Geographic information systems (GIS) are important for decision support based on spatial data. Due to technical and economical progress an ever increasing number of data sources are available leading to a rapidly growing fast and unreliable amount of data that can be beneficial (1) in the approximation of multivariate and causal predictions of future values as well as (2) in robust and proactive decision-making processes. However, today's GIS are not designed for such big data demands and require new methodologies to effectively model uncertainty and generate meaningful knowledge. As a consequence, we introduce BigGIS, a predictive and prescriptive spatio-temporal analytics platform, that symbiotically combines big data analytics, semantic web technologies and visual analytics methodologies. We present a novel continuous refinement model and show future challenges as an intermediate result of a collaborative research project into big data methodologies for spatio-temporal analysis and design for a big data enabled GIS.

Fachgebiet (DDC)

004 Informatik

Schlagwörter

Knowledge Generation, Big Data Analytics, Data Architecture

Konferenz

24th ACM SIGSPATIAL International Conference, 31. Okt. 2016 - 3. Nov. 2016, Burlingame, California

Zitieren

ISO 690

WIENER, Patrick, Manuel STEIN, Daniel SEEBACHER, Julian BRUNS, Matthias FRANK, Viliam SIMKO, Stefan ZANDER, Jens NIMIS, 2016. BigGIS : a continuous refinement approach to master heterogeneity and uncertainty in spatio-temporal big data (vision paper). 24th ACM SIGSPATIAL International Conference. Burlingame, California, 31. Okt. 2016 - 3. Nov. 2016. In: ALI, Mohamed, ed., Shawn NEWSAM, ed.. GIS '16 : Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '16. New York: ACM Press, 2016, 8. ISBN 978-1-4503-4589-7. Available under: doi: 10.1145/2996913.2996931

BibTex

@inproceedings{Wiener2016BigGI-36921,
  year={2016},
  doi={10.1145/2996913.2996931},
  title={BigGIS : a continuous refinement approach to master heterogeneity and uncertainty in spatio-temporal big data (vision paper)},
  isbn={978-1-4503-4589-7},
  publisher={ACM Press},
  address={New York},
  booktitle={GIS '16 : Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems  - GIS '16},
  editor={Ali, Mohamed and Newsam, Shawn},
  author={Wiener, Patrick and Stein, Manuel and Seebacher, Daniel and Bruns, Julian and Frank, Matthias and Simko, Viliam and Zander, Stefan and Nimis, Jens},
  note={Article Number: 8}
}

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Universitätsbibliographie

Ja