short-paper

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

Authors:

Patrick Wiener,

Daniel Seebacher,

Matthias Frank,

Jens NimisAuthors Info & Claims

SIGSPACIAL '16: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Article No.: 8, Pages 1 - 4

https://doi.org/10.1145/2996913.2996931

Published: 31 October 2016 Publication History

Abstract

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.

References

[1]

N. Cressie and C. K. Wikle. Statistics for Spatio-Temporal Data. John Wiley & Sons, 2015.

[2]

M. D. Crossland, B. E. Wynne, and W. C. Perkins. Spatial Decision Support Systems: An Overview of Technology and a Test of Efficacy. Decis. Support Syst., 14(3):219--235, July 1995.

Digital Library

[3]

M. Frank. Integrating Big Spatio-Temporal Data Using Collaborative Semantic Data Management. In 16th Int. Conf. on Web Engineering (ICWE 2016).

[4]

M. Frank and S. Zander. Smart Web Services for Big Spatio-Temporal Data in Geographical Information Systems. In ESWC workshop: Services and Applications over Linked APIs and Data (SALAD 2016).

[5]

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2 edition, 2009.

[6]

D. Jäckle, H. Senaratne, J. Buchmüller, and D. A. Keim. Integrated Spatial Uncertainty Visualization using Off-screen Aggregation. In E. Bertini and J. C. Roberts, editors, EuroVis Workshop on Visual Analytics (EuroVA). The Eurographics Association, 2015.

[7]

D. A. Keim, G. Andrienko, J.-D. Fekete, C. Görg, J. Kohlhammer, and G. Melançon. Information Visualization: Human-Centered Issues and Perspectives, chapter Visual Analytics: Definition, Process, and Challenges, pages 154--175. Springer, 2008.

[8]

D. A. Keim, J. Kohlhammer, G. Ellis, and F. Mansmann. Mastering the Information Age: Solving Problems with Visual Analytics. The Eurographics Association, 2010.

[9]

J. Kreps. Questioning the Lambda Architecture. https://www.oreilly.com/ideas/questioning-the-lambda-architecture, July 2014. Accessed: 10 May 2016.

[10]

N. Marz and J. Warren. Big Data: Principles and Best Practices of Scalable Realtime Data Systems. Manning Publications, 2013.

Digital Library

[11]

OGC. Big Processing of Geospatial Data. http://www.opengeospatial.org/blog/1866, August 2013. Accessed: 10 May 2016.

[12]

Y. Peng and J. Liangcun. BigGIS: How big data can shape next-generation GIS. In 3rd Int. Conf. on Agro-Geoinformatics (Agro-Geoinformatics 2014), pages 1--6. IEEE, August 2014.

[13]

D. Sacha, H. Senaratne, B. C. Kwon, G. Ellis, and D. A. Keim. The Role of Uncertainty, Awareness, and Trust in Visual Analytics. IEEE Transactions on Visualization and Computer Graphics, 22(01):240--249, Jan 2016.

[14]

D. Sacha, A. Stoffel, F. Stoffel, B. C. Kwon, G. Ellis, and D. A. Keim. Knowledge Generation Model for Visual Analytics. IEEE Transactions on Visualization and Computer Graphics, 20(12):1604--1613, December 2014.

[15]

V. Tanasescu, A. Gugliotta, J. Domingue, R. Davies, L. Gutiérrez-Villarías, M. Rowlatt, M. Richardson, and S. Stinčić. A Semantic Web Services GIS Based Emergency Management Application. In Proc. of the 5th Int. Conf. on The Semantic Web, pages 959--966. Springer, 2006.

Digital Library

[16]

G. S. Thakur, B. L. Bhaduri, J. O. Piburn, K. M. Sims, R. N. Stewart, and M. L. Urban. PlanetSense: A Real-time Streaming and Spatio-temporal Analytics Platform for Gathering Geo-spatial Intelligence from Open Source Data. In Proc. of the 23rd SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems, pages 11:1--11:4. ACM, 2015.

Digital Library

Cited By

Mukherjee SGupta SRawlley OJain S(2022)Leveraging big data analytics in 5G‐enabled IoT and industrial IoT for the development of sustainable smart citiesTransactions on Emerging Telecommunications Technologies10.1002/ett.461833:12Online publication date: 30-Jul-2022
https://doi.org/10.1002/ett.4618
Follin JGirres JOlteanu‐Raimond ASheeren D(2019)The Origins of Imperfection in Geographic DataGeographic Data Imperfection 110.1002/9781119507284.ch3(25-44)Online publication date: 5-Aug-2019
https://doi.org/10.1002/9781119507284.ch3
Frank MSimko V(2018)Semantic Data Stream Mapping and Shape Constraint Validation Based on Collaboratively Created AnnotationsWeb Engineering10.1007/978-3-319-91662-0_26(321-329)Online publication date: 20-May-2018
https://doi.org/10.1007/978-3-319-91662-0_26

Index Terms

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

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Information

Published In

cover image ACM Other conferences

SIGSPACIAL '16: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

October 2016

649 pages

ISBN:9781450345897

DOI:10.1145/2996913

General Chairs:
Mohamed Ali
University of Washington, Tacoma
,
Shawn Newsam
University of California, Merced
,
Program Chairs:
Matthias Renz
George Mason University, USA
,
Goce Trajcevski
Northwestern University, USA
,
Siva Ravada
Oracle Corporation, USA

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 October 2016

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Bundesministerium für Bildung und Forschung

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SIGSPATIAL'16

SIGSPATIAL'16: 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

October 31 - November 3, 2016

California, Burlingame

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SIGSPACIAL '16 Paper Acceptance Rate 40 of 216 submissions, 19%;

Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

Mukherjee SGupta SRawlley OJain S(2022)Leveraging big data analytics in 5G‐enabled IoT and industrial IoT for the development of sustainable smart citiesTransactions on Emerging Telecommunications Technologies10.1002/ett.461833:12Online publication date: 30-Jul-2022
https://doi.org/10.1002/ett.4618
Follin JGirres JOlteanu‐Raimond ASheeren D(2019)The Origins of Imperfection in Geographic DataGeographic Data Imperfection 110.1002/9781119507284.ch3(25-44)Online publication date: 5-Aug-2019
https://doi.org/10.1002/9781119507284.ch3
Frank MSimko V(2018)Semantic Data Stream Mapping and Shape Constraint Validation Based on Collaboratively Created AnnotationsWeb Engineering10.1007/978-3-319-91662-0_26(321-329)Online publication date: 20-May-2018
https://doi.org/10.1007/978-3-319-91662-0_26

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