Article | Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016 | Interpolating Lost Spatio-Temporal Data by Web Sensors Linköping University Electronic Press Conference Proceedings
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Title:
Interpolating Lost Spatio-Temporal Data by Web Sensors
Author:
Shun Hattori: Web Intelligence Time-Space (WITS) Laboratory, College of Information and Systems, Graduate School of Engineering, Muroran Institute of Technology, 27–1 Mizumoto-cho, Muroran, Hokkaido 050–8585, Japan
DOI:
10.3384/ecp171421048
Download:
Full text (pdf)
Year:
2018
Conference:
Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016
Issue:
142
Article no.:
154
Pages:
1048-1052
No. of pages:
5
Publication type:
Abstract and Fulltext
Published:
2018-12-19
ISBN:
978-91-7685-399-3
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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We experience various phenomena (e.g., rain, snow, and earthquake) in the physical world, while we carry out various actions (e.g., posting, querying, and e-shopping) in the Web world. Many researches have tried to mine the Web for knowledge about various phenomena in the physical world, and also several Web services using Web mined knowledge have been made available for the public. Meanwhile, the previous papers have introduced various kinds of “Web Sensors” with Temporal Shift, Temporal Propagation, and Geospatial Propagation to sense the Web for knowledge about a targeted physical phenomenon, i.e., to extract its spatiotemporal data sensitively by analyzing big data on the Web (e.g., Web documents, Web query logs, and e-shopping logs), and compared them based on their correlation coef?cients with Japan Meteorological Agency’s physically-sensed spatiotemporal statistics to ensure the accuracy of Web-sensed spatiotemporal data suf?ciently. As an industrial application of Web Sensors to a problem of the loss or error of physically-sensed spatiotemporal data due to some sort of troubles (e.g., temporary faults of JMA’s observatories), this paper tries to enable Web Sensors to interpolate lost spatiotemporal data of physical statistics by regression analysis.

Keywords: spatiotemporal data mining, big data analysis, web sensors, regression analysis

Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Author:
Shun Hattori
Title:
Interpolating Lost Spatio-Temporal Data by Web Sensors
DOI:
http://dx.doi.org/10.3384/ecp171421048
References:
No references available

Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Author:
Shun Hattori
Title:
Interpolating Lost Spatio-Temporal Data by Web Sensors
DOI:
https://doi.org10.3384/ecp171421048
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