Moving phenomenon: Aggregation and analysis of geotime-tagged contents on the Web

Kyoung Sook Kim, Koji Zettsu, Yutaka Kidawara, Yasushi Kiyoki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

The analysis of movement of people, vehicles, and other objects is important for carrying out research in social and scientific domains. The study of movement behavior of spatiotemporal entities helps enhance the quality of service in decision-making in real applications. However, the spread of certain entities such as diseases or rumor is difficult to observe compared to the movement of people, vehicles, or animals. We can only infer their locations in a certain region of space-time on the basis of observable events. In this paper, we propose a new model, called as moving phenomenon, to represent time-varying phenomena over geotime-tagged contents on the Web. The most important feature of this model is the integration of thematic dimension into an event-based spatiotemporal data model. By using the proposed model, a user can aggregate relevant contents relating to an interesting phenomenon and perceive its movement behavior; further, the model also enables a user to navigate the spatial, temporal, and thematic information of the contents along all the three-dimensions. Finally, we present an example of typhoons to illustrate moving phenomena and draw a comparison between the movement of the moving phenomenon created using information from news articles on the Web and that of the actual typhoon.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages7-24
Number of pages18
Volume5886 LNCS
DOIs
Publication statusPublished - 2009
Event9th International Symposium on Web and Wireless Geographical Information Systems, W2GIS 2009 - Maynooth, Ireland
Duration: 2009 Dec 72009 Dec 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5886 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other9th International Symposium on Web and Wireless Geographical Information Systems, W2GIS 2009
CountryIreland
CityMaynooth
Period09/12/709/12/8

Fingerprint

Aggregation
Agglomeration
Typhoon
Spatio-temporal Model
Spatio-temporal Data
Data structures
Quality of service
Animals
Model
Decision making
Data Model
Quality of Service
Three-dimension
Time-varying
Decision Making
Space-time
Movement

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Kim, K. S., Zettsu, K., Kidawara, Y., & Kiyoki, Y. (2009). Moving phenomenon: Aggregation and analysis of geotime-tagged contents on the Web. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5886 LNCS, pp. 7-24). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5886 LNCS). https://doi.org/10.1007/978-3-642-10601-9_2

Moving phenomenon : Aggregation and analysis of geotime-tagged contents on the Web. / Kim, Kyoung Sook; Zettsu, Koji; Kidawara, Yutaka; Kiyoki, Yasushi.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5886 LNCS 2009. p. 7-24 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5886 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kim, KS, Zettsu, K, Kidawara, Y & Kiyoki, Y 2009, Moving phenomenon: Aggregation and analysis of geotime-tagged contents on the Web. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 5886 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5886 LNCS, pp. 7-24, 9th International Symposium on Web and Wireless Geographical Information Systems, W2GIS 2009, Maynooth, Ireland, 09/12/7. https://doi.org/10.1007/978-3-642-10601-9_2
Kim KS, Zettsu K, Kidawara Y, Kiyoki Y. Moving phenomenon: Aggregation and analysis of geotime-tagged contents on the Web. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5886 LNCS. 2009. p. 7-24. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-10601-9_2
Kim, Kyoung Sook ; Zettsu, Koji ; Kidawara, Yutaka ; Kiyoki, Yasushi. / Moving phenomenon : Aggregation and analysis of geotime-tagged contents on the Web. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5886 LNCS 2009. pp. 7-24 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{cb59a4ef927d49f4b688e962bb339ec1,
title = "Moving phenomenon: Aggregation and analysis of geotime-tagged contents on the Web",
abstract = "The analysis of movement of people, vehicles, and other objects is important for carrying out research in social and scientific domains. The study of movement behavior of spatiotemporal entities helps enhance the quality of service in decision-making in real applications. However, the spread of certain entities such as diseases or rumor is difficult to observe compared to the movement of people, vehicles, or animals. We can only infer their locations in a certain region of space-time on the basis of observable events. In this paper, we propose a new model, called as moving phenomenon, to represent time-varying phenomena over geotime-tagged contents on the Web. The most important feature of this model is the integration of thematic dimension into an event-based spatiotemporal data model. By using the proposed model, a user can aggregate relevant contents relating to an interesting phenomenon and perceive its movement behavior; further, the model also enables a user to navigate the spatial, temporal, and thematic information of the contents along all the three-dimensions. Finally, we present an example of typhoons to illustrate moving phenomena and draw a comparison between the movement of the moving phenomenon created using information from news articles on the Web and that of the actual typhoon.",
author = "Kim, {Kyoung Sook} and Koji Zettsu and Yutaka Kidawara and Yasushi Kiyoki",
year = "2009",
doi = "10.1007/978-3-642-10601-9_2",
language = "English",
isbn = "3642106005",
volume = "5886 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "7--24",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Moving phenomenon

T2 - Aggregation and analysis of geotime-tagged contents on the Web

AU - Kim, Kyoung Sook

AU - Zettsu, Koji

AU - Kidawara, Yutaka

AU - Kiyoki, Yasushi

PY - 2009

Y1 - 2009

N2 - The analysis of movement of people, vehicles, and other objects is important for carrying out research in social and scientific domains. The study of movement behavior of spatiotemporal entities helps enhance the quality of service in decision-making in real applications. However, the spread of certain entities such as diseases or rumor is difficult to observe compared to the movement of people, vehicles, or animals. We can only infer their locations in a certain region of space-time on the basis of observable events. In this paper, we propose a new model, called as moving phenomenon, to represent time-varying phenomena over geotime-tagged contents on the Web. The most important feature of this model is the integration of thematic dimension into an event-based spatiotemporal data model. By using the proposed model, a user can aggregate relevant contents relating to an interesting phenomenon and perceive its movement behavior; further, the model also enables a user to navigate the spatial, temporal, and thematic information of the contents along all the three-dimensions. Finally, we present an example of typhoons to illustrate moving phenomena and draw a comparison between the movement of the moving phenomenon created using information from news articles on the Web and that of the actual typhoon.

AB - The analysis of movement of people, vehicles, and other objects is important for carrying out research in social and scientific domains. The study of movement behavior of spatiotemporal entities helps enhance the quality of service in decision-making in real applications. However, the spread of certain entities such as diseases or rumor is difficult to observe compared to the movement of people, vehicles, or animals. We can only infer their locations in a certain region of space-time on the basis of observable events. In this paper, we propose a new model, called as moving phenomenon, to represent time-varying phenomena over geotime-tagged contents on the Web. The most important feature of this model is the integration of thematic dimension into an event-based spatiotemporal data model. By using the proposed model, a user can aggregate relevant contents relating to an interesting phenomenon and perceive its movement behavior; further, the model also enables a user to navigate the spatial, temporal, and thematic information of the contents along all the three-dimensions. Finally, we present an example of typhoons to illustrate moving phenomena and draw a comparison between the movement of the moving phenomenon created using information from news articles on the Web and that of the actual typhoon.

UR - http://www.scopus.com/inward/record.url?scp=75649088129&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=75649088129&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-10601-9_2

DO - 10.1007/978-3-642-10601-9_2

M3 - Conference contribution

AN - SCOPUS:75649088129

SN - 3642106005

SN - 9783642106002

VL - 5886 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 7

EP - 24

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -