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JDR Vol.11 No.2 pp. 255-264
(2016)
doi: 10.20965/jdr.2016.p0255

Paper:

Simulation Data Warehouse for Integration and Analysis of Disaster Information

Jing Zhao, Kento Sugiura, Yuanyuan Wang, and Yoshiharu Ishikawa

Graduate School of Information Science, Nagoya University
Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan

Received:
October 1, 2015
Accepted:
January 21, 2016
Online released:
March 18, 2016
Published:
March 1, 2016
Keywords:
data warehouse, disaster information, simulation data, spatio-temporal databases, interactive analysis
Abstract
Studies on disaster countermeasures utilize extensive simulations of earthquake, tsunami, people evacuation, and other targets, generating enormous amounts of data. The continuing development of computational capability has facilitated the increase of the simulation data size and the utilization of such “big data” has become a serious problem. With this background, the present study proposes, from the viewpoint of information science, the simulation data warehouse approach for the interactive analysis of large simulation data and describes a method of realizing a data warehouse. An objective of this study is to integrate different simulation data sets and enable exploratory analysis of multiple accumulated simulation data with high-speed response by data preprocessing. Further, the developed prototype system architecture and a case example of its use are explained.
Cite this article as:
J. Zhao, K. Sugiura, Y. Wang, and Y. Ishikawa, “Simulation Data Warehouse for Integration and Analysis of Disaster Information,” J. Disaster Res., Vol.11 No.2, pp. 255-264, 2016.
Data files:
References
  1. [1] T. Hey, S. Tansley, and K. Tolle (Eds.), “The Fourth Paradigm: Data-Intensive Scientific Discovery,” Microsoft Research, 2009.
  2. [2] S. Hayashi and S. Koshimura, “The 2011 Tohoku tsunami flow velocity estimation by the aerial video analysis and numerical modeling,” Journal of Disaster Research, Vol.8, No.4, pp. 561–572, 2013.
  3. [3] J. Han, M. Kamber, and J. Pei, “Data Mining: Concepts and Techniques,” Morgan Kaufmann, 3rd edition, 2011.
  4. [4] W. H. Inmon, “Building the Data Warehouse,” John Wiley & Sons, 3rd edition, 2002.
  5. [5] A. Vaisman and E. Zimányi, “Data Warehouse Systems: Design and Implementation,” Springer, 2014.
  6. [6] E. F. Codd, S. B. Codd, and C. T. Smalley, “Providing OLAP to user-analysis: An IT mandate,” E.F. Codd and Associates, 1993, http://www.minet.uni-jena.de/dbis/lehre/ss2005/sem_dwh/lit/ Cod93.pdf [accessed December 21, 2015]
  7. [7] J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatarao, F. Pellow, and H. Pirahesh, “Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals,” Data Mining and Knowledge Discovery, Vol.1, No.1, pp. 29–53, 1997.
  8. [8] MultiDimensional eXpressions, http://en.wikipedia.org/wiki/ MultiDimensional_eXpressions [accessed December 21, 2015]
  9. [9] Microsoft SQL Server, http://www.microsoft.com/en-us/sqlserver [accessed December 21, 2015]
  10. [10] GeoServer, http://geoserver.org/ [accessed December 21, 2015]
  11. [11] OpenLayers 3, http://openlayers.org/ [accessed December 21, 2015]
  12. [12] Multidimensional modeling (Adventure Works tutorial), https:// msdn.microsoft.com/en-us/library/ms170208(v=sql.120).aspx [accessed December 21, 2015]
  13. [13] A. Eisenberg and J. Melton, “SQL standardization: The next steps,” ACM SIGMOD Record, Vol.29, No.1, pp. 63–67, Mar. 2000.
  14. [14] A. Eisenberg, K. Kulkarni, J. Melton, J.-E. Michels, and F. Zemke, “SQL:2003 has been published,” ACM SIGMOD Record, Vol.33, No.1, pp. 119–126, Mar. 2004.
  15. [15] J. Melton, “Advanced SQL:1999 – Understanding Object-Relational and Otehr Advanced Features,” Morgan Kaufmann, 2003.
  16. [16] H. Samet, “Object-based and image-based object representations,” ACM Computing Surveys, Vol.36, No.2, pp. 159–217, June 2004.
  17. [17] Google BigQuery, https://cloud.google.com/bigquery/?hl=en [accessed December 21, 2015]
  18. [18] N. Kamat, P. Jayachandran, K. Tunga, and A. Nandi, “Distributed and interactive cube exploration,” in Proc. ICDE, pp. 472–483, 2014.
  19. [19] M. Stonebraker, P. Brown, D. Zhang, and J. Becla, “SciDB: A database management system for applications with complex analytics,” IEEE Computational Science & Engineering, Vol.15, No.3, pp. 54–62, 2013.
  20. [20] A. Aiken, J. Chen, M. Stonebraker, and A. Woodruff, “Tioga-2: A direct manipulation database visualization environment,” In Proc. ICDE, pp. 208–217, 1996.
  21. [21] L. G’omez, B. Kuijpers, and B. Moelans, “A survey of spatio-temporal data warehousing,” International Journal of Data Warehousing and Mining, Vol.5, No.3, pp. 28–55, 2009.
  22. [22] D. Papadias, P. Kalnis, J. Zhang, and Y. Tao, “Efficient OLAP operations in spatial data warehouses,” In Proc. SSTD, pp. 443–459, 2001.
  23. [23] D. Papadias, Y. Tao, P. Kalnis, and J. Zhang, “Indexing spatio-temporal data warehouses,” In Proc. ICDE, pp. 166–175, 2002.
  24. [24] L. G’omez, B. Kuijpers, and A. Vaisman, “A data model and query language for spatio-temporal decision support,” GeoInformatica, Vol.15, No.3, pp. 455–496, 2011.
  25. [25] S. I. G’omez, L. A. G’omez, and A. A. Vaisman, “A generic data model and query language for spatiotemporal OLAP cube analysis,” in Proc. EDBT, 2012.
  26. [26] L. Leonardi, G. Marketos, E. Frentzos, N. Giatrakos, S. Orlando, N. Pelekis, A. Raffaetà, A. Roncato, C. Silvestri, and Y. Theodoridis, “T-warehouse: Visual OLAP analysis on trajectory data,” In Proc. ICDE, pp. 1141–1144, 2010.
  27. [27] S. Sarawagi, R. Agrawal, and N. Megiddo, “Discovery-driven exploration of OLAP data cubes,” in Proc. EDBT, pp. 168–182, 1998.
  28. [28] S. Sawaragi, “User-adaptive exploration of multidimensional data,” in Proc. VLDB, pp. 307–316, 2000.
  29. [29] S. Sarawagi and G. Sathe, “I3: Intelligent, interactive investigation of OLAP data cubes,” in Proc. ACM SIGMOD, 2000.
  30. [30] M. Stonebraker, P. Brown, A. Poliakov, and S. Raman, “The architecture of SciDB,” in Proc. SSDBM, pp. 1–16, 2011.
  31. [31] A. Eldawy, M. F. Mokbel, S. Al-Harthi, A. Alzaidy, K. Tarek, and S. Ghani, “SHAHED: A MapReduce-based system for querying and visualizing spatio-temporal satellite data,” in Proc. ICDE, pp. 1585–1596, 2015.

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