single-dr.php

JDR Vol.12 No.2 pp. 347-354
(2017)
doi: 10.20965/jdr.2017.p0347

Paper:

Views over last 60 days: 362

Difference Operators in Simulation Data Warehouses

Jing Zhao†, Yoshiharu Ishikawa, Yukiko Wakita, and Kento Sugiura

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

†Corresponding author

Received:
October 16, 2016
Accepted:
March 3, 2017
Online released:
March 16, 2017
Published:
March 20, 2017
Keywords:
data warehouse, difference operator, spatio-temporal databases, disaster information, simulation data
Abstract

In analyzing observation data and simulation results, there are frequent demands for comparing more than one data on the same subject to detect any differences between them. For example, comparison of observation data for an object in a certain spatial domain at different times or comparison of spatial simulation data with different parameters. Therefore, this paper proposes the difference operator in spatio-temporal data warehouses, which store temporal and spatial observation data and simulation data. The requirements for the difference operator are summarized, and the approaches to implement them are presented. In addition, the proposed approach is applied to the mass evacuation of simulation data in a tsunami disaster, and its effectiveness is verified. Extensions of the difference operator and their applications are also discussed.

Cite this article as:
J. Zhao, Y. Ishikawa, Y. Wakita, and K. Sugiura, “Difference Operators in Simulation Data Warehouses,” J. Disaster Res., Vol.12, No.2, pp. 347-354, 2017.
Data files:
References
  1. [1] S. Acharya, V. Poosala, and S. Ramaswamy, “Selectivity estimation in spatial databases,” In ACM SIGMOD, pp. 13–24, 1999.
  2. [2] N. Bruno, S. Chaudhuri, and L. Gravano “STHoles: A multidimensional workload-aware histogram,” In ACM SIGMOD, pp. 211–222, May, 2001.
  3. [3] H. Ehsan, M. A. Sharaf, and P. K. Chrysanthis, “MuVE: Efficient multi-objective view recommendation for visual data exploration,” In ICDE, pp. 731–742, 2016.
  4. [4] L. Gómez, 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.
  5. [5] Y. Ioannidis, “The history of histograms (abridged),” In VLDB, pp. 19–30, 2003.
  6. [6] Y. Ishikawa, “Research trend and future prospects for large-scale data analytics,” IEICE Trans. on Information and Systems (Japanese Edition), J97-D(4), pp. 718–728, 2014 (in Japanese).
  7. [7] 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.
  8. [8] H. Lustosa, F. Porto, P. Blanco, and P. Valduriez, “Database system support of simulation data,” PVLDB, Vol.9, No.13, pp. 1329–1340, Sept. 2016.
  9. [9] A. Parameswaran, N. Polyzotis, and H. Garcia-Molina, “SeeDB: Visualizing database queries efficiently,” Proceedings of the VLDB Endowment, Vol.7, No.4, pp. 325–328, 2013.
  10. [10] Paradigm4: Creators of SciDB a computational DBMS, http://www.paradigm4.com/ [accessed October 1, 2016]
  11. [11] M. Stonebraker, P. Brown, A. Poliakov, and S. Raman, “The architecture of SciDB,” In SSDBM, volume 6809 of LNCS, pp. 1–16, 2011.
  12. [12] 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.
  13. [13] A. Vaisman and E. Zimányi, “Data Warehouse Systems: Design and Implementation,” Springer, 2014.
  14. [14] J. Zhao, K. Sugiura, Y. Wang, and Y. Ishikawa, “Simulation data warehouse for integration and analysis of disaster information,” Journal of Disaster Research, Vol.11, No.2, pp. 255–264, 2016.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Feb. 19, 2020