single-dr.php

JDR Vol.13 No.2 pp. 338-346
doi: 10.20965/jdr.2018.p0338
(2018)

Paper:

An Analysis Technique of Evacuation Simulation Using an Array DBMS

Yusuke Kawai*, Jing Zhao**, Kento Sugiura**, Yoshiharu Ishikawa*,†, and Yukiko Wakita*

*Graduate School of Informatics, Nagoya University
Furo-cho, Chikusa-ku, 464-8601 Japan

Corresponding author

**Graduate School of Information Science, Nagoya University

Received:
November 2, 2017
Accepted:
March 2, 2018
Online released:
March 19, 2018
Published:
March 20, 2018
Keywords:
spatio-temporal simulation, array DBMS, evacuation simulations
Abstract

Today, large-scale simulations are thriving because of the increase of computating performance and storage capacity. Understanding the results of these simulations is not easy, and hence, support for interactive and exploratory analysis is becoming more important. This study focuses on spatio-temporal simulations and attempts to develop an analysis technology to support them. It uses a database system for supporting interactive analysis of large-scale data.

Since the data gained via spatio-temporal simulations is not suitable for management in a relational DBMS (RDBMS), this study uses an array DBMS, a type of DBMS that has been garnering increased attention in recent years. An array DBMS is designed for the management of large-scale array data; it provides a logical model for array data, yet it also supports efficient query processing. SciDB is used as our specific array DBMS in this paper.

This study targets disaster evacuation simulation data and demonstrates via experimentation that the query-processing functions offered by an array DBMS provide effective analysis support.

Cite this article as:
Y. Kawai, J. Zhao, K. Sugiura, Y. Ishikawa, and Y. Wakita, “An Analysis Technique of Evacuation Simulation Using an Array DBMS,” J. Disaster Res., Vol.13, No.2, pp. 338-346, 2018.
Data files:
References
  1. [1] “The architecture and motivation for Paradigm4’s SciDB,” Technical report, Paradigm4, 2016.
  2. [2] Paul G. Brown, “Overview of SciDB: Large scale array storage, processing and analysis,” In Proc. ACM SIGMOD, pp. 963–968, 2010, doi: 10.1145/1807167.1807271.
  3. [3] H. Lustosa, F. Porto, P. Valduriez, and P. Blanco, “Database system support of simulation data,” Proc. VLDB Endow. (PVLDB), Vol.9, No.13, pp. 1329–1340, 2016, doi: 10.14778/3007263.3007271.
  4. [4] T. Osaragi and T. Oki, “Wide-area evacuation simulation incorporating rescue and firefighting by local residents,” Journal of Disaster Research, Vol.12, No.2, pp. 296–310, 2017, doi: 10.20965/jdr.2017.p0296.
  5. [5] Paradigm4: Creators of SciDB a computational DBMS, http://www.paradigm4.com/ [accessed October 31, 2017]
  6. [6] M. Stonebraker, P. Brown, J. Becla, and D. Zhang, “SciDB: A database management system for applications with complex analytics,” IEEE Computational Science & Engineering, Vol.15, No.3, pp. 54–62, 2013, doi: 10.1109/MCSE.2013.19.
  7. [7] J. Zhao, Y. Ishikawa, Y. Wakita, and K. Sugiura, “Difference operators in simulation data warehouses,” Journal of Disaster Research, Vol.12, No.2, pp. 347–354, 2017, doi: 10.20965/jdr.2017.p0347.
  8. [8] 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, doi: 10.20965/jdr.2017.p0347.

*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 Oct. 16, 2018