Storage of Simulation Result Data: A Database Perspective

Storage of Simulation Result Data: A Database Perspective

François Pinet (Irstea – Clermont-Ferrand, France), Nadia Carluer (Irstea – Lyon, France), Claire Lauvernet (Irstea – Lyon, France), Bruno Cheviron (Irstea – Montpellier, France), Sandro Bimonte (Irstea – Clermont-Ferrand, France) and André Miralles (Irstea – Montpellier, France)
Copyright: © 2016 |Pages: 13
DOI: 10.4018/978-1-4666-8841-4.ch004
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

DBMS is a traditional technology for the storage of business application data. In this chapter, we show that this technology can be of interest in scientific fields. We present a survey of the emergence of the concept of simulation result database. Scientific simulation models become more complex, use more data and produce more outputs. Stochastic models can also be simulated. In this case, numerous simulations are run in order to discover a general trend in results. The replications of simulation increase again the amount of produced data, which makes exploration and analysis difficult. It is also often useful to compare the results obtained in using different model versions, scenarios or assumptions (for example different weather forecasts). In this chapter, we provide several examples of projects of simulation result databases. We show that the database technology can help to manage the large volumes of simulation outputs. We also illustrate this type of projects on environmental databases storing pesticide transfer simulation results. We conclude in highlighting some trends and future works.
Chapter Preview
Top

2. Using Databases For Simulation Data Storage

The authors of (Pfaltz & Orlandic, 1999) propose an object-oriented DBMS called ADAMS (Advanced Data Management System) especially dedicated for scientific simulations. According to the authors, the system is scalable to manage large volume of scientific data and provides a variety of data organization capabilities, including aggregation, linear ordering and multi-dimensional clustering. To overcome the issue related to the large volume of scientific data, the ADAMS system described in (Pfaltz & Orlandic, 1999) provides the possibility to distribute the objects on different sites. Gradual data migrations are possible when the quantity of stored data increases. ADAMS can also manage spatial data which are used in models.

Complete Chapter List

Search this Book:
Reset