The goal of this paper is to accelarate visualization and quantitive analysis of simulation data. <\/p>\n\n\n\n
A simulation is forecasting or estimating numerical value as velocity and pressure etc. at each 3D space corrdinate and each time in area such as astronomy; medicine; biology. A simulation solves a set of differential equations at an initial condition through discretization of the space. A set of simulation data are point clouds using several initial conditions of differential equation. Size of simulation data may become terabytes.<\/p>\n\n\n\n
An example of quantitive analysis is aggregation of distance between simulation value and reference value.<\/p>\n\n\n\n
Row store is inadequate for analytical query. Array DBMS such as SciDB can intuitive represent simulation data because a set of simulation data is multidimensional array. However a naive managing approach produce sparse array so cannnot accelarate analytical queries when using discretization of the space with irregular meshes.<\/p>\n\n\n\n
When simulation data is fit in memory: managing data using in-memory column store such as MonetDB. Otherwise mapping simulation data to evenly distributed storage blocks using equi-depth histogram and space filling curves.<\/p>\n\n\n\n