## 1. Memo

### (1) The goal of this paper

The goal of this paper is to accelarate visualization and quantitive analysis of simulation data.

### (2) What simulation data is

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.

### (3) What quantitive analysis of simulation data is

An example of quantitive analysis is aggregation of distance between simulation value and reference value.

### (4) Challenge

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.

### (5) Approach

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.

## 2. References

[1] H. Lustosa, F. Porto, P. Blanco, Database System Support of Simulation Data, PVLDB2016.

https://www.vldb.org/pvldb/vol9/p1329-lustosa.pdf